Sorry, it’s code about ’ The Annotated Transformer’.
And the bug is focused on run_epoch , EncoderDecoder.
In the end, I will update the RuntimeError information.
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math, copy, time
from torch.autograd import Variable
import matplotlib.pyplot as plt
import seaborn
seaborn.set_context(context="talk")
class EncoderDecoder(nn.Module):
'''
构建一个标准的Encoder-Decoder架构
可以发现里面的encode和decode处理的不同,在前面说过,decoder比encoder多了一个context attention。
'''
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
self.tgt_embed = tgt_embed
self.generator = generator
def encode(self, src, src_mask):
return self.encoder(self.tgt_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)
def forward(self, src, tgt, src_mask, tgt_mask):
return self.decode(self.encode(src, src_mask), src_mask, tgt, tgt_mask)
class Generator(nn.Module):
'''
定义标准的线性+softmax生成步骤
这是在8. Embeddings和Softmax中
'''
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)
def clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
class LayerNorm(nn.Module):
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):
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 Encoder(nn.Module):
'''
核心encoder是N层构成的堆
'''
def __init__(self, layer, N):
super(Encoder, self).__init__()
self.layers = clones(layer, N)
self.norm = LayerNorm(layer.size)
def forward(self, x, mask):
for layer in self.layers:
x = layer(x, mask)
return self.norm(x)
class SublayerConnection(nn.Module):
def __init__(self, size, dropout):
super(SublayerConnection, self).__init__()
self.norm = LayerNorm(size)
self.dropout = nn.Dropout(dropout)
def forward(self, x, sublayer):
'''
将残差连接以同样的size到任意一个子层
:param x:
:param sublayer:
:return:
'''
return x + self.dropout(sublayer(self.norm(x)))
class EncoderLayer(nn.Module):
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):
x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
return self.sublayer[1](x, self.feed_forward)
class Decoder(nn.Module):
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):
def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
super(DecoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.src_attn = src_attn
self.sublayer = clones(SublayerConnection(size, dropout), 3)
self.size = size
def forward(self, x, memory, src_mask, tgt_mask):
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 out subsequent positions."
attn_shape = (1, size, size)
subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
return torch.from_numpy(subsequent_mask) == 0
def attention(query, key, value, mask=None, dropout=None):
d_k = query.size(-1)
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):
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):
if mask is not None:
mask = mask.unsqueeze(1)
nbatches = query.size(0)
# 1) Do all the linear projections in batch from d_model => h x d_k
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) Apply attention on all the projected vectors in batch.
x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout)
# 3) "Concat" using a view and apply a final linear.
x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)
return self.linears[-1](x)
class PositionwiseFeedForward(nn.Module):
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))))
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
def forward(self, x):
return self.lut(x) * math.sqrt(self.d_model)
class PositionalEncoding(nn.Module):
"Implement the PE function."
def __init__(self, d_model, dropout, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
# Compute the positional encodings once in log space.
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)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + Variable(self.pe[:, :x.size(1)],
requires_grad=False)
return self.dropout(x)
def make_model(src_vocab, tgt_vocab, N=6, d_model=512, h=8, d_ff=2048, dropout=0.1):
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)
)
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
return model
class Batch:
def __init__(self, src, trg=None, pad=0):
self.src = src
self.src_mask = (src != pad).unsqueeze(-2)
if trg is not None:
self.trg = trg[:, :-1]
self.trg_y = trg[:, 1:]
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):
"构建一个mask用于隐藏padding和未来的词"
tgt_mask = (tgt != pad).unsqueeze(-2)
tgt_mask = tgt_mask & Variable(subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))
return tgt_mask
def run_epoch(data_iter, model, loss_compute):
start = time.time()
total_tokens = 0
total_loss = 0
tokens = 0
for i, batch in enumerate(data_iter):
out = model.forward(batch.src, batch.trg, batch.src_mask, batch.trg_mask)
loss = loss_compute(out, batch.trg_y, batch.ntokens)
total_loss += loss
total_tokens += batch.ntokens
tokens += batch.ntokens
if i % 50 == 1:
elapsed = time.time() - start
print("Epoch Step: %d Loss: %f Tokens per Sec: %f" % (i, loss / batch.ntokens, tokens / elapsed))
start = time.time()
tokens = 0
return total_loss / total_tokens
global max_src_in_batch, max_tgt_in_batch
def batch_size_fn(new, count, sofar):
"Keep augmenting batch and calculate total number of tokens + padding."
global max_src_in_batch, max_tgt_in_batch
if count == 1:
max_src_in_batch = 0
max_tgt_in_batch = 0
max_src_in_batch = max(max_src_in_batch, len(new.src))
max_tgt_in_batch = max(max_tgt_in_batch, len(new.trg) + 2)
src_elements = count * max_src_in_batch
tgt_elements = count * max_tgt_in_batch
return max(src_elements, tgt_elements)
class NoamOpt:
"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)))
def get_std_opt(model):
return NoamOpt(model.src_embed[0].d_model, 2, 4000,
torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
class LabelSmoothing(nn.Module):
"Implement label smoothing."
def __init__(self, size, padding_idx, smoothing=0.0):
super(LabelSmoothing, self).__init__()
self.criterion = nn.KLDivLoss(size_average=False)
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.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))
def data_gen(V, batch, nbatches):
'''
为src-tgt复制任务生成随机数据
:param V:
:param batch:
:param nbatches:
:return:
'''
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:
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.float()
loss.backward()
if self.opt is not None:
self.opt.step()
self.opt.optimizer.zero_grad()
return loss.item() * norm.float().item()
if __name__ == '__main__':
V = 10
criterion = LabelSmoothing(size=V, padding_idx=0, smoothing=0.0)
model = make_model(V, V, N=2)
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(10):
model.train()
run_epoch(data_gen(V, 30, 20), model, SimpleLossCompute(model.generator, criterion, model_opt))
model.eval()
print(run_epoch(data_gen(V, 30, 5), model, SimpleLossCompute(model.generator, criterion, None)))
Traceback (most recent call last):
File “C:/Users/01/Desktop/机器学习作业/AllenNLP/[NLP]23AllenNLP08Transformer.py”, line 664, in
run_epoch(data_gen(V, 30, 20), model, SimpleLossCompute(model.generator, criterion, model_opt))
File “C:/Users/01/Desktop/机器学习作业/AllenNLP/[NLP]23AllenNLP08Transformer.py”, line 433, in run_epoch
out = model.forward(batch.src, batch.trg, batch.src_mask, batch.trg_mask)
File “C:/Users/01/Desktop/机器学习作业/AllenNLP/[NLP]23AllenNLP08Transformer.py”, line 42, in forward
return self.decode(self.encode(src, src_mask), src_mask, tgt, tgt_mask)
File “C:/Users/01/Desktop/机器学习作业/AllenNLP/[NLP]23AllenNLP08Transformer.py”, line 36, in encode
return self.encoder(self.tgt_embed(src), src_mask)
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\modules\module.py”, line 491, in call
result = self.forward(*input, **kwargs)
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\modules\container.py”, line 91, in forward
input = module(input)
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\modules\module.py”, line 491, in call
result = self.forward(*input, **kwargs)
File “C:/Users/01/Desktop/机器学习作业/AllenNLP/[NLP]23AllenNLP08Transformer.py”, line 319, in forward
return self.lut(x) * math.sqrt(self.d_model)
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\modules\module.py”, line 491, in call
result = self.forward(*input, **kwargs)
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\modules\sparse.py”, line 108, in forward
self.norm_type, self.scale_grad_by_freq, self.sparse)
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\functional.py”, line 1076, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected tensor for argument #1 ‘indices’ to have scalar type Long; but got CPUIntTensor instead (while checking arguments for embedding)
Best
Alex