理解transformer源码
本文参考哈佛开源代码，对代码进行了一些修改，结合论文学习笔记加深对transformer算法整体的理解。
import copy
import math
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
##########################################################
# 生成模型
##########################################################
class EncoderDecoder(nn.Module):
"""标准的Encoder-Decoder架构"""
def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):
super(EncoderDecoder, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.src_embed = src_embed # 源序列embedding
self.tgt_embed = tgt_embed # 目标序列embedding
self.generator = generator # 生成目标单词的概率
def forward(self, src, tgt, src_mask, tgt_mask):
"""接收和处理原序列,目标序列,以及他们的mask"""
return self.decode(self.encode(src, src_mask), src_mask,
tgt, tgt_mask)
def encode(self, src, src_mask):
return self.encoder(self.src_embed(src), src_mask)
def decode(self, memory, src_mask, tgt, tgt_mask):
return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)
class Generator(nn.Module):
"""定义标准的linear+softmax生成步骤"""
def __init__(self, d_model, vocab):
super(Generator, self).__init__()
self.proj = nn.Linear(d_model, vocab)
def forward(self, x):
return F.log_softmax(self.proj(x), dim=-1)
# Encoder部分
def clones(module, N):
"""产生N个相同的层"""
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
class Encoder(nn.Module):
"""N层堆叠的Encoder"""
def __init__(self, layer, N):
super(Encoder, self).__init__()
self.layers = clones(layer, N)
self.norm = LayerNorm(layer.size)
def forward(self, x, mask):
"""每层layer依次通过输入序列与mask"""
for layer in self.layers:
x = layer(x, mask)
return self.norm(x)
class LayerNorm(nn.Module):
"""构造一个layernorm模块"""
def __init__(self, features, eps=1e-6):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(features))
self.eps = eps
def forward(self, x):
"""Norm"""
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
class SublayerConnection(nn.Module):
"""Add+Norm"""
def __init__(self, size, dropout):
super(SublayerConnection, self).__init__()
self.norm = LayerNorm(size)
self.dropout = nn.Dropout(dropout)
def forward(self, x, sublayer):
"""add norm"""
norm = self.dropout(sublayer(self.norm(x)))
return x + norm
class EncoderLayer(nn.Module):
"""Encoder分为两层Self-Attn和Feed Forward"""
def __init__(self, size, self_attn, feed_forward, dropout):
super(EncoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.sublayer = clones(SublayerConnection(size, dropout), 2)
self.size = size
def forward(self, x, mask):
"""Self-Attn和Feed Forward"""
x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
return self.sublayer[1](x, self.feed_forward)
# Decoder部分
class Decoder(nn.Module):
"""带mask功能的通用Decoder结构"""
def __init__(self, layer, N):
super(Decoder, self).__init__()
self.layers = clones(layer, N)
self.norm = LayerNorm(layer.size)
def forward(self, x, memory, src_mask, tgt_mask):
for layer in self.layers:
x = layer(x, memory, src_mask, tgt_mask)
return self.norm(x)
class DecoderLayer(nn.Module):
"""Decoder is made of self-attn, src-attn, and feed forward"""
def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
super(DecoderLayer, self).__init__()
self.size = size
self.self_attn = self_attn
self.src_attn = src_attn
self.feed_forward = feed_forward
self.sublayer = clones(SublayerConnection(size, dropout), 3)
def forward(self, x, memory, src_mask, tgt_mask):
"""将decoder的三个Sublayer串联起来"""
m = memory
x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))
x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))
return self.sublayer[2](x, self.feed_forward)
def subsequent_mask(size):
"""
mask后续的位置，返回[size, size]尺寸下三角Tensor
对角线及其左下角全是1，右上角全是0
"""
attn_shape = (1, size, size)
subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
return torch.from_numpy(subsequent_mask) == 0
# Attention
def attention(query, key, value, mask=None, dropout=None):
"""计算Attention即点乘V"""
d_k = query.size(-1)
# [B, h, L, L]
scores = torch.matmul(query, key.transpose(-2, -1)) \
/ math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
p_attn = F.softmax(scores, dim=-1)
if dropout is not None:
p_attn = dropout(p_attn)
return torch.matmul(p_attn, value), p_attn
class MultiHeadedAttention(nn.Module):
def __init__(self, h, d_model, dropout=0.1):
"""Take in model size and number of heads."""
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
self.d_k = d_model // h
self.h = h
self.linears = clones(nn.Linear(d_model, d_model), 4)
self.attn = None
self.dropout = nn.Dropout(p=dropout)
def forward(self, query, key, value, mask=None):
"""
实现MultiHeadedAttention。
输入的q，k，v是形状 [batch, L, d_model]。
输出的x 的形状同上。
"""
if mask is not None:
# Same mask applied to all h heads.
mask = mask.unsqueeze(1)
nbatches = query.size(0)
# 1) 这一步qkv变化:[batch, L, d_model] ->[batch, h, L, d_model/h]
# 因为初始化不同，所以最后输出的k,q,v矩阵不同
query, key, value = \
[l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
for l, x in zip(self.linears, (query, key, value))]
# 2) 计算注意力attn 得到attn*v 与attn
# qkv :[batch, h, L, d_model/h] -->x:[b, h, L, d_model/h], attn[b, h, L, L]
x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout)
# 3) 上一步的结果合并在一起还原成原始输入序列的形状
x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)
# 最后再过一个线性层
return self.linears[-1](x)
# Position-wise Feed-Forward Networks
class PositionwiseFeedForward(nn.Module):
"""实现FFN函数"""
def __init__(self, d_model, d_ff, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self.w_2 = nn.Linear(d_ff, d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
return self.w_2(self.dropout(F.relu(self.w_1(x))))
# Embeddings
class Embeddings(nn.Module):
def __init__(self, d_model, vocab):
super(Embeddings, self).__init__()
self.lut = nn.Embedding(vocab, d_model)
self.d_model = d_model # 表示embedding的维度
def forward(self, x):
embed = self.lut(x)
dk = math.sqrt(self.d_model)
return embed * dk
# Positional Encoding
class PositionalEncoding(nn.Module):
"""实现PE功能"""
def __init__(self, d_model, dropout, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0., max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0., d_model, 2) *
-(math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term) # 偶数列
pe[:, 1::2] = torch.cos(position * div_term) # 奇数列
pe = pe.unsqueeze(0) # [1, max_len, d_model]
self.register_buffer('pe', pe)
def forward(self, x):
x = x + Variable(self.pe[:, :x.size(1)], requires_grad=False)
return self.dropout(x)
# 定义一个接受超参数并生成完整模型的函数
def make_model(src_vocab, tgt_vocab, N=6, d_model=512, d_ff=2048, h=8, dropout=0.1):
"""根据输入的超参数构建一个模型"""
c = copy.deepcopy
attn = MultiHeadedAttention(h, d_model)
ff = PositionwiseFeedForward(d_model, d_ff, dropout)
position = PositionalEncoding(d_model, dropout)
model = EncoderDecoder(
Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
Decoder(DecoderLayer(d_model, c(attn), c(attn),
c(ff), dropout), N),
nn.Sequential(Embeddings(d_model, src_vocab), c(position)),
nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),
Generator(d_model, tgt_vocab))
# 使用xavier初始化参数
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
return model之后生成模拟数据进行训练过程
# 定义个一个Batch对象
class Batch(object):
"""定义一个训练时需要的批次数据对象，封装了用于训练的src和tgt句子，以及mask"""
def __init__(self, src, trg=None, pad=0):
self.src = src # B 个序列[1,5,3, 0]
self.src_mask = (src != pad).unsqueeze(-2) # [[1,1,1,0]]
if trg is not None:
self.trg = trg[:, :-1] #
self.trg_y = trg[:, 1:] # 后挪一个位置开始
self.trg_mask = \
self.make_std_mask(self.trg, pad)
self.ntokens = (self.trg_y != pad).data.sum()
@staticmethod
def make_std_mask(tgt, pad):
"""Create a mask to hide padding and future words."""
tgt_mask = (tgt != pad).unsqueeze(-2)
tgt_mask = tgt_mask & Variable(
subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))
return tgt_mask
# 定义一个训练函数用于训练和计算损失、更新梯度
def run_epoch(data_iter, model, loss_compute, device):
"""提供训练和日志功能"""
start = time.time()
total_tokens = 0
total_loss = 0
tokens = 0
for i, batch in enumerate(data_iter):
src = batch.src.to(device)
trg = batch.trg.to(device)
src_mask = batch.src_mask.to(device)
trg_mask = batch.trg_mask.to(device)
trg_y = batch.trg_y.to(device)
ntokens = batch.ntokens.to(device)
out = model.forward(src, trg, src_mask, trg_mask)
loss = loss_compute(out, trg_y, ntokens)
total_loss += loss.detach().cpu().numpy()
total_tokens += ntokens.cpu().numpy()
tokens += ntokens.cpu().numpy()
if i % 50 == 1:
elapsed = time.time() - start
print("Epoch Step: %d Loss: %f Tokens per Sec: %f" %
(i, loss.detach().cpu().numpy() / ntokens.cpu().numpy(), tokens / elapsed))
start = time.time()
tokens = 0
return total_loss / total_tokens
class NoamOpt(object):
"""Optim wrapper that implements rate."""
def __init__(self, model_size, factor, warmup, optimizer):
self.optimizer = optimizer
self._step = 0
self.warmup = warmup
self.factor = factor
self.model_size = model_size
self._rate = 0
def step(self):
"""Update parameters and rate"""
self._step += 1
rate = self.rate()
for p in self.optimizer.param_groups:
p['lr'] = rate
self._rate = rate
self.optimizer.step()
def rate(self, step=None):
"""Implement `lrate` above"""
if step is None:
step = self._step
return self.factor * \
(self.model_size ** (-0.5) *
min(step ** (-0.5), step * self.warmup ** (-1.5)))
class LabelSmoothing(nn.Module):
"""实现labelsmoothing."""
def __init__(self, size, padding_idx, smoothing=0.0):
super(LabelSmoothing, self).__init__()
self.criterion = nn.KLDivLoss(reduction='sum')
self.padding_idx = padding_idx
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
self.size = size
self.true_dist = None
def forward(self, x, target):
assert x.size(1) == self.size
true_dist = x.data.clone()
true_dist.fill_(self.smoothing / (self.size - 2))
true_dist.scatter_(1, target.data.type(torch.int64).unsqueeze(1), self.confidence)
# true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
true_dist[:, self.padding_idx] = 0
mask = torch.nonzero(target.data == self.padding_idx)
if mask.dim() > 0:
true_dist.index_fill_(0, mask.squeeze(), 0.0)
self.true_dist = true_dist
return self.criterion(x, Variable(true_dist, requires_grad=False))
# 数据生成
def data_gen(V, batch, nbatches):
"""Generate random data for a src-tgt copy task."""
for i in range(nbatches):
data = torch.from_numpy(np.random.randint(1, V, size=(batch, 10)))
data[:, 0] = 1.
src = Variable(data, requires_grad=False)
tgt = Variable(data, requires_grad=False)
yield Batch(src, tgt, 0)
class SimpleLossCompute(object):
"""A simple loss compute and train function."""
def __init__(self, generator, criterion, opt=None):
self.generator = generator
self.criterion = criterion
self.opt = opt
def __call__(self, x, y, norm):
x = self.generator(x)
loss = self.criterion(x.contiguous().view(-1, x.size(-1)),
y.contiguous().view(-1)) / norm
loss.backward()
if self.opt is not None:
self.opt.step()
self.opt.optimizer.zero_grad()
return loss.item() * norm.float()
V = 11
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
criterion = LabelSmoothing(size=V, padding_idx=0, smoothing=0.1)
model = make_model(V, V, N=2)
model = model.to(device)
model_opt = NoamOpt(model.src_embed[0].d_model, 1, 400,
torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
for epoch in range(5):
model.train()
loss_func = SimpleLossCompute(model.generator, criterion, model_opt)
run_epoch(data_gen(V, 30, 20), model, loss_func, device)
model.eval()
print(run_epoch(data_gen(V, 30, 5), model,
SimpleLossCompute(model.generator, criterion, None), device))将以上代码放在同一个py文件中，通过断点调试可以加深对transformer算法整体框架的理解。