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import math
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.modules.normalization import LayerNorm
class DilatedMultiheadSelfAttentionWithRelativePositionalEmbedding(nn.Module):
def __init__(self, dmodel, num_heads, dropout=0, Er_provided=False, attn_len=5):
super(DilatedMultiheadSelfAttentionWithRelativePositionalEmbedding, self).__init__()
self.attn_len = attn_len
self.dmodel = dmodel
self.num_heads = num_heads
self.head_dim = dmodel // num_heads
assert self.head_dim * num_heads == dmodel, "embed_dim must be divisible by num_heads"
self.key = nn.Linear(dmodel, dmodel)
self.value = nn.Linear(dmodel, dmodel)
self.query = nn.Linear(dmodel, dmodel)
self.dropout = nn.Dropout(dropout)
self.Er_provided = Er_provided
if not Er_provided:
self.Er = nn.Parameter(torch.randn(num_heads, self.head_dim, attn_len))
def forward(self, query, key, value, layer=0):
#query, key, and value: (batch, time, dmodel), float tensor
batch, time, d_model = query.shape
q = self.query(query).reshape(batch, time, self.num_heads, 1, self.head_dim).transpose(1, 2) #(batch, num_head, time, 1, head_dim)
k = self.key(key).reshape(batch, time, self.num_heads, 1, self.head_dim).transpose(1, 2) #(batch, num_head, time, 1, head_dim)
v = self.value(value).reshape(batch, time, self.num_heads, 1, self.head_dim).transpose(1, 2) #(batch, num_head, time, 1, head_dim)
k = torch.cat(
(
self.kv_roll(k[:, 0: 4], layer, padding_value=0, shift=0),
self.kv_roll(k[:, 4: 5], layer, padding_value=0, shift=-2),
self.kv_roll(k[:, 5: 6], layer, padding_value=0, shift=-1),
self.kv_roll(k[:, 6: 7], layer, padding_value=0, shift=1),
self.kv_roll(k[:, 6: 7], layer, padding_value=0, shift=2) #This should be k[:, 7: 8]. We had this bug during training so we keep it to fit the checkpoints.
),
dim=1
) #we define 4 symmetrical heads and 4 skewed heads
v = torch.cat(
(
self.kv_roll(v[:, 0: 4], layer, padding_value=0, shift=0),
self.kv_roll(v[:, 4: 5], layer, padding_value=0, shift=-2),
self.kv_roll(v[:, 5: 6], layer, padding_value=0, shift=-1),
self.kv_roll(v[:, 6: 7], layer, padding_value=0, shift=1),
self.kv_roll(v[:, 7: 8], layer, padding_value=0, shift=2)
),
dim=1
) #we define 4 symmetrical heads and 4 skewed heads
Er_t = self.Er.unsqueeze(1).unsqueeze(0) #(1, num_head, 1, head_dim, attn_len)
qk = torch.matmul(q, k.transpose(-2, -1))
attn_mask = torch.zeros_like(qk).masked_fill_((qk==0), float('-inf'))
attn = (qk + torch.matmul(q, Er_t)) / math.sqrt(self.head_dim)
attn = F.softmax(attn + attn_mask, dim=-1)
out = torch.matmul(attn, v) #(batch, num_head, time, 1, head_dim)
out = out.squeeze(-2).transpose(1, 2).reshape(batch, time, d_model)
return self.dropout(out), attn
def kv_roll(self, tensor, layer, padding_value=0, shift=1):
#tensor: (batch, num_head, time, 1, head_dim)
batch, num_head, time, _, head_dim = tensor.shape
tensor = F.pad(tensor, (0, 0, 0, 0, (2**layer)*(self.attn_len//2), (2**layer)*(self.attn_len//2)), mode='constant', value=padding_value)
#(batch, num_head, time+(2**layer)*(self.attn_len//2), 1, head_dim)
tensor = torch.cat([torch.roll(tensor, shifts=-i*(2**layer), dims=2) for i in range(shift, self.attn_len+shift)], dim=-2)
#(batch, num_head, time+(2**layer)*(self.attn_len//2), attn_len, head_dim)
return tensor[:, :, :time, :, :] #(batch, num_head, time, attn_len, head_dim)
class DilatedTransformerLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, Er_provided=False, attn_len=5, norm_first=False, layer_norm_eps=1e-5):
super(DilatedTransformerLayer, self).__init__()
self.self_attn = DilatedMultiheadSelfAttentionWithRelativePositionalEmbedding(d_model, nhead, dropout, Er_provided, attn_len)
# 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.norm_first = norm_first
self.norm1 = LayerNorm(d_model, eps=layer_norm_eps)
self.norm2 = LayerNorm(d_model, eps=layer_norm_eps)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = F.gelu
def forward(self, x, layer=0):
#x: (batch, time, dmodel)
if self.norm_first:
x_ = self._sa_block(self.norm1(x), layer)[0]
x = x + x_
x = x + self._ff_block(self.norm2(x))
else:
x_ = self._sa_block(x, layer)[0]
x = self.norm1(x + x_)
x = self.norm2(x + self._ff_block(x))
return x, x_
def inference(self, x, layer=0):
#x: (batch, time, dmodel)
if self.norm_first:
x_, attn = self._sa_block(self.norm1(x), layer)
x = x + x_
x = x + self._ff_block(self.norm2(x))
else:
x_, attn = self._sa_block(x, layer)
x = self.norm1(x + x_)
x = self.norm2(x + self._ff_block(x))
attn = attn.squeeze(-2) #batch, num_head, time, attn_len
batch, num_head, time, attn_len = attn.shape
padded_attn_len = (attn_len-1) * (2**layer) + 1
tmp_output = torch.zeros(batch, num_head, time, padded_attn_len, device=x.device)
for i, j in enumerate(range(0, padded_attn_len, 2**layer)):
tmp_output[:, :, :, j] = attn[:, :, :, i]
attn = torch.zeros(batch, num_head, time, time+(padded_attn_len-1)*2, device=x.device)
for i in range(time):
attn[:, :, i, i: i+padded_attn_len] = tmp_output[:, :, i]
center = (padded_attn_len-1)
attn = torch.cat(
[
attn[:, 0: 4, :, center - (2**layer) * 2: center - (2**layer) * 2 + time],
attn[:, 4: 5, :, center - (2**layer) * 1: center - (2**layer) * 1 + time],
attn[:, 5: 6, :, center - (2**layer) * 0: center - (2**layer) * 0 + time],
attn[:, 6: 7, :, center - (2**layer) * 3: center - (2**layer) * 3 + time],
attn[:, 7: 8, :, center - (2**layer) * 4: center - (2**layer) * 4 + time]
],
dim=1
) #restore the square attention matrix from dilated self-attention
return x, x_, attn
# self-attention block
def _sa_block(self, x, layer=0):
x, attn = self.self_attn(x, x, x, layer)
return self.dropout1(x), attn
# feed forward block
def _ff_block(self, x):
x = self.linear2(self.dropout(self.activation(self.linear1(x))))
return self.dropout2(x)
if __name__ == '__main__':
BATCH=1
TIME=9
DMODEL=8
N_HEAD=4
ATTN_LEN=5
LAYER=1
x = torch.ones(BATCH, TIME, DMODEL)
model = DilatedMultiheadSelfAttentionWithRelativePositionalEmbedding(dmodel=DMODEL, num_heads=N_HEAD, attn_len=ATTN_LEN)
output, attn = model(x, x, x, layer=LAYER)
print(attn[0, 0, :, :, :]) |