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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt
class CausalConv1d(torch.nn.Conv1d):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
dilation=1,
groups=1,
bias=True):
self.__padding = (kernel_size - 1) * dilation
super(CausalConv1d, self).__init__(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=self.__padding,
dilation=dilation,
groups=groups,
bias=bias)
def forward(self, input):
result = super(CausalConv1d, self).forward(input)
if self.__padding != 0:
return result[:, :, :-self.__padding]
return result
class TriangularCausalMask():
def __init__(self, b, n, device="cpu"):
mask_shape = [b, 1, n, n]
with torch.no_grad():
self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)
@property
def mask(self):
return self._mask
class MultiheadAttention(nn.Module):
def __init__(self, d_model, n_heads, d_keys, mask_flag, r_att_drop=0.1):
super(MultiheadAttention, self).__init__()
self.h, self.d, self.mask_flag= n_heads, d_keys, mask_flag
self.proj_q = nn.Linear(d_model, self.h * self.d)
self.proj_k = nn.Linear(d_model, self.h * self.d)
self.proj_v = nn.Linear(d_model, self.h * self.d)
self.proj_out = nn.Linear(self.h * self.d, d_model)
self.dropout = nn.Dropout(r_att_drop)
def forward(self, q, k, v):
b, n_q, n_k, h, d = q.size(0), q.size(1), k.size(1), self.h, self.d
q, k, v = self.proj_q(q), self.proj_k(k), self.proj_v(v) # b, n_*, h*d
q, k, v = map(lambda x: x.reshape(b, -1, h, d), [q, k, v]) # b, n_*, h, d
scores = torch.einsum('bnhd,bmhd->bhnm', (q,k)) # b, h, n_q, n_k
if self.mask_flag:
att_mask = TriangularCausalMask(b, n_q, device=q.device)
scores.masked_fill_(att_mask.mask, -np.inf)
att = F.softmax(scores / (self.d ** .5), dim=-1) # b, h, n_q, n_k
att = self.dropout(att)
att_out = torch.einsum('bhnm,bmhd->bnhd', (att,v)) # b, n_q, h, d
att_out = att_out.reshape(b, -1, h*d) # b, n_q, h*d
out = self.proj_out(att_out) # b, n_q, d_model
return out
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