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import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import ones_, trunc_normal_, zeros_
from .nrtr_decoder import TransformerBlock, Embeddings
class CPA(nn.Module):
def __init__(self, dim, max_len=25):
super(CPA, self).__init__()
self.fc1 = nn.Linear(dim, dim)
self.fc2 = nn.Linear(dim, dim)
self.fc3 = nn.Linear(dim, dim)
self.pos_embed = nn.Parameter(torch.zeros([1, max_len + 1, dim],
dtype=torch.float32),
requires_grad=True)
trunc_normal_(self.pos_embed, std=0.02)
def forward(self, feat):
# feat: B, L, Dim
feat = feat.mean(1).unsqueeze(1) # B, 1, Dim
x = self.fc1(feat) + self.pos_embed # B max_len dim
x = F.softmax(self.fc2(F.tanh(x)), -1) # B max_len dim
x = self.fc3(feat * x) # B max_len dim
return x
class ARDecoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
nhead=None,
num_decoder_layers=6,
max_len=25,
attention_dropout_rate=0.0,
residual_dropout_rate=0.1,
scale_embedding=True,
):
super(ARDecoder, self).__init__()
self.out_channels = out_channels
self.ignore_index = out_channels - 1
self.bos = out_channels - 2
self.eos = 0
self.max_len = max_len
d_model = in_channels
dim_feedforward = d_model * 4
nhead = nhead if nhead is not None else d_model // 32
self.embedding = Embeddings(
d_model=d_model,
vocab=self.out_channels,
padding_idx=0,
scale_embedding=scale_embedding,
)
self.pos_embed = nn.Parameter(torch.zeros([1, max_len + 1, d_model],
dtype=torch.float32),
requires_grad=True)
trunc_normal_(self.pos_embed, std=0.02)
self.decoder = nn.ModuleList([
TransformerBlock(
d_model,
nhead,
dim_feedforward,
attention_dropout_rate,
residual_dropout_rate,
with_self_attn=True,
with_cross_attn=False,
) for i in range(num_decoder_layers)
])
self.tgt_word_prj = nn.Linear(d_model,
self.out_channels - 2,
bias=False)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
def forward_train(self, src, tgt):
tgt = tgt[:, :-1]
tgt = self.embedding(
tgt) + src[:, :tgt.shape[1]] + self.pos_embed[:, :tgt.shape[1]]
tgt_mask = self.generate_square_subsequent_mask(
tgt.shape[1], device=src.get_device())
for decoder_layer in self.decoder:
tgt = decoder_layer(tgt, self_mask=tgt_mask)
output = tgt
logit = self.tgt_word_prj(output)
return logit
def forward(self, src, data=None):
if self.training:
max_len = data[1].max()
tgt = data[0][:, :2 + max_len]
res = self.forward_train(src, tgt)
else:
res = self.forward_test(src)
return res
def forward_test(self, src):
bs = src.shape[0]
src = src + self.pos_embed
dec_seq = torch.full((bs, self.max_len + 1),
self.ignore_index,
dtype=torch.int64,
device=src.get_device())
dec_seq[:, 0] = self.bos
logits = []
for len_dec_seq in range(0, self.max_len):
dec_seq_embed = self.embedding(
dec_seq[:, :len_dec_seq + 1]) # N dim 26+10 # </s> 012 a
dec_seq_embed = dec_seq_embed + src[:, :len_dec_seq + 1]
tgt_mask = self.generate_square_subsequent_mask(
dec_seq_embed.shape[1], src.get_device())
tgt = dec_seq_embed # bs, 3, dim #bos, a, b, c, ... eos
for decoder_layer in self.decoder:
tgt = decoder_layer(tgt, self_mask=tgt_mask)
dec_output = tgt
dec_output = dec_output[:, -1:, :]
word_prob = F.softmax(self.tgt_word_prj(dec_output), dim=-1)
logits.append(word_prob)
if len_dec_seq < self.max_len:
# greedy decode. add the next token index to the target input
dec_seq[:, len_dec_seq + 1] = word_prob.squeeze(1).argmax(-1)
# Efficient batch decoding: If all output words have at least one EOS token, end decoding.
if (dec_seq == self.eos).any(dim=-1).all():
break
logits = torch.cat(logits, dim=1)
return logits
def generate_square_subsequent_mask(self, sz, device):
"""Generate a square mask for the sequence.
The masked positions are filled with float('-inf'). Unmasked positions
are filled with float(0.0).
"""
mask = torch.zeros([sz, sz], dtype=torch.float32)
mask_inf = torch.triu(
torch.full((sz, sz), dtype=torch.float32, fill_value=-torch.inf),
diagonal=1,
)
mask = mask + mask_inf
return mask.unsqueeze(0).unsqueeze(0).to(device)
class OTEDecoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
max_len=25,
num_heads=None,
ar=False,
num_decoder_layers=1,
**kwargs):
super(OTEDecoder, self).__init__()
self.out_channels = out_channels - 2 # none + 26 + 10
dim = in_channels
self.dim = dim
self.max_len = max_len + 1 # max_len + eos
self.cpa = CPA(dim=dim, max_len=max_len)
self.ar = ar
if ar:
self.ar_decoder = ARDecoder(in_channels=dim,
out_channels=out_channels,
nhead=num_heads,
num_decoder_layers=num_decoder_layers,
max_len=max_len)
else:
self.fc = nn.Linear(dim, self.out_channels)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
zeros_(m.bias)
elif isinstance(m, nn.LayerNorm):
zeros_(m.bias)
ones_(m.weight)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed'}
def forward(self, x, data=None):
x = self.cpa(x)
if self.ar:
return self.ar_decoder(x, data=data)
logits = self.fc(x) # B, 26, 37
if self.training:
logits = logits[:, :data[1].max() + 1]
return logits
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