基于注意力机制，机器之心带你理解与训练神经机器翻译系统

# !pip install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl numpy matplotlib spacy torchtext seaborn 
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")
%matplotlib inline

class EncoderDecoder(nn.Module):
 """
 A standard Encoder-Decoder architecture. Base for this and many 
 other models.
 """
 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 forward(self, src, tgt, src_mask, tgt_mask):
 "Take in and process masked src and target sequences."
 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):
 "Define standard linear + softmax generation step."
 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):
 "Produce N identical layers."
 return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
class Encoder(nn.Module):
 "Core encoder is a stack of N layers"
 def __init__(self, layer, N):
 super(Encoder, self).__init__()
 self.layers = clones(layer, N)
 self.norm = LayerNorm(layer.size)
 def forward(self, x, mask):
 "Pass the input (and mask) through each layer in turn."
 for layer in self.layers:
 x = layer(x, mask)
 return self.norm(x)

class LayerNorm(nn.Module):
 "Construct a layernorm module (See citation for details)."
 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 SublayerConnection(nn.Module):
 """
 A residual connection followed by a layer norm.
 Note for code simplicity the norm is first as opposed to last.
 """
 def __init__(self, size, dropout):
 super(SublayerConnection, self).__init__()
 self.norm = LayerNorm(size)
 self.dropout = nn.Dropout(dropout)
 def forward(self, x, sublayer):
 "Apply residual connection to any sublayer with the same size."
 return x + self.dropout(sublayer(self.norm(x)))

class EncoderLayer(nn.Module):
 "Encoder is made up of self-attn and feed forward (defined below)"
 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):
 "Follow Figure 1 (left) for connections."
 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):
 "Generic N layer decoder with masking."
 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 (defined below)"
 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):
 "Follow Figure 1 (right) for connections."
 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

plt.figure(figsize=(5,5))
plt.imshow(subsequent_mask(20)[0])
None

Image(filename='images/ModalNet-19.png')

def attention(query, key, value, mask=None, dropout=None):
 "Compute 'Scaled Dot Product Attention'"
 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):
 "Take in model size and number of heads."
 super(MultiHeadedAttention, self).__init__()
 assert d_model % h == 0
 # We assume d_v always equals d_k
 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):
 "Implements Figure 2"
 if mask is not None:
 # Same mask applied to all h heads.
 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):
 "Implements FFN equation."
 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)

plt.figure(figsize=(15, 5))
pe = PositionalEncoding(20, 0)
y = pe.forward(Variable(torch.zeros(1, 100, 20)))
plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())
plt.legend(["dim %d"%p for p in [4,5,6,7]])
None

def make_model(src_vocab, tgt_vocab, N=6, 
 d_model=512, d_ff=2048, h=8, dropout=0.1):
 "Helper: Construct a model from hyperparameters."
 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))
 # This was important from their code. 
 # Initialize parameters with Glorot / fan_avg.
 for p in model.parameters():
 if p.dim() > 1:
 nn.init.xavier_uniform(p)
 return model

# Small example model.
tmp_model = make_model(10, 10, 2)
None

class Batch:
 "Object for holding a batch of data with mask during training."
 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):
 "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

def run_epoch(data_iter, model, loss_compute):
 "Standard Training and Logging Function"
 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))

# Three settings of the lrate hyperparameters.
opts = [NoamOpt(512, 1, 4000, None), 
 NoamOpt(512, 1, 8000, None),
 NoamOpt(256, 1, 4000, None)]
plt.plot(np.arange(1, 20000), [[opt.rate(i) for opt in opts] for i in range(1, 20000)])
plt.legend(["512:4000", "512:8000", "256:4000"])
None

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))

# Example of label smoothing.
crit = LabelSmoothing(5, 0, 0.4)
predict = torch.FloatTensor([[0, 0.2, 0.7, 0.1, 0],
 [0, 0.2, 0.7, 0.1, 0], 
 [0, 0.2, 0.7, 0.1, 0]])
v = crit(Variable(predict.log()), 
 Variable(torch.LongTensor([2, 1, 0])))
# Show the target distributions expected by the system.
plt.imshow(crit.true_dist)
None

crit = LabelSmoothing(5, 0, 0.1)
def loss(x):
 d = x + 3 * 1
 predict = torch.FloatTensor([[0, x / d, 1 / d, 1 / d, 1 / d],
 ])
 #print(predict)
 return crit(Variable(predict.log()),
 Variable(torch.LongTensor([1]))).data[0]
plt.plot(np.arange(1, 100), [loss(x) for x in range(1, 100)])
None

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:
 "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.data[0] * norm

# Train the simple copy task.
V = 11
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)))
Epoch Step: 1 Loss: 3.023465 Tokens per Sec: 403.074173
Epoch Step: 1 Loss: 1.920030 Tokens per Sec: 641.689380
1.9274832487106324
Epoch Step: 1 Loss: 1.940011 Tokens per Sec: 432.003378
Epoch Step: 1 Loss: 1.699767 Tokens per Sec: 641.979665
1.657595729827881
Epoch Step: 1 Loss: 1.860276 Tokens per Sec: 433.320240
Epoch Step: 1 Loss: 1.546011 Tokens per Sec: 640.537198
1.4888023376464843
Epoch Step: 1 Loss: 1.278768 Tokens per Sec: 433.568756
Epoch Step: 1 Loss: 1.062384 Tokens per Sec: 642.542067
0.9853351473808288
Epoch Step: 1 Loss: 1.269471 Tokens per Sec: 433.388727
Epoch Step: 1 Loss: 0.590709 Tokens per Sec: 642.862135
0.34273059368133546

def greedy_decode(model, src, src_mask, max_len, start_symbol):
 memory = model.encode(src, src_mask)
 ys = torch.ones(1, 1).fill_(start_symbol).type_as(src.data)
 for i in range(max_len-1):
 out = model.decode(memory, src_mask, 
 Variable(ys), 
 Variable(subsequent_mask(ys.size(1))
 .type_as(src.data)))
 prob = model.generator(out[:, -1])
 _, next_word = torch.max(prob, dim = 1)
 next_word = next_word.data[0]
 ys = torch.cat([ys, 
 torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=1)
 return ys
model.eval()
src = Variable(torch.LongTensor([[1,2,3,4,5,6,7,8,9,10]]) )
src_mask = Variable(torch.ones(1, 1, 10) )
print(greedy_decode(model, src, src_mask, max_len=10, start_symbol=1))
 1 2 3 4 5 6 7 8 9 10
[torch.LongTensor of size 1x10]

#!pip install torchtext spacy
#!python -m spacy download en
#!python -m spacy download de

# For data loading.
from torchtext import data, datasets
if True:
 import spacy
 spacy_de = spacy.load('de')
 spacy_en = spacy.load('en')
 def tokenize_de(text):
 return [tok.text for tok in spacy_de.tokenizer(text)]
 def tokenize_en(text):
 return [tok.text for tok in spacy_en.tokenizer(text)]
 BOS_WORD = '<s>'
 EOS_WORD = '</s>'
 BLANK_WORD = "<blank>"
 SRC = data.Field(tokenize=tokenize_de, pad_token=BLANK_WORD)
 TGT = data.Field(tokenize=tokenize_en, init_token = BOS_WORD, 
 eos_token = EOS_WORD, pad_token=BLANK_WORD)
 MAX_LEN = 100
 train, val, test = datasets.IWSLT.splits(
 exts=('.de', '.en'), fields=(SRC, TGT), 
 filter_pred=lambda x: len(vars(x)['src']) <= MAX_LEN and 
 len(vars(x)['trg']) <= MAX_LEN)
 MIN_FREQ = 2
 SRC.build_vocab(train.src, min_freq=MIN_FREQ)
 TGT.build_vocab(train.trg, min_freq=MIN_FREQ)

class MyIterator(data.Iterator):
 def create_batches(self):
 if self.train:
 def pool(d, random_shuffler):
 for p in data.batch(d, self.batch_size * 100):
 p_batch = data.batch(
 sorted(p, key=self.sort_key),
 self.batch_size, self.batch_size_fn)
 for b in random_shuffler(list(p_batch)):
 yield b
 self.batches = pool(self.data(), self.random_shuffler)
 else:
 self.batches = []
 for b in data.batch(self.data(), self.batch_size,
 self.batch_size_fn):
 self.batches.append(sorted(b, key=self.sort_key))
def rebatch(pad_idx, batch):
 "Fix order in torchtext to match ours"
 src, trg = batch.src.transpose(0, 1), batch.trg.transpose(0, 1)
 return Batch(src, trg, pad_idx)

replicate - split modules onto different gpus.
scatter - split batches onto different gpus
parallel_apply - apply module to batches on different gpus
gather - pull scattered data back onto one gpu.
nn.DataParallel - a special module wrapper that calls these all before evaluating.
# Skip if not interested in multigpu.
class MultiGPULossCompute:
 "A multi-gpu loss compute and train function."
 def __init__(self, generator, criterion, devices, opt=None, chunk_size=5):
 # Send out to different gpus.
 self.generator = generator
 self.criterion = nn.parallel.replicate(criterion, 
 devices=devices)
 self.opt = opt
 self.devices = devices
 self.chunk_size = chunk_size
 def __call__(self, out, targets, normalize):
 total = 0.0
 generator = nn.parallel.replicate(self.generator, 
 devices=self.devices)
 out_scatter = nn.parallel.scatter(out, 
 target_gpus=self.devices)
 out_grad = [[] for _ in out_scatter]
 targets = nn.parallel.scatter(targets, 
 target_gpus=self.devices)
 # Divide generating into chunks.
 chunk_size = self.chunk_size
 for i in range(0, out_scatter[0].size(1), chunk_size):
 # Predict distributions
 out_column = [[Variable(o[:, i:i+chunk_size].data, 
 requires_grad=self.opt is not None)] 
 for o in out_scatter]
 gen = nn.parallel.parallel_apply(generator, out_column)
 # Compute loss. 
 y = [(g.contiguous().view(-1, g.size(-1)), 
 t[:, i:i+chunk_size].contiguous().view(-1)) 
 for g, t in zip(gen, targets)]
 loss = nn.parallel.parallel_apply(self.criterion, y)
 # Sum and normalize loss
 l = nn.parallel.gather(loss, 
 target_device=self.devices[0])
 l = l.sum()[0] / normalize
 total += l.data[0]
 # Backprop loss to output of transformer
 if self.opt is not None:
 l.backward()
 for j, l in enumerate(loss):
 out_grad[j].append(out_column[j][0].grad.data.clone())
 # Backprop all loss through transformer. 
 if self.opt is not None:
 out_grad = [Variable(torch.cat(og, dim=1)) for og in out_grad]
 o1 = out
 o2 = nn.parallel.gather(out_grad, 
 target_device=self.devices[0])
 o1.backward(gradient=o2)
 self.opt.step()
 self.opt.optimizer.zero_grad()
 return total * normalize

# GPUs to use
devices = [0, 1, 2, 3]
if True:
 pad_idx = TGT.vocab.stoi["<blank>"]
 model = make_model(len(SRC.vocab), len(TGT.vocab), N=6)
 model.cuda()
 criterion = LabelSmoothing(size=len(TGT.vocab), padding_idx=pad_idx, smoothing=0.1)
 criterion.cuda()
 BATCH_SIZE = 12000
 train_iter = MyIterator(train, batch_size=BATCH_SIZE, device=0,
 repeat=False, sort_key=lambda x: (len(x.src), len(x.trg)),
 batch_size_fn=batch_size_fn, train=True)
 valid_iter = MyIterator(val, batch_size=BATCH_SIZE, device=0,
 repeat=False, sort_key=lambda x: (len(x.src), len(x.trg)),
 batch_size_fn=batch_size_fn, train=False)
 model_par = nn.DataParallel(model, device_ids=devices)
None

#!wget https://s3.amazonaws.com/opennmt-models/iwslt.pt
if False:
 model_opt = NoamOpt(model.src_embed[0].d_model, 1, 2000,
 torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
 for epoch in range(10):
 model_par.train()
 run_epoch((rebatch(pad_idx, b) for b in train_iter), 
 model_par, 
 MultiGPULossCompute(model.generator, criterion, 
 devices=devices, opt=model_opt))
 model_par.eval()
 loss = run_epoch((rebatch(pad_idx, b) for b in valid_iter), 
 model_par, 
 MultiGPULossCompute(model.generator, criterion, 
 devices=devices, opt=None))
 print(loss)
else:
 model = torch.load("iwslt.pt")

for i, batch in enumerate(valid_iter):
 src = batch.src.transpose(0, 1)[:1]
 src_mask = (src != SRC.vocab.stoi["<blank>"]).unsqueeze(-2)
 out = greedy_decode(model, src, src_mask, 
 max_len=60, start_symbol=TGT.vocab.stoi["<s>"])
 print("Translation:", end="\t")
 for i in range(1, out.size(1)):
 sym = TGT.vocab.itos[out[0, i]]
 if sym == "</s>": break
 print(sym, end =" ")
 print()
 print("Target:", end="\t")
 for i in range(1, batch.trg.size(0)):
 sym = TGT.vocab.itos[batch.trg.data[i, 0]]
 if sym == "</s>": break
 print(sym, end =" ")
 print()
 break
Translation: <unk> <unk> . In my language , that means , thank you very much . 
Gold: <unk> <unk> . It means in my language , thank you very much .