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"""
Transformer-based varitional encoder model.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import copy
def clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
def build_mask(base_mask):
assert len(base_mask.shape) == 2
batch_size, seq_len = base_mask.shape[0], base_mask.shape[-1]
# create subsequent token mask
sub_mask = torch.tril(torch.ones([seq_len, seq_len],
dtype=torch.uint8)).type_as(base_mask)
sub_mask = sub_mask.unsqueeze(0).expand(batch_size, -1, -1)
base_mask = base_mask.unsqueeze(1).expand(-1, seq_len, -1)
return sub_mask & base_mask
class Adaptor(nn.Module):
def __init__(self, input_dim, tar_dim):
super(Adaptor, self).__init__()
if tar_dim == 32768:
output_channel = 8
elif tar_dim == 16384:
output_channel = 4
else:
raise NotImplementedError("only support 512px, 256px does not need this")
self.tar_dim = tar_dim
self.fc1 = nn.Linear(input_dim, 4096)
self.ln_fc1 = nn.LayerNorm(4096)
self.fc2 = nn.Linear(4096, 4096)
self.ln_fc2 = nn.LayerNorm(4096)
self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1)
self.ln_conv1 = nn.LayerNorm([32, 64, 64])
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
self.ln_conv2 = nn.LayerNorm([64, 64, 64])
self.conv3 = nn.Conv2d(in_channels=64, out_channels=output_channel, kernel_size=3, padding=1)
def forward(self, x):
x = torch.relu(self.ln_fc1(self.fc1(x)))
x = torch.relu(self.ln_fc2(self.fc2(x)))
x = x.view(-1, 1, 64, 64)
x = torch.relu(self.ln_conv1(self.conv1(x)))
x = torch.relu(self.ln_conv2(self.conv2(x)))
x = self.conv3(x)
x = x.view(-1, self.tar_dim)
return x
class Compressor(nn.Module):
def __init__(self, input_dim=4096, tar_dim=2048):
super(Compressor, self).__init__()
self.fc1 = nn.Linear(input_dim, tar_dim)
self.ln_fc1 = nn.LayerNorm(tar_dim)
self.fc2 = nn.Linear(tar_dim, tar_dim)
def forward(self, x):
x = torch.relu(self.ln_fc1(self.fc1(x)))
x = self.fc2(x)
return x
class TransEncoder(nn.Module):
def __init__(self, d_model, N, num_token, head_num, d_ff, latten_size, down_sample_block=3, dropout=0.1, last_norm=True):
super(TransEncoder, self).__init__()
self.N = N
if d_model==4096:
# for T5-XXL, first use MLP to compress into 1024
self.compressor = Compressor(input_dim=d_model, tar_dim=1024)
d_model = 1024
else:
self.compressor = None
self.layers = clones(EncoderLayer(MultiHeadAttentioin(d_model, head_num, dropout=dropout),
FeedForward(d_model, d_ff, dropout=dropout),
LayerNorm(d_model),
LayerNorm(d_model)), N)
self.reduction_layers = nn.ModuleList()
for _ in range(down_sample_block):
self.reduction_layers.append(
EncoderReductionLayer(MultiHeadAttentioin(d_model, head_num, dropout=dropout),
FeedForward(d_model, d_ff, dropout=dropout),
nn.Linear(d_model, d_model // 2),
LayerNorm(d_model),
LayerNorm(d_model)))
d_model = d_model // 2
if latten_size == 8192 or latten_size == 4096:
self.arc = 0
self.linear = nn.Linear(d_model*num_token, latten_size)
self.norm = LayerNorm(latten_size) if last_norm else None
else:
self.arc = 1
self.adaptor = Adaptor(d_model*num_token, latten_size)
def forward(self, x, mask):
mask = mask.unsqueeze(1)
if self.compressor is not None:
x = self.compressor(x)
for i, layer in enumerate(self.layers):
x = layer(x, mask)
for i, layer in enumerate(self.reduction_layers):
x = layer(x, mask)
if self.arc == 0:
x = self.linear(x.view(x.shape[0],-1))
x = self.norm(x) if self.norm else x
else:
x = self.adaptor(x.view(x.shape[0],-1))
return x
class EncoderLayer(nn.Module):
def __init__(self, attn, feed_forward, norm1, norm2, dropout=0.1):
super(EncoderLayer, self).__init__()
self.attn = attn
self.feed_forward = feed_forward
self.norm1, self.norm2 = norm1, norm2
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
def forward(self, x, mask):
# multihead attn & norm
a = self.attn(x, x, x, mask)
t = self.norm1(x + self.dropout1(a))
# feed forward & norm
z = self.feed_forward(t) # linear(dropout(act(linear(x)))))
y = self.norm2(t + self.dropout2(z))
return y
class EncoderReductionLayer(nn.Module):
def __init__(self, attn, feed_forward, reduction, norm1, norm2, dropout=0.1):
super(EncoderReductionLayer, self).__init__()
self.attn = attn
self.feed_forward = feed_forward
self.reduction = reduction
self.norm1, self.norm2 = norm1, norm2
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
def forward(self, x, mask):
# multihead attn & norm
a = self.attn(x, x, x, mask)
t = self.norm1(x + self.dropout1(a))
# feed forward & norm
z = self.feed_forward(t) # linear(dropout(act(linear(x)))))
y = self.norm2(t + self.dropout2(z))
# reduction
# y = self.reduction(y).view(x.shape[0], -1, x.shape[-1])
y = self.reduction(y)
return y
class MultiHeadAttentioin(nn.Module):
def __init__(self, d_model, head_num, dropout=0.1, d_v=None):
super(MultiHeadAttentioin, self).__init__()
assert d_model % head_num == 0, "d_model must be divisible by head_num"
self.d_model = d_model
self.head_num = head_num
self.d_k = d_model // head_num
self.d_v = self.d_k if d_v is None else d_v
# d_model = d_k * head_num
self.W_Q = nn.Linear(d_model, head_num * self.d_k)
self.W_K = nn.Linear(d_model, head_num * self.d_k)
self.W_V = nn.Linear(d_model, head_num * self.d_v)
self.W_O = nn.Linear(d_model, d_model)
self.dropout = nn.Dropout(dropout)
def scaled_dp_attn(self, query, key, value, mask=None):
assert self.d_k == query.shape[-1]
# scores: [batch_size, head_num, seq_len, seq_len]
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.d_k)
# if torch.isinf(scores).any():
# # to avoid leaking
# scores = torch.where(scores == float('-inf'), torch.tensor(-65504.0), scores)
# scores = torch.where(scores == float('inf'), torch.tensor(65504.0), scores)
if mask is not None:
assert mask.ndim == 3, "Mask shape {} doesn't seem right...".format(mask.shape)
mask = mask.unsqueeze(1)
try:
if scores.dtype == torch.float32:
scores = scores.masked_fill(mask == 0, -1e9)
else:
scores = scores.masked_fill(mask == 0, -1e4)
except RuntimeError:
print("- scores device: {}".format(scores.device))
print("- mask device: {}".format(mask.device))
# attn: [batch_size, head_num, seq_len, seq_len]
attn = F.softmax(scores, dim=-1)
attn = self.dropout(attn)
return torch.matmul(attn, value), attn
def forward(self, q, k, v, mask):
batch_size = q.shape[0]
query = self.W_Q(q).view(batch_size, -1, self.head_num, self.d_k).transpose(1, 2)
key = self.W_K(k).view(batch_size, -1, self.head_num, self.d_k).transpose(1, 2)
value = self.W_V(v).view(batch_size, -1, self.head_num, self.d_k).transpose(1, 2)
heads, attn = self.scaled_dp_attn(query, key, value, mask)
heads = heads.transpose(1, 2).contiguous().view(batch_size, -1,
self.head_num * self.d_k)
assert heads.shape[-1] == self.d_model and heads.shape[0] == batch_size
y = self.W_O(heads)
assert y.shape == q.shape
return y
class LayerNorm(nn.Module):
def __init__(self, layer_size, eps=1e-5):
super(LayerNorm, self).__init__()
self.g = nn.Parameter(torch.ones(layer_size))
self.b = nn.Parameter(torch.zeros(layer_size))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
x = (x - mean) / (std + self.eps)
return self.g * x + self.b
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff, dropout=0.1, act='relu', d_output=None):
super(FeedForward, self).__init__()
self.d_model = d_model
self.d_ff = d_ff
d_output = d_model if d_output is None else d_output
self.ffn_1 = nn.Linear(d_model, d_ff)
self.ffn_2 = nn.Linear(d_ff, d_output)
if act == 'relu':
self.act = nn.ReLU()
elif act == 'rrelu':
self.act = nn.RReLU()
else:
raise NotImplementedError
self.dropout = nn.Dropout(dropout)
def forward(self, x):
y = self.ffn_2(self.dropout(self.act(self.ffn_1(x))))
return y
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