transformer源码详解
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一、背景介绍
NLP在AI中的地位可谓是举足轻重，如果把NLP领域比作江湖的话，deep learning这个门派已经把传统的统计机器学习门派吊打的体无完肤，自从Deep Learning统领江湖以后，这个门派逐渐划分为CNN，RNN为核心的两大派系，鄙人认为在这两大派系之争中RNN通过快速的发展出来LSTM->GRU等牛逼人物绝技，更压过CNN一头。时间来到的2017年的6月份，正当RNN派系自以为可以一统江湖夺得武林至尊之际，一个足以撼动武林的无上心法(Transformer)悄然诞生，一场腥风血雨拉开了序幕。时间来到了2018年10月11日，transformer派系的bert教主通过光明顶11战11胜的压倒性优势，一举夺得武林至尊的称号，从此堪称九阳神功的无上心法在江湖上广为流传。时间来到了2020的6月份，虽然transformer派系的教主之位多经易主，但是transformer的无上心法依旧经久不衰。今天我们来窥视一下transformer的心法口诀。
原文连接和翻译：
https://www.yiyibooks.cn/yiyibooks/Attention_Is_All_You_Need/index.html
transformer目前来说可以分为以下几个版本。
tensorflow版本：https://github.com/Kyubyong/transformer
pytorch版本：
tensor2tensor版本：https://github.com/tensorflow/tensor2tensor
今天我们主要研究transformer最原始的版本tensorflow版本。
二、目录
(1)文件夹
|| ---iwslt2016 训练数据和预测数据
||---log 模型结果
||---eval 验证数据
||---test 测试数据
||---fig 图片：bleu loss lr
(2)数据处理
||---download.sh 下载训练数据的脚本
||---hparams.py 超参文件
||---prepro.py 数据预处理：根据训练数据生成词典等
||---data_load.py 加载数据处理数据
||---utils.py 工具类文件：保存参数、加载参数、模型评估
(3)模型相关
||---train.py 训练入口文件：
||---test.py 测试数据入口文件：
||--- model.py 模型网络结构
||--- modules.py 模型各个组件的实现
三、架构
四、代码
(1).train.py
# -*- coding: utf-8 -*-
#/usr/bin/python3
'''
Feb. 2019 by kyubyong park.
kbpark.linguist@gmail.com.
https://www.github.com/kyubyong/transformer
'''
import tensorflow as tf
from model import Transformer
from tqdm import tqdm
from data_load import get_batch
from utils import save_hparams, save_variable_specs, get_hypotheses, calc_bleu
import os
from hparams import Hparams
import math
import logging
logging.basicConfig(level=logging.INFO)
# 参数
logging.info("# hparams")
hparams = Hparams()
parser = hparams.parser
hp = parser.parse_args()
save_hparams(hp, hp.logdir)
logging.info("# Prepare train/eval batches")
#数据预处理
# 处理训练集数据，生成一个batch
train_batches, num_train_batches, num_train_samples = get_batch(hp.train1, hp.train2,
hp.maxlen1, hp.maxlen2,
hp.vocab, hp.batch_size,
shuffle=True)
# 处理验证集集数据，生成一个batch
eval_batches, num_eval_batches, num_eval_samples = get_batch(hp.eval1, hp.eval2,
100000, 100000,
hp.vocab, hp.batch_size,
shuffle=False)
# create a iterator of the correct shape and type
iter = tf.data.Iterator.from_structure(train_batches.output_types, train_batches.output_shapes)
xs, ys = iter.get_next()
train_init_op = iter.make_initializer(train_batches)
eval_init_op = iter.make_initializer(eval_batches)
logging.info("# Load model")
#实例化模型
m = Transformer(hp)
#传入x,y（label）开始训练模型
loss, train_op, global_step, train_summaries = m.train(xs, ys)
# 传入x,y（label）开始验证模型
y_hat, eval_summaries = m.eval(xs, ys)
# y_hat = m.infer(xs, ys)
logging.info("# Session")
saver = tf.train.Saver(max_to_keep=hp.num_epochs)
with tf.Session() as sess:
#从训练log中恢复模型
ckpt = tf.train.latest_checkpoint(hp.logdir)
# 如果没有模型初始化
if ckpt is None:
logging.info("Initializing from scratch")
sess.run(tf.global_variables_initializer())
save_variable_specs(os.path.join(hp.logdir, "specs"))
# 如果有模型恢复模型
else:
saver.restore(sess, ckpt)
# 指定模型保存文件路径和网络结构
summary_writer = tf.summary.FileWriter(hp.logdir, sess.graph)
sess.run(train_init_op)
# 计算总batch数量 num_train_batches：训练一轮batch的数量个数
total_steps = hp.num_epochs * num_train_batches
# 当前batch步数
_gs = sess.run(global_step)
#循环batch数量开始训练，tqdm是为了显示进度条
for i in tqdm(range(_gs, total_steps+1)):
_, _gs, _summary = sess.run([train_op, global_step, train_summaries])
print(train_op)
epoch = math.ceil(_gs / num_train_batches)
summary_writer.add_summary(_summary, _gs)
# 当训练一轮完成
if _gs and _gs % num_train_batches == 0:
logging.info("epoch {} is done".format(epoch))
_loss = sess.run(loss) # train loss
logging.info("# test evaluation")
_, _eval_summaries = sess.run([eval_init_op, eval_summaries])
summary_writer.add_summary(_eval_summaries, _gs)
logging.info("# get hypotheses")
hypotheses = get_hypotheses(num_eval_batches, num_eval_samples, sess, y_hat, m.idx2token)
logging.info("# write results")
model_output = "iwslt2016_E%02dL%.2f" % (epoch, _loss)
if not os.path.exists(hp.evaldir): os.makedirs(hp.evaldir)
translation = os.path.join(hp.evaldir, model_output)
with open(translation, 'w') as fout:
fout.write("\n".join(hypotheses))
# 验证模型
logging.info("# calc bleu score and append it to translation")
calc_bleu(hp.eval3, translation)
# 保存模型
logging.info("# save models")
ckpt_name = os.path.join(hp.logdir, model_output)
saver.save(sess, ckpt_name, global_step=_gs)
logging.info("after training of {} epochs, {} has been saved.".format(epoch, ckpt_name))
logging.info("# fall back to train mode")
sess.run(train_init_op)
summary_writer.close()
logging.info("Done")
(2).model.py
# -*- coding: utf-8 -*-
# /usr/bin/python3
'''
Feb. 2019 by kyubyong park.
kbpark.linguist@gmail.com.
https://www.github.com/kyubyong/transformer
Transformer network
'''
import tensorflow as tf
from data_load import load_vocab
from modules import get_token_embeddings, ff, positional_encoding, multihead_attention, label_smoothing, noam_scheme
from utils import convert_idx_to_token_tensor
from tqdm import tqdm
import logging
logging.basicConfig(level=logging.INFO)
class Transformer:
'''
xs: tuple of
x: int32 tensor. (N, T1)
x_seqlens: int32 tensor. (N,)
sents1: str tensor. (N,)
ys: tuple of
decoder_input: int32 tensor. (N, T2)
y: int32 tensor. (N, T2)
y_seqlen: int32 tensor. (N, )
sents2: str tensor. (N,)
training: boolean.
'''
def __init__(self, hp):
self.hp = hp
self.token2idx, self.idx2token = load_vocab(hp.vocab)
# 字向量(tooke向量)，将待翻译的每个字映射到目标词表中
self.embeddings = get_token_embeddings(self.hp.vocab_size, self.hp.d_model, zero_pad=True)
def encode(self, xs, training=True):
'''
Returns
memory: encoder outputs. (N, T1, d_model)
'''
with tf.variable_scope("encoder", reuse=tf.AUTO_REUSE):
x, seqlens, sents1 = xs
# src_masks：x的masks
# [[False False False False False True True True True True True True
# True True True True True True True True True True True]
# [False False False False False False False False False False True True
# True True True True True True True True True True True]
src_masks = tf.math.equal(x, 0) # (N, T1)
# embedding
enc = tf.nn.embedding_lookup(self.embeddings, x) # (N, T1, d_model)
enc *= self.hp.d_model**0.5 # scale
# 将位置信息加入
enc += positional_encoding(enc, self.hp.maxlen1)
enc = tf.layers.dropout(enc, self.hp.dropout_rate, training=training)
## Blocks
for i in range(self.hp.num_blocks):
with tf.variable_scope("num_blocks_{}".format(i), reuse=tf.AUTO_REUSE):
# self-attention
enc = multihead_attention(queries=enc,
keys=enc,
values=enc,
key_masks=src_masks,
num_heads=self.hp.num_heads,
dropout_rate=self.hp.dropout_rate,
training=training,
causality=False)
# feed forward
enc = ff(enc, num_units=[self.hp.d_ff, self.hp.d_model])
memory = enc
return memory, sents1, src_masks
def decode(self, ys, memory, src_masks, training=True):
'''
memory: encoder outputs. (N, T1, d_model)
src_masks: (N, T1)
Returns
logits: (N, T2, V). float32.
y_hat: (N, T2). int32 预测值： [[1,2133,2123,4444,...],[1,2342,43434,....]]
y: (N, T2). int32 label值：[[1,2133,2123,4444,...],[1,2342,43434,....]]
sents2: (N,). string.
'''
with tf.variable_scope("decoder", reuse=tf.AUTO_REUSE):
# decoder_inputs:(N,T2) 是开头加了<s> 去掉最后一个，目的是要添加到x中预测下一个字
# decoder_inputs：[[ 2 40 496 305 25112 31943 0 0 0],[2 142 2823 77 2960 532 368 1230]]
# y:就是label:(N,T2)
# y:[[ 40 496 305 25112 31943 3 0 0],[ 142 2823 77 2960 532 368 1230 319
# seqlens:[ 6 24 14 41]
# sents2:(N)
# sents2: [b'_I _am _my _connectome .', b"_And _though _that _company _has _]
decoder_inputs, y, seqlens, sents2 = ys
# tgt_masks:(N,T2)
# [[False False False False False False False False False False True True
# True True True True True True True True True True True True
# True True True True]
# [False False False False False False False False False True True True
# True True True True True True True True True True True True
# True True True True]
tgt_masks = tf.math.equal(decoder_inputs, 0) # (N, T2)
# embedding 将字索引id转换为向量
# self.embeddings：字向量：(V，E)
# dec：(N, T2, d_model)
dec = tf.nn.embedding_lookup(self.embeddings, decoder_inputs) # (N, T2, d_model)
dec *= self.hp.d_model ** 0.5 # scale
# 加上位置张量 positional_encoding
# dec：(N, T2, d_model)
dec += positional_encoding(dec, self.hp.maxlen2)
# dropout
dec = tf.layers.dropout(dec, self.hp.dropout_rate, training=training)
# Blocks
for i in range(self.hp.num_blocks):
with tf.variable_scope("num_blocks_{}".format(i), reuse=tf.AUTO_REUSE):
# Masked self-attention (Note that causality is True at this time)
dec = multihead_attention(queries=dec,
keys=dec,
values=dec,
key_masks=tgt_masks,
num_heads=self.hp.num_heads,
dropout_rate=self.hp.dropout_rate,
training=training,
causality=True,
scope="self_attention")
# Vanilla attention
dec = multihead_attention(queries=dec,
keys=memory,
values=memory,
key_masks=src_masks,
num_heads=self.hp.num_heads,
dropout_rate=self.hp.dropout_rate,
training=training,
causality=False,
scope="vanilla_attention")
### Feed Forward 前馈网络
dec = ff(dec, num_units=[self.hp.d_ff, self.hp.d_model])
# Final linear projection (embedding weights are shared)
#将参数矩阵转置
weights = tf.transpose(self.embeddings) # (d_model, vocab_size)
#tf.einsum：爱因斯坦求和 transformer之后的字矩阵与权重矩阵相乘得到词典中每个字的概率
#logits:(N, T2,[0,0.999,0,.....,0]
logits = tf.einsum('ntd,dk->ntk', dec, weights) # (N, T2, vocab_size)
#tf.argmax：返回某维度最大的索引
# y_hat:(N, T2） 值:[[ 5768 7128 7492 3546 7128 3947 21373 7128 7128 7128 7492 4501
# 7128 7128 14651],]
y_hat = tf.to_int32(tf.argmax(logits, axis=-1))
#参考注释
return logits, y_hat, y, sents2
def train(self, xs, ys):
'''
Returns
loss: scalar.
train_op: training operation
global_step: scalar.
summaries: training summary node
'''
# forward
memory, sents1, src_masks = self.encode(xs)
logits, preds, y, sents2 = self.decode(ys, memory, src_masks)
# train scheme
# y:(N, T2）值:[[ 5768 7128 7492 7128 7492 4501 7128 7128 14651],[ 5768 7128 7492 7128 7492 4501 7128 7128 14651]]
# y_:(N, T2, vocab_size); 值：(N, T2,[0,0.999,0,.....,0]
y_ = label_smoothing(tf.one_hot(y, depth=self.hp.vocab_size))
# 预测值和label做交叉熵，生成损失值
# logits:预测id的概率 (N, T2,[0,0.999,0,.....,0])
# ce: (N,T2) 例如：(4, 42) 值：array([[ 6.8254533, 6.601975 , 6.5515084...,9.603574 , 10.001306 ],[6.8502007, 6.645137...]】,每个字粒度的损失
ce = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=y_)
# nonpadding:(N,T2) 例如：(4, 42) 值：[[[1., 1., 1.,0,0,0],[1., 1., 1., 1., 1., 1.,.....]
nonpadding = tf.to_float(tf.not_equal(y, self.token2idx["<pad>"])) # 0: <pad>
# tf.reduce_sum 按照某一维度求和 不指定axis，默认所有维度
# ce * nonpadding:只求没有填充的词的损失，padding的去掉了 tf.reduce_sum(nonpadding)：个数相加为了求平均
loss = tf.reduce_sum(ce * nonpadding) / (tf.reduce_sum(nonpadding) + 1e-7)
global_step = tf.train.get_or_create_global_step()
# 根据训练步数，动态改变学习率
lr = noam_scheme(self.hp.lr, global_step, self.hp.warmup_steps)
#定义优化器
optimizer = tf.train.AdamOptimizer(lr)
train_op = optimizer.minimize(loss, global_step=global_step)
tf.summary.scalar('lr', lr)
tf.summary.scalar("loss", loss)
tf.summary.scalar("global_step", global_step)
summaries = tf.summary.merge_all()
return loss, train_op, global_step, summaries
def eval(self, xs, ys):
'''Predicts autoregressively
At inference, input ys is ignored.
Returns
y_hat: (N, T2)
'''
decoder_inputs, y, y_seqlen, sents2 = ys
decoder_inputs = tf.ones((tf.shape(xs[0])[0], 1), tf.int32) * self.token2idx["<s>"]
ys = (decoder_inputs, y, y_seqlen, sents2)
memory, sents1, src_masks = self.encode(xs, False)
logging.info("Inference graph is being built. Please be patient.")
for _ in tqdm(range(self.hp.maxlen2)):
logits, y_hat, y, sents2 = self.decode(ys, memory, src_masks, False)
if tf.reduce_sum(y_hat, 1) == self.token2idx["<pad>"]: break
_decoder_inputs = tf.concat((decoder_inputs, y_hat), 1)
ys = (_decoder_inputs, y, y_seqlen, sents2)
# monitor a random sample
n = tf.random_uniform((), 0, tf.shape(y_hat)[0]-1, tf.int32)
sent1 = sents1[n]
pred = convert_idx_to_token_tensor(y_hat[n], self.idx2token)
sent2 = sents2[n]
tf.summary.text("sent1", sent1)
tf.summary.text("pred", pred)
tf.summary.text("sent2", sent2)
summaries = tf.summary.merge_all()
return y_hat, summaries
(3).modules.py 模块
# -*- coding: utf-8 -*-
#/usr/bin/python3
'''
Feb. 2019 by kyubyong park.
kbpark.linguist@gmail.com.
https://www.github.com/kyubyong/transformer.
Building blocks for Transformer
'''
import numpy as np
import tensorflow as tf
# laye 数据标准化，
def ln(inputs, epsilon = 1e-8, scope="ln"):
'''Applies layer normalization. See https://arxiv.org/abs/1607.06450.
inputs: A tensor with 2 or more dimensions, where the first dimension has `batch_size`.
epsilon: A floating number. A very small number for preventing ZeroDivision Error.
scope: Optional scope for `variable_scope`.
Returns:
A tensor with the same shape and data dtype as `inputs`.
'''
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
# inputs (2, 28, 512)
# [[[-2.954033 1.695996 -3.1413105 ... -4.018251 -1.3023977
# -0.30223972]
# [-2.3530521 1.7192748 -3.351814 ... -2.8273482 -0.65232337
# -0.4264419 ]
# [-2.2563033 1.5593072 -3.2775855 ... -3.2531385 -0.9977432
# -0.35023227]
# ...
# [-0.6843967 -1.003483 1.6845378 ... -0.31852347 2.5649729
# 0.804183 ]
# [-0.9206941 -1.1214577 2.0042856 ... 0.09208354 3.1693528
# 1.0978277 ]
# [-0.74338186 -0.9217373 1.9481943 ... 0.17723289 3.5957296
# 1.2593844 ]]
#
# [[-2.517493 0.91683435 -3.4142447 ... -3.9844556 -1.1842881
# -0.72232884]
# [-1.8562554 1.1425755 -3.2166462 ... -4.044012 -1.2189282
# -0.38615695]
# [-2.0858154 1.4967486 -2.6159432 ... -3.5880928 -0.77991307
# -0.33104044]
inputs_shape = inputs.get_shape() # (2, 28, 512)
params_shape = inputs_shape[-1:]
# mean:每个字的均值:(N,L,1):batch=2 (2, 22, 1)
# [[[-0.06484328]
# [-0.01978662]
# [-0.05463864]
# [-0.03227896]
# [-0.05496355]
# [ 0.00383393]
# [-0.00831935]
# [-0.04848529]
# [-0.05807229]
# [-0.02759192]
# [-0.04658262]
# [-0.044351 ]
# [-0.0404046 ]
# [-0.01897928]
# [-0.03630898]
# [-0.04527371]
# [ 0.00215435]
# [-0.02535543]
# [-0.02192712]
# [-0.03002348]
# [-0.01454192]
# [-0.02426025]]
#
# [[-0.01858747]
# [-0.09324092]
# variance：每个字的方差：(N,L,1):batch=2 (2, 22, 1)
# [[[10.156523 ]
# [ 6.320237 ]
# [ 7.1246476]
# [ 7.285763 ]
# [ 5.4343634]
# [ 7.8283257]
# [ 6.818144 ]
# [ 7.768257 ]
# [ 7.3524065]
# [ 6.89916 ]
# [ 6.85674 ]
# [ 7.8054104]
# [ 7.141796 ]
# [ 6.5306134]
# [ 5.97371 ]
# [ 6.9241476]
# [ 6.4381695]
# [ 7.5288787]
# [ 7.336317 ]
# [ 6.9332967]
# [ 5.7453003]
# [ 6.994274 ]]
#
# [[ 5.858997 ]
# [ 6.8444324]
# [ 4.2397 ]
mean, variance = tf.nn.moments(inputs, [-1], keep_dims=True)
# beta shape: (512,) 都是0附近的
# [ 6.42933836e-03 -7.10692781e-04 3.21562425e-03 3.74931638e-04
# -4.57212422e-03 -2.78607651e-04 -1.94830136e-05 -2.29233573e-03
# -3.16770235e-03 -6.85446896e-04 1.31265656e-03 3.29568808e-04
# -3.31255049e-03 2.30989186e-03 -4.35887882e-03 -1.96260633e-04
# -9.28307883e-03 5.05952770e-03 -4.32743737e-03 2.50508357e-02
beta= tf.get_variable("beta", params_shape, initializer=tf.zeros_initializer())
# gamma shape: (512,) 都是1附近的
# [0.9971061 1.0393478 0.99570525 1.0020051 1.0036978 0.9996209
# 1.0018373 1.0109453 1.012793 0.9991118 1.0150126 1.0089304
# 1.0066382 1.0279335 1.0096924 1.0351689 0.9996498 1.0024785
# 0.988923 1.0238222 1.0073771 0.99466056 1.0291134 1.0062896
# 0.99483615 1.0175365 1.0021777 0.9909473 1.0064633 1.0024587
gamma = tf.get_variable("gamma", params_shape, initializer=tf.ones_initializer())
# normalized :(2, 28, 512) 和上面的inputs是同一个数据
# [[[-1.5576326 0.8367065 -1.6540633 ... -2.1056075 -0.7071917
# -0.19220194]
# [-1.1740364 0.8412268 -1.6682914 ... -1.4087503 -0.33240056
# -0.22061913]
# [-1.2856865 0.8827096 -1.8660771 ... -1.8521839 -0.5704518
# -0.20247398]
# ...
# [-0.36244592 -0.5411275 0.9641068 ... -0.15756473 1.4571316
# 0.47112694]
# [-0.4678502 -0.57143646 1.0413258 ... 0.05470374 1.6424552
# 0.57362866]
# [-0.3773605 -0.47287214 1.064013 ... 0.11564048 1.9462892
# 0.69514644]]
normalized = (inputs - mean) / ( (variance + epsilon) ** (.5) )
# 最终outpus:
outputs = gamma * normalized + beta
return outputs
#构造字向量矩阵
def get_token_embeddings(vocab_size, num_units, zero_pad=True):
'''Constructs token embedding matrix.
Note that the column of index 0's are set to zeros.
vocab_size: scalar. V.
num_units: embedding dimensionalty. E.
zero_pad: Boolean. If True, all the values of the first row (id = 0) should be constant zero
To apply query/key masks easily, zero pad is turned on.
Returns
weight variable: (V, E)
'''
with tf.variable_scope("shared_weight_matrix"):
embeddings = tf.get_variable('weight_mat',
dtype=tf.float32,
shape=(vocab_size, num_units),
initializer=tf.contrib.layers.xavier_initializer())
if zero_pad:
embeddings = tf.concat((tf.zeros(shape=[1, num_units]),
embeddings[1:, :]), 0)
return embeddings
def scaled_dot_product_attention(Q, K, V, key_masks,
causality=False, dropout_rate=0.,
training=True,
scope="scaled_dot_product_attention"):
'''See 3.2.1.
Q: Packed queries. 3d tensor. [N, T_q, d_k]. 这个地方错了(h*N, T_q, d_model/h)
K: Packed keys. 3d tensor. [N, T_k, d_k]. 这个地方错了(h*N, T_q, d_model/h)
V: Packed values. 3d tensor. [N, T_k, d_v]. 这个地方错了(h*N, T_q, d_model/h)
key_masks: A 2d tensor with shape of [N, key_seqlen]
causality: If True, applies masking for future blinding
dropout_rate: A floating point number of [0, 1].
training: boolean for controlling droput
scope: Optional scope for `variable_scope`.
'''
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
d_k = Q.get_shape().as_list()[-1]
# dot product:点积：Q和K的点积
outputs = tf.matmul(Q, tf.transpose(K, [0, 2, 1])) # (N, T_q, T_k)
# scale
outputs /= d_k ** 0.5
# key masking
# # key_masks:(N,T2)
# # [[False False False False False False False False False False True True
# # True True True True True True True True True True True True
# # True True True True]
# # [False False False False False False False False False True True True
# # True True True True True True True True True True True True
# # True True True True]
outputs = mask(outputs, key_masks=key_masks, type="key")
# causality or future blinding masking
if causality:
outputs = mask(outputs, type="future")
# softmax
outputs = tf.nn.softmax(outputs)
attention = tf.transpose(outputs, [0, 2, 1])
tf.summary.image("attention", tf.expand_dims(attention[:1], -1))
# # query masking
# outputs = mask(outputs, Q, K, type="query")
# dropout
outputs = tf.layers.dropout(outputs, rate=dropout_rate, training=training)
# weighted sum (context vectors)
# tf.matmul：矩阵相乘
outputs = tf.matmul(outputs, V) # (N, T_q, d_v)
return outputs
def mask(inputs, key_masks=None, type=None):
"""Masks paddings on keys or queries to inputs
inputs: 3d tensor. (h*N, T_q, T_k)
key_masks: 3d tensor. (N, 1, T_k)
type: string. "key" | "future"
e.g.,
>> inputs = tf.zeros([2, 2, 3], dtype=tf.float32)
>> key_masks = tf.constant([[0., 0., 1.],
[0., 1., 1.]])
>> mask(inputs, key_masks=key_masks, type="key")
array([[[ 0.0000000e+00, 0.0000000e+00, -4.2949673e+09],
[ 0.0000000e+00, 0.0000000e+00, -4.2949673e+09]],
[[ 0.0000000e+00, -4.2949673e+09, -4.2949673e+09],
[ 0.0000000e+00, -4.2949673e+09, -4.2949673e+09]],
[[ 0.0000000e+00, 0.0000000e+00, -4.2949673e+09],
[ 0.0000000e+00, 0.0000000e+00, -4.2949673e+09]],
[[ 0.0000000e+00, -4.2949673e+09, -4.2949673e+09],
[ 0.0000000e+00, -4.2949673e+09, -4.2949673e+09]]], dtype=float32)
"""
padding_num = -2 ** 32 + 1
if type in ("k", "key", "keys"):
key_masks = tf.to_float(key_masks)
# tf.tile:平铺；The output tensor's i'th dimension has input.dims(i) * multiples[i] elements,
# 因为Input进行了多头分割，所有标签也要扩展
# batch=2的时候，key_masks:(16, 34)
#
key_masks = tf.tile(key_masks, [tf.shape(inputs)[0] // tf.shape(key_masks)[0], 1]) # (h*N, seqlen)
# tf.expand_dims：Returns a tensor with an additional dimension inserted at index axis.
# batch=2的时候，key_masks: (16, 1, 34),
#[[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
# [[0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]
key_masks = tf.expand_dims(key_masks, 1) # (h*N, 1, seqlen)
outputs = inputs + key_masks * padding_num
# elif type in ("q", "query", "queries"):
# # Generate masks
# masks = tf.sign(tf.reduce_sum(tf.abs(queries), axis=-1)) # (N, T_q)
# masks = tf.expand_dims(masks, -1) # (N, T_q, 1)
# masks = tf.tile(masks, [1, 1, tf.shape(keys)[1]]) # (N, T_q, T_k)
#
# # Apply masks to inputs
# outputs = inputs*masks
elif type in ("f", "future", "right"):
# tf.ones_like：Creates a tensor of all ones that has the same shape as the input.
# diag_vals shape: (20, 20)
# [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]
diag_vals = tf.ones_like(inputs[0, :, :]) # (T_q, T_k)
# tril：生成一个下三角 shape: (20, 20)
# [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
# [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]
tril = tf.linalg.LinearOperatorLowerTriangular(diag_vals).to_dense() # (T_q, T_k)
#batch取得2，head=8则： future_masks: (16, 20, 20),其中20是每句话词的个数，每一句话生成一个下三角
# [[[1. 0. 0. ... 0. 0. 0.]
# [1. 1. 0. ... 0. 0. 0.]
# [1. 1. 1. ... 0. 0. 0.]
# ...
# [1. 1. 1. ... 1. 0. 0.]
# [1. 1. 1. ... 1. 1. 0.]
# [1. 1. 1. ... 1. 1. 1.]]
#
# [[1. 0. 0. ... 0. 0. 0.]
# [1. 1. 0. ... 0. 0. 0.]
# [1. 1. 1. ... 0. 0. 0.]
# ...
# [1. 1. 1. ... 1. 0. 0.]
# [1. 1. 1. ... 1. 1. 0.]
# [1. 1. 1. ... 1. 1. 1.]]
future_masks = tf.tile(tf.expand_dims(tril, 0), [tf.shape(inputs)[0], 1, 1]) # (N, T_q, T_k)
# paddings:按照 paddings 的shap生成一个填充矩阵
# [[[-4.2949673e+09 -4.2949673e+09 -4.2949673e+09 ... -4.2949673e+09
# -4.2949673e+09 -4.2949673e+09]
# [-4.2949673e+09 -4.2949673e+09 -4.2949673e+09 ... -4.2949673e+09
# -4.2949673e+09 -4.2949673e+09]
# [-4.2949673e+09 -4.2949673e+09 -4.2949673e+09 ... -4.2949673e+09
# -4.2949673e+09 -4.2949673e+09]
# ...
# [-4.2949673e+09 -4.2949673e+09 -4.2949673e+09 ... -4.2949673e+09
# -4.2949673e+09 -4.2949673e+09]
# [-4.2949673e+09 -4.2949673e+09 -4.2949673e+09 ... -4.2949673e+09
# -4.2949673e+09 -4.2949673e+09]
# [-4.2949673e+09 -4.2949673e+09 -4.2949673e+09 ... -4.2949673e+09
# -4.2949673e+09 -4.2949673e+09]]
paddings = tf.ones_like(future_masks) * padding_num
# tf.where(condition,paddings, inputs) 按照：condition=true的部位，将padding 填充到input中
# [[[ 4.43939781e+01 -4.29496730e+09 -4.29496730e+09 ... -4.29496730e+09
# -4.29496730e+09 -4.29496730e+09]
# [ 3.41685486e+01 -1.06252086e+00 -4.29496730e+09 ... -4.29496730e+09
# -4.29496730e+09 -4.29496730e+09]
# [ 3.96921425e+01 2.78434825e+00 4.21420937e+01 ... -4.29496730e+09
# -4.29496730e+09 -4.29496730e+09]
# ...
# [ 5.53764381e+01 2.47523355e+00 5.91777534e+01 ... -1.21471321e+02
# -4.29496730e+09 -4.29496730e+09]
# [ 5.32606239e+01 4.99608231e+00 5.68281059e+01 ... -1.08563034e+02
# -4.29496730e+09 -4.29496730e+09]
# [ 4.16031532e+01 1.54854858e+00 4.41170464e+01 ... -8.69959717e+01
# -4.29496730e+09 -4.29496730e+09]]
#
# [[-1.77314243e+01 -4.29496730e+09 -4.29496730e+09 ... -4.29496730e+09
# -4.29496730e+09 -4.29496730e+09]
# [-1.86583366e+01 -1.04054117e+01 -4.29496730e+09 ... -4.29496730e+09
# -4.29496730e+09 -4.29496730e+09]
# [-2.13199158e+01 -1.20762053e+01 -6.59975433e+01 ... -4.29496730e+09
# -4.29496730e+09 -4.29496730e+09]
# ...
# [-2.05263672e+01 -1.20537119e+01 -6.91874313e+01 ... -8.82377319e+01
# -4.29496730e+09 -4.29496730e+09]
# [-1.61425571e+01 -8.80572414e+00 -5.09573174e+01 ... -6.37179871e+01
# 1.55839844e+01 -4.29496730e+09]
# [-2.01059513e+01 -1.21201067e+01 -6.66899109e+01 ... -8.49668579e+01
# 2.01012688e+01 -1.04354706e+02]]
outputs = tf.where(tf.equal(future_masks, 0), paddings, inputs)
else:
print("Check if you entered type correctly!")
return outputs
# 多头注意力机制
def multihead_attention(queries, keys, values, key_masks,
num_heads=8,
dropout_rate=0,
training=True,
causality=False,
scope="multihead_attention"):
'''Applies multihead attention. See 3.2.2
queries: A 3d tensor with shape of [N, T_q, d_model].
keys: A 3d tensor with shape of [N, T_k, d_model].
values: A 3d tensor with shape of [N, T_k, d_model].
key_masks: A 2d tensor with shape of [N, key_seqlen]
num_heads: An int. Number of heads.
dropout_rate: A floating point number.
training: Boolean. Controller of mechanism for dropout.
causality: Boolean. If true, units that reference the future are masked.
scope: Optional scope for `variable_scope`.
Returns
A 3d tensor with shape of (N, T_q, C)
'''
d_model = queries.get_shape().as_list()[-1]
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
# Linear projections
# 先加一层
Q = tf.layers.dense(queries, d_model, use_bias=True) # (N, T_q, d_model)
K = tf.layers.dense(keys, d_model, use_bias=True) # (N, T_k, d_model)
V = tf.layers.dense(values, d_model, use_bias=True) # (N, T_k, d_model)
# Split and concat
# 切割与合并,这里体现的多头注意力机制
Q_ = tf.concat(tf.split(Q, num_heads, axis=2), axis=0) # (h*N, T_q, d_model/h)
K_ = tf.concat(tf.split(K, num_heads, axis=2), axis=0) # (h*N, T_k, d_model/h)
V_ = tf.concat(tf.split(V, num_heads, axis=2), axis=0) # (h*N, T_k, d_model/h)
# 这里是重点：Attention计算
# 传入的 src_masks或者 tgt_masks的值
outputs = scaled_dot_product_attention(Q_, K_, V_, key_masks, causality, dropout_rate, training)
# Restore shape
# 这里将多头注意力结果恢复N
outputs = tf.concat(tf.split(outputs, num_heads, axis=0), axis=2 ) # (N, T_q, d_model)
# Residual connection
outputs += queries
# Normalize
outputs = ln(outputs)
return outputs
def ff(inputs, num_units, scope="positionwise_feedforward"):
'''position-wise feed forward net. See 3.3
inputs: A 3d tensor with shape of [N, T, C].
num_units: A list of two integers.
scope: Optional scope for `variable_scope`.
Returns:
A 3d tensor with the same shape and dtype as inputs
'''
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
# Inner layer
# inputs
# outputs： (2, 21, 2048)
# [[[0. 0. 0. ... 6.132974 0. 0. ]
# [0. 0. 0. ... 7.307194 0. 0. ]
# [0. 0. 0. ... 7.982828 0. 0. ]
# ...
# [0. 0. 0. ... 7.964889 0. 0. ]
# [0. 0. 0. ... 5.4325767 0. 0. ]
# [0. 0. 0. ... 7.275622 0. 0. ]]
#
# [[0. 0. 0. ... 7.5862374 0. 0. ]
# [0. 0. 0. ... 5.2648916 0. 0. ]
# [0. 0.23242699 0. ... 6.187096 0. 0. ]
# ...
# [0. 0. 0. ... 8.233661 0. 0. ]
# [0. 1.2058965 0. ... 7.485455 0. 0. ]
# [0. 0. 0. ... 8.30237 0. 0. ]]]
outputs = tf.layers.dense(inputs, num_units[0], activation=tf.nn.relu)
# Outer layer
# outputs (2, 21, 512)
# [[[-25.37711 1.3692019 -50.80832 ... 0.9118566 -24.534058
# -29.246645 ]
# [-28.563698 4.2556305 -48.531235 ... -0.37409064 -25.03862
# -28.988926 ]
# [-35.380463 9.315982 -49.559715 ... -2.7038898 -30.601368
# -33.075985 ]
# ...
# [-35.643818 9.848609 -48.638973 ... -3.1406584 -30.439838
# -32.913677 ]
# [-21.56209 -0.767092 -48.07494 ... 3.6515896 -22.752474
# -24.482904 ]
# [-33.58284 8.678018 -46.875854 ... -2.9922912 -28.830814
# -31.506397 ]]
#
# [[-33.383442 9.613742 -46.971367 ... -3.2966337 -28.788752
# -32.695858 ]
# [-19.326656 -3.1725218 -50.551548 ... 3.6544466 -20.554945
# -25.621178 ]
# [-30.288332 17.503975 -24.142347 ... -9.287842 -23.152536
# -29.06433 ]
# ...
# [-35.35156 9.312039 -49.94821 ... -2.5631473 -30.52241
# -33.088287 ]
# [-37.59673 21.043293 -29.895994 ... -10.287443 -30.355291
# -35.290684 ]
# [-35.986378 9.922715 -50.2701 ... -3.6265292 -30.689642
# -34.769424 ]]]
outputs = tf.layers.dense(outputs, num_units[1])
# Residual connection
outputs += inputs
# Normalize
outputs = ln(outputs)
return outputs
def label_smoothing(inputs, epsilon=0.1):
'''Applies label smoothing. See 5.4 and https://arxiv.org/abs/1512.00567.
inputs: 3d tensor. [N, T, V], where V is the number of vocabulary.
epsilon: Smoothing rate.
For example,
import tensorflow as tf
inputs = tf.convert_to_tensor([[[0, 0, 1],
[0, 1, 0],
[1, 0, 0]],
[[1, 0, 0],
[1, 0, 0],
[0, 1, 0]]], tf.float32)
outputs = label_smoothing(inputs)
with tf.Session() as sess:
print(sess.run([outputs]))
>>
[array([[[ 0.03333334, 0.03333334, 0.93333334],
[ 0.03333334, 0.93333334, 0.03333334],
[ 0.93333334, 0.03333334, 0.03333334]],
[[ 0.93333334, 0.03333334, 0.03333334],
[ 0.93333334, 0.03333334, 0.03333334],
[ 0.03333334, 0.93333334, 0.03333334]]], dtype=float32)] '''
V = inputs.get_shape().as_list()[-1] # number of channels
return ((1-epsilon) * inputs) + (epsilon / V)
#位置向量
def positional_encoding(inputs,
maxlen,
masking=True,
scope="positional_encoding"):
'''Sinusoidal Positional_Encoding. See 3.5
inputs: 3d tensor. (N, T, E)
maxlen: scalar. Must be >= T
masking: Boolean. If True, padding positions are set to zeros.
scope: Optional scope for `variable_scope`.
returns
3d tensor that has the same shape as inputs.
'''
E = inputs.get_shape().as_list()[-1] # static
N, T = tf.shape(inputs)[0], tf.shape(inputs)[1] # dynamic
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
# position indices
position_ind = tf.tile(tf.expand_dims(tf.range(T), 0), [N, 1]) # (N, T)
# First part of the PE function: sin and cos argument
position_enc = np.array([
[pos / np.power(10000, (i-i%2)/E) for i in range(E)]
for pos in range(maxlen)])
# Second part, apply the cosine to even columns and sin to odds.
position_enc[:, 0::2] = np.sin(position_enc[:, 0::2]) # dim 2i
position_enc[:, 1::2] = np.cos(position_enc[:, 1::2]) # dim 2i+1
position_enc = tf.convert_to_tensor(position_enc, tf.float32) # (maxlen, E)
# lookup
outputs = tf.nn.embedding_lookup(position_enc, position_ind)
# masks
if masking:
outputs = tf.where(tf.equal(inputs, 0), inputs, outputs)
return tf.to_float(outputs)
# 改变学习率算法：学习率衰减
def noam_scheme(init_lr, global_step, warmup_steps=4000.):
'''Noam scheme learning rate decay
init_lr: initial learning rate. scalar.
global_step: scalar.
warmup_steps: scalar. During warmup_steps, learning rate increases
until it reaches init_lr.
'''
step = tf.cast(global_step + 1, dtype=tf.float32)
return init_lr * warmup_steps ** 0.5 * tf.minimum(step * warmup_steps ** -1.5, step ** -0.5)
最主要的就是上面的三个文件了。
Q,K,V的理解
1.https://blog.csdn.net/qq_42004289/article/details/85990009
2.https://www.bilibili.com/video/BV1J441137V6?from=search&seid=12093118819715772273
3.https://charon.me/posts/transformer/
源码：
https://github.com/Kyubyong/transforme
参考文献：
1.https://zhuanlan.zhihu.com/p/110800071
postion位置理解
2.https://www.zhihu.com/question/347678607/answer/864217252
3.https://zhuanlan.zhihu.com/p/110800071
4.https://zhuanlan.zhihu.com/p/63191028
5.https://charon.me/posts/transformer/
6.https://zhuanlan.zhihu.com/p/47510705
7.https://blog.csdn.net/qq_42004289/article/details/85990009
8.https://spaces.ac.cn/archives/4765
9.https://spaces.ac.cn/archives/6933
10.https://www.zhihu.com/question/347678607/answer/864217252
11.https://zhuanlan.zhihu.com/p/95079337
12.https://blog.csdn.net/u012526436/article/details/86295971
13.https://zhuanlan.zhihu.com/p/60821628