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import torch
import torch.nn.functional as F
from torch import nn
import copy, math
from models.position_encoding import build_position_encoding
class TransformerEncoder(nn.Module):
def __init__(self, enc_layer, num_layers, use_dense_pos=False):
super().__init__()
self.layers = nn.ModuleList([copy.deepcopy(enc_layer) for i in range(num_layers)])
self.num_layers = num_layers
self.use_dense_pos = use_dense_pos
def forward(self, src, pos, padding_mask=None):
if self.use_dense_pos:
## pos encoding at each MH-Attention block (q,k)
output, pos_enc = src, pos
for layer in self.layers:
output, att_map = layer(output, pos_enc, padding_mask)
else:
## pos encoding at input only (q,k,v)
output, pos_enc = src + pos, None
for layer in self.layers:
output, att_map = layer(output, pos_enc, padding_mask)
return output, att_map
class EncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu",
use_dense_pos=False):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
def with_pos_embed(self, tensor, pos):
return tensor if pos is None else tensor + pos
def forward(self, src, pos, padding_mask):
q = k = self.with_pos_embed(src, pos)
src2, attn = self.self_attn(q, k, value=src, key_padding_mask=padding_mask)
src = src + self.dropout1(src2)
src = self.norm1(src)
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
src = src + self.dropout2(src2)
src = self.norm2(src)
return src, attn
class TransformerDecoder(nn.Module):
def __init__(self, dec_layer, num_layers, use_dense_pos=False, return_intermediate=False):
super().__init__()
self.layers = nn.ModuleList([copy.deepcopy(dec_layer) for i in range(num_layers)])
self.num_layers = num_layers
self.use_dense_pos = use_dense_pos
self.return_intermediate = return_intermediate
def forward(self, tgt, tgt_pos, memory, memory_pos,
tgt_padding_mask, src_padding_mask, tgt_attn_mask=None):
intermediate = []
if self.use_dense_pos:
## pos encoding at each MH-Attention block (q,k)
output = tgt
tgt_pos_enc, memory_pos_enc = tgt_pos, memory_pos
for layer in self.layers:
output, att_map = layer(output, tgt_pos_enc, memory, memory_pos_enc,
tgt_padding_mask, src_padding_mask, tgt_attn_mask)
if self.return_intermediate:
intermediate.append(output)
else:
## pos encoding at input only (q,k,v)
output = tgt + tgt_pos
tgt_pos_enc, memory_pos_enc = None, None
for layer in self.layers:
output, att_map = layer(output, tgt_pos_enc, memory, memory_pos_enc,
tgt_padding_mask, src_padding_mask, tgt_attn_mask)
if self.return_intermediate:
intermediate.append(output)
if self.return_intermediate:
return torch.stack(intermediate)
return output, att_map
class DecoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu",
use_dense_pos=False):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.corr_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.norm3 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
def with_pos_embed(self, tensor, pos):
return tensor if pos is None else tensor + pos
def forward(self, tgt, tgt_pos, memory, memory_pos,
tgt_padding_mask, memory_padding_mask, tgt_attn_mask):
q = k = self.with_pos_embed(tgt, tgt_pos)
tgt2, attn = self.self_attn(q, k, value=tgt, key_padding_mask=tgt_padding_mask,
attn_mask=tgt_attn_mask)
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
tgt2, attn = self.corr_attn(query=self.with_pos_embed(tgt, tgt_pos),
key=self.with_pos_embed(memory, memory_pos),
value=memory, key_padding_mask=memory_padding_mask)
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout3(tgt2)
tgt = self.norm3(tgt)
return tgt, attn
def _get_activation_fn(activation):
"""Return an activation function given a string"""
if activation == "relu":
return F.relu
if activation == "gelu":
return F.gelu
if activation == "glu":
return F.glu
raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
#-----------------------------------------------------------------------------------
'''
copy from the implementatoin of "attention-is-all-you-need-pytorch-master" by Yu-Hsiang Huang
'''
class MultiHeadAttention(nn.Module):
''' Multi-Head Attention module '''
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
def forward(self, q, k, v, mask=None):
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
residual = q
# Pass through the pre-attention projection: b x lq x (n*dv)
# Separate different heads: b x lq x n x dv
q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
# Transpose for attention dot product: b x n x lq x dv
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
if mask is not None:
mask = mask.unsqueeze(1) # For head axis broadcasting.
q, attn = self.attention(q, k, v, mask=mask)
# Transpose to move the head dimension back: b x lq x n x dv
# Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
q = self.dropout(self.fc(q))
q += residual
q = self.layer_norm(q)
return q, attn
class ScaledDotProductAttention(nn.Module):
''' Scaled Dot-Product Attention '''
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=None):
attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 0, -1e9)
attn = self.dropout(F.softmax(attn, dim=-1))
output = torch.matmul(attn, v)
return output, attn |