class ParallelTransformerEncoder(nn.Module):
"""Encoder in 'Attention is all you need'
Args:
opt: list of options ( see train.py )
dicts : dictionary (for source language)
"""
def __init__(self, opt, dicts, positional_encoder):
super(ParallelTransformerEncoder, self).__init__()
self.model_size = opt.model_size
self.n_heads = opt.n_heads
self.inner_size = opt.inner_size
self.layers = opt.layers
self.dropout = opt.dropout
self.word_dropout = opt.word_dropout
self.attn_dropout = opt.attn_dropout
self.emb_dropout = opt.emb_dropout
self.time = opt.time
if hasattr(opt, 'grow_dropout'):
self.grow_dropout = opt.grow_dropout
self.word_lut = nn.Embedding(dicts.size(),
self.model_size,
padding_idx=onmt.Constants.PAD)
if opt.time == 'positional_encoding':
self.time_transformer = positional_encoder
elif opt.time == 'gru':
self.time_transformer = nn.GRU(self.model_size, self.model_size, 1, batch_first=True)
elif opt.time == 'lstm':
self.time_transformer = nn.LSTM(self.model_size, self.model_size, 1, batch_first=True)
#~ self.preprocess_layer = PrePostProcessing(self.model_size, self.emb_dropout, sequence='d', static=False)
self.preprocess_layer = PrePostProcessing(self.model_size, self.emb_dropout, sequence='d', static=onmt.Constants.static)
self.postprocess_layer = PrePostProcessing(self.model_size, 0, sequence='n')
self.positional_encoder = positional_encoder
self.layer_modules = nn.ModuleList([ParallelEncoderLayer(self.n_heads, self.model_size, self.dropout, self.inner_size, self.attn_dropout) for _ in range(self.layers)])
def add_layers(self, n_new_layer):
self.new_modules = list()
self.layers += n_new_layer
for i in range(n_new_layer):
layer = ParallelEncoderLayer(self.n_heads, self.model_size, self.dropout, self.inner_size, self.attn_dropout)
# the first layer will use the preprocessing which is the last postprocessing
if i == 0:
layer.preprocess_attn.load_state_dict(self.postprocess_layer.state_dict())
#~ layer.preprocess_attn.layer_norm.function.weight.requires_grad = False
#~ layer.preprocess_attn.layer_norm.function.bias.requires_grad = False
#~ if hasattr(layer.postprocess_attn, 'k'):
#~ layer.postprocess_attn.k.data.fill_(0.01)
# replace the last postprocessing layer with a new one
self.postprocess_layer = PrePostProcessing(self.model_size, 0, sequence='n')
self.layer_modules.append(layer)
def mark_pretrained(self):
self.pretrained_point = self.layers
def forward(self, input, grow=False):
"""
Inputs Shapes:
input: batch_size x len_src (wanna tranpose)
Outputs Shapes:
out: batch_size x len_src x d_model
mask_src
"""
if grow:
return self.forward_grow(input)
""" Embedding: batch_size x len_src x d_model """
emb = embedded_dropout(self.word_lut, input, dropout=self.word_dropout if self.training else 0)
""" Scale the emb by sqrt(d_model) """
if self.time == 'positional_encoding':
emb = emb * math.sqrt(self.model_size)
""" Adding positional encoding """
emb = self.time_transformer(emb)
if isinstance(emb, tuple):
emb = emb[0]
emb = self.preprocess_layer(emb)
mask_src = input.data.eq(onmt.Constants.PAD).unsqueeze(1) # batch_size x len_src x 1 for broadcasting
pad_mask = torch.autograd.Variable(input.data.ne(onmt.Constants.PAD)) # batch_size x len_src
#~ pad_mask = None
context = emb.contiguous()
memory_bank = list()
for i, layer in enumerate(self.layer_modules):
if len(self.layer_modules) - i <= onmt.Constants.checkpointing and self.training:
context, norm_input = checkpoint(custom_layer(layer), context, mask_src, pad_mask)
#~ print(type(context))
else:
context, norm_input = layer(context, mask_src, pad_mask) # batch_size x len_src x d_model
if i > 0: # don't keep the norm input of the first layer (a.k.a embedding)
memory_bank.append(norm_input)
# From Google T2T
# if normalization is done in layer_preprocess, then it should also be done
# on the output, since the output can grow very large, being the sum of
# a whole stack of unnormalized layer outputs.
context = self.postprocess_layer(context)
# make a huge memory bank on the encoder side
memory_bank.append(context)
memory_bank = torch.stack(memory_bank)
return memory_bank, mask_src
def forward_grow(self, input):
"""
Inputs Shapes:
input: batch_size x len_src (wanna tranpose)
Outputs Shapes:
out: batch_size x len_src x d_model
mask_src
"""
with torch.no_grad():
""" Embedding: batch_size x len_src x d_model """
emb = embedded_dropout(self.word_lut, input, dropout=self.word_dropout if self.training else 0)
""" Scale the emb by sqrt(d_model) """
if self.time == 'positional_encoding':
emb = emb * math.sqrt(self.model_size)
""" Adding positional encoding """
emb = self.time_transformer(emb)
if isinstance(emb, tuple):
emb = emb[0]
emb = self.preprocess_layer(emb)
mask_src = input.data.eq(onmt.Constants.PAD).unsqueeze(1) # batch_size x len_src x 1 for broadcasting
pad_mask = torch.autograd.Variable(input.data.ne(onmt.Constants.PAD)) # batch_size x len_src
#~ pad_mask = None
context = emb.contiguous()
memory_bank = list()
for i in range(self.pretrained_point):
layer = self.layer_modules[i]
context, norm_input = layer(context, mask_src, pad_mask) # batch_size x len_src x d_model
if i > 0: # don't keep the norm input of the first layer (a.k.a embedding)
memory_bank.append(norm_input)
for i in range(self.layers - self.pretrained_point):
res_drop_rate = 0.0
if i == 0:
res_drop_rate = self.grow_dropout
layer = self.layer_modules[self.pretrained_point + i]
context, norm_input = layer(context, mask_src, pad_mask, residual_dropout=res_drop_rate) # batch_size x len_src x d_model
memory_bank.append(norm_input)
# From Google T2T
# if normalization is done in layer_preprocess, then it should also be done
# on the output, since the output can grow very large, being the sum of
# a whole stack of unnormalized layer outputs.
context = self.postprocess_layer(context)
# make a huge memory bank on the encoder side
memory_bank.append(context)
memory_bank = torch.stack(memory_bank)
return memory_bank, mask_src
class ParallelTransformerDecoder(nn.Module):
"""Encoder in 'Attention is all you need'
Args:
opt
dicts
"""
def __init__(self, opt, dicts, positional_encoder):
super(ParallelTransformerDecoder, self).__init__()
self.model_size = opt.model_size
self.n_heads = opt.n_heads
self.inner_size = opt.inner_size
self.layers = opt.layers
self.dropout = opt.dropout
self.word_dropout = opt.word_dropout
self.attn_dropout = opt.attn_dropout
self.emb_dropout = opt.emb_dropout
self.time = opt.time
if hasattr(opt, 'grow_dropout'):
self.grow_dropout = opt.grow_dropout
if opt.time == 'positional_encoding':
self.time_transformer = positional_encoder
elif opt.time == 'gru':
self.time_transformer = nn.GRU(self.model_size, self.model_size, 1, batch_first=True)
elif opt.time == 'lstm':
self.time_transformer = nn.LSTM(self.model_size, self.model_size, 1, batch_first=True)
#~ self.preprocess_layer = PrePostProcessing(self.model_size, self.emb_dropout, sequence='d', static=False)
self.preprocess_layer = PrePostProcessing(self.model_size, self.emb_dropout, sequence='d', static=onmt.Constants.static)
self.postprocess_layer = PrePostProcessing(self.model_size, 0, sequence='n')
self.word_lut = nn.Embedding(dicts.size(),
self.model_size,
padding_idx=onmt.Constants.PAD)
self.positional_encoder = positional_encoder
self.layer_modules = nn.ModuleList([DecoderLayer(self.n_heads, self.model_size, self.dropout, self.inner_size, self.attn_dropout) for _ in range(self.layers)])
len_max = self.positional_encoder.len_max
mask = torch.ByteTensor(np.triu(np.ones((len_max,len_max)), k=1).astype('uint8'))
self.register_buffer('mask', mask)
def renew_buffer(self, new_len):
self.positional_encoder.renew(new_len)
mask = torch.ByteTensor(np.triu(np.ones((new_len,new_len)), k=1).astype('uint8'))
self.register_buffer('mask', mask)
def mark_pretrained(self):
self.pretrained_point = self.layers
def add_layers(self, n_new_layer):
self.new_modules = list()
self.layers += n_new_layer
for i in range(n_new_layer):
layer = DecoderLayer(self.n_heads, self.model_size, self.dropout, self.inner_size, self.attn_dropout)
# the first layer will use the preprocessing which is the last postprocessing
if i == 0:
# layer.preprocess_attn = self.postprocess_layer
layer.preprocess_attn.load_state_dict(self.postprocess_layer.state_dict())
#~ layer.preprocess_attn.layer_norm.function.weight.requires_grad = False
#~ layer.preprocess_attn.layer_norm.function.bias.requires_grad = False
# replace the last postprocessing layer with a new one
#~ if hasattr(layer.postprocess_attn, 'k'):
#~ layer.postprocess_attn.k.data.fill_(0.01)
self.postprocess_layer = PrePostProcessing(self.model_size, 0, sequence='n')
self.layer_modules.append(layer)
def forward(self, input, context, src, grow=False):
"""
Inputs Shapes:
input: (Variable) batch_size x len_tgt (wanna tranpose)
context: (Variable) batch_size x len_src x d_model
mask_src (Tensor) batch_size x len_src
Outputs Shapes:
out: batch_size x len_tgt x d_model
coverage: batch_size x len_tgt x len_src
"""
""" Embedding: batch_size x len_tgt x d_model """
if grow:
return self.forward_grow(input, context, src)
emb = embedded_dropout(self.word_lut, input, dropout=self.word_dropout if self.training else 0)
if self.time == 'positional_encoding':
emb = emb * math.sqrt(self.model_size)
""" Adding positional encoding """
emb = self.time_transformer(emb)
if isinstance(emb, tuple):
emb = emb[0]
emb = self.preprocess_layer(emb)
mask_src = src.data.eq(onmt.Constants.PAD).unsqueeze(1)
pad_mask_src = torch.autograd.Variable(src.data.ne(onmt.Constants.PAD))
len_tgt = input.size(1)
mask_tgt = input.data.eq(onmt.Constants.PAD).unsqueeze(1) + self.mask[:len_tgt, :len_tgt]
mask_tgt = torch.gt(mask_tgt, 0)
output = emb.contiguous()
pad_mask_tgt = torch.autograd.Variable(input.data.ne(onmt.Constants.PAD)) # batch_size x len_src
pad_mask_src = torch.autograd.Variable(1 - mask_src.squeeze(1))
#~ memory_bank = None
for i, layer in enumerate(self.layer_modules):
if len(self.layer_modules) - i <= onmt.Constants.checkpointing and self.training:
output, coverage = checkpoint(custom_layer(layer), output, context[i], mask_tgt, mask_src,
pad_mask_tgt, pad_mask_src) # batch_size x len_src x d_model
else:
output, coverage = layer(output, context[i], mask_tgt, mask_src,
pad_mask_tgt, pad_mask_src) # batch_size x len_src x d_model
# From Google T2T
# if normalization is done in layer_preprocess, then it should also be done
# on the output, since the output can grow very large, being the sum of
# a whole stack of unnormalized layer outputs.
output = self.postprocess_layer(output)
return output, coverage
def forward_grow(self, input, context, src):
"""
Inputs Shapes:
input: (Variable) batch_size x len_tgt (wanna tranpose)
context: (Variable) batch_size x len_src x d_model
mask_src (Tensor) batch_size x len_src
Outputs Shapes:
out: batch_size x len_tgt x d_model
coverage: batch_size x len_tgt x len_src
"""
""" Embedding: batch_size x len_tgt x d_model """
with torch.no_grad():
emb = embedded_dropout(self.word_lut, input, dropout=self.word_dropout if self.training else 0)
if self.time == 'positional_encoding':
emb = emb * math.sqrt(self.model_size)
""" Adding positional encoding """
emb = self.time_transformer(emb)
if isinstance(emb, tuple):
emb = emb[0]
emb = self.preprocess_layer(emb)
mask_src = src.data.eq(onmt.Constants.PAD).unsqueeze(1)
pad_mask_src = torch.autograd.Variable(src.data.ne(onmt.Constants.PAD))
len_tgt = input.size(1)
mask_tgt = input.data.eq(onmt.Constants.PAD).unsqueeze(1) + self.mask[:len_tgt, :len_tgt]
mask_tgt = torch.gt(mask_tgt, 0)
output = emb.contiguous()
pad_mask_tgt = torch.autograd.Variable(input.data.ne(onmt.Constants.PAD)) # batch_size x len_src
pad_mask_src = torch.autograd.Variable(1 - mask_src.squeeze(1))
for i in range(self.pretrained_point):
layer = self.layer_modules[i]
output, coverage = layer(output, context[i], mask_tgt, mask_src,
pad_mask_tgt, pad_mask_src) # batch_size x len_src x d_model
for i in range(self.layers - self.pretrained_point):
res_drop_rate = 0.0
if i == 0:
res_drop_rate = self.grow_dropout
layer = self.layer_modules[self.pretrained_point + i]
output, coverage = layer(output, context[self.pretrained_point + i], mask_tgt, mask_src,
pad_mask_tgt, pad_mask_src, residual_dropout=res_drop_rate) # batch_size x len_src x d_model
# From Google T2T
# if normalization is done in layer_preprocess, then it should also be done
# on the output, since the output can grow very large, being the sum of
# a whole stack of unnormalized layer outputs.
output = self.postprocess_layer(output)
return output, coverage
#~ def step(self, input, context, src, buffer=None):
def step(self, input, decoder_state):
"""
Inputs Shapes:
input: (Variable) batch_size x len_tgt (wanna tranpose)
context: (Variable) batch_size x len_src x d_model
mask_src (Tensor) batch_size x len_src
buffer (List of tensors) List of batch_size * len_tgt-1 * d_model for self-attention recomputing
Outputs Shapes:
out: batch_size x len_tgt x d_model
coverage: batch_size x len_tgt x len_src
"""
# note: transpose 1-2 because the first dimension (0) is the number of layer
context = decoder_state.context.transpose(1, 2)
buffer = decoder_state.buffer
src = decoder_state.src.transpose(0, 1)
if decoder_state.input_seq is None:
decoder_state.input_seq = input
else:
# concatenate the last input to the previous input sequence
decoder_state.input_seq = torch.cat([decoder_state.input_seq, input], 0)
input = decoder_state.input_seq.transpose(0, 1)
input_ = input[:,-1].unsqueeze(1)
output_buffer = list()
batch_size = input.size(0)
input_ = input[:,-1].unsqueeze(1)
# print(input_.size())
""" Embedding: batch_size x 1 x d_model """
emb = self.word_lut(input_)
if self.time == 'positional_encoding':
emb = emb * math.sqrt(self.model_size)
""" Adding positional encoding """
if self.time == 'positional_encoding':
emb = self.time_transformer(emb, t=input.size(1))
else:
prev_h = buffer[0] if buffer is None else None
emb = self.time_transformer(emb, prev_h)
buffer[0] = emb[1]
if isinstance(emb, tuple):
emb = emb[0] # emb should be batch_size x 1 x dim
# Preprocess layer: adding dropout
emb = self.preprocess_layer(emb)
# batch_size x 1 x len_src
mask_src = src.data.eq(onmt.Constants.PAD).unsqueeze(1)
pad_mask_src = torch.autograd.Variable(src.data.ne(onmt.Constants.PAD))
len_tgt = input.size(1)
mask_tgt = input.data.eq(onmt.Constants.PAD).unsqueeze(1) + self.mask[:len_tgt, :len_tgt]
# mask_tgt = self.mask[:len_tgt, :len_tgt].unsqueeze(0).repeat(batch_size, 1, 1)
mask_tgt = torch.gt(mask_tgt, 0)
mask_tgt = mask_tgt[:, -1, :].unsqueeze(1)
output = emb.contiguous()
pad_mask_tgt = torch.autograd.Variable(input.data.ne(onmt.Constants.PAD)) # batch_size x len_src
pad_mask_src = torch.autograd.Variable(1 - mask_src.squeeze(1))
memory_bank = None
for i, layer in enumerate(self.layer_modules):
buffer_ = buffer[i] if buffer is not None else None
assert(output.size(1) == 1)
output, coverage, buffer_ = layer.step(output, context[i], mask_tgt, mask_src,
pad_mask_tgt=None, pad_mask_src=None, buffer=buffer_) # batch_size x len_src x d_model
output_buffer.append(buffer_)
buffer = torch.stack(output_buffer)
# From Google T2T
# if normalization is done in layer_preprocess, then it should also be done
# on the output, since the output can grow very large, being the sum of
# a whole stack of unnormalized layer outputs.
output = self.postprocess_layer(output)
decoder_state._update_state(buffer)
return output, coverage
