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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.modules.layer_norm import LayerNorm
from .adaptive_span_attention import AdaptiveSpan
# Size notations:
# B = batch_size, H = d_model, M = block_size, L = attn_span
def _skew(X, pad_value):
"""shift every row 1 step to right"""
# X = B x M x L
B, M, L = X.size()
X = F.pad(X, (0, M + 1), value=pad_value) # B x M x (L+M+1)
X = X.view(B, -1) # B x ML+MM+M
X = X[:, :-M] # B x ML+MM
X = X.view(B, M, M + L) # B x M x L+M
return X
def _unskew(X):
"""reverse _skew operation"""
# X = B x M x L+M
B, M, L = X.size()
L -= M
X = X.view(B, -1) # B x ML+MM
X = F.pad(X, (0, M)) # B x ML+MM+M
X = X.view(B, M, M + L + 1) # B x M x L+M+1
X = X[:, :, :L] # B x M x L
return X
class SeqAttention(nn.Module):
"""Sequential self-attention layer.
Each token will attend to its previous fixed number of steps.
Note that attention doesn't include the current step itself.
"""
def __init__(self, d_model, n_head, attn_span, dropout, adapt_span_layer, **kargs):
nn.Module.__init__(self)
self.dropout = nn.Dropout(dropout)
self.d_model = d_model # size of a single head
self.attn_span = attn_span
self.adaptive_span = AdaptiveSpan(
attn_span=attn_span,
n_head=n_head,
adapt_span_layer=adapt_span_layer,
**kargs
)
def forward(self, query, key, value, key_pe):
# query size = B x M x H
# key, value sizes = B x (M+L) x H
key, value, key_pe = self.adaptive_span.trim_memory(query, key, value, key_pe)
# compute attention from context
# B x M (dest) x (M+L) (src)
attn_cont = torch.matmul(query, key.transpose(-1, -2))
attn_cont = _unskew(attn_cont) # B x M x L
# compute the effect of position embedding
attn_pos = torch.matmul(query, key_pe) # B x M x L_pos
attn = attn_cont + attn_pos
attn = attn / math.sqrt(self.d_model) # B x M X L_pos
attn = F.softmax(attn.float(), dim=-1).type_as(attn)
# trim attention lengths according to the learned span
attn = self.adaptive_span(attn)
attn = self.dropout(attn) # B x M X L_pos
attn_cont = _skew(attn, 0) # B x M X (L+M)
out = torch.matmul(attn_cont, value) # B x M x H
return out
def get_cache_size(self):
return self.adaptive_span.get_cache_size()
class MultiHeadSeqAttention(nn.Module):
def __init__(self, d_model, n_head, **kargs):
nn.Module.__init__(self)
assert d_model % n_head == 0
self.n_head = n_head
self.head_dim = d_model // n_head
self.attn = SeqAttention(d_model=self.head_dim, n_head=n_head, **kargs)
self.proj_query = nn.Linear(d_model, d_model, bias=False)
nn.init.xavier_normal_(self.proj_query.weight)
self.proj_out = nn.Linear(d_model, d_model, bias=False)
nn.init.xavier_normal_(self.proj_out.weight)
self.proj_val = nn.Linear(d_model, d_model, bias=False)
nn.init.xavier_normal_(self.proj_val.weight)
self.proj_key = nn.Linear(d_model, d_model, bias=False)
nn.init.xavier_normal_(self.proj_key.weight)
def head_reshape(self, x):
K = self.n_head
D = self.head_dim
x = x.view(x.size()[:-1] + (K, D)) # B x (M+L) x K x D
x = x.transpose(1, 2).contiguous() # B x K x (M+L) x D
x = x.view(-1, x.size(-2), x.size(-1)) # B_K x (M+L) x D
return x
def forward(self, query, key, value, key_pe):
B = query.size(0)
K = self.n_head
D = self.head_dim
M = query.size(1)
query = self.proj_query(query)
query = self.head_reshape(query)
value = self.proj_val(value)
value = self.head_reshape(value)
key = self.proj_key(key)
key = self.head_reshape(key)
out = self.attn(query, key, value, key_pe) # B_K x M x D
out = out.view(B, K, M, D) # B x K x M x D
out = out.transpose(1, 2).contiguous() # B x M x K x D
out = out.view(B, M, -1) # B x M x K_D
out = self.proj_out(out)
return out
class FeedForwardLayer(nn.Module):
def __init__(self, d_model, d_inner, dropout, **kargs):
nn.Module.__init__(self)
self.fc1 = nn.Linear(d_model, d_inner)
self.fc2 = nn.Linear(d_inner, d_model)
nn.init.xavier_uniform_(self.fc1.weight)
nn.init.xavier_uniform_(self.fc2.weight)
self.dropout = nn.Dropout(dropout)
def forward(self, h):
h1 = F.relu(self.fc1(h))
h1 = self.dropout(h1)
h2 = self.fc2(h1)
return h2
class TransformerSeqLayer(nn.Module):
def __init__(self, d_model, **kargs):
nn.Module.__init__(self)
self.attn = MultiHeadSeqAttention(d_model=d_model, **kargs)
self.norm1 = LayerNorm(d_model)
self.ff = FeedForwardLayer(d_model=d_model, **kargs)
self.norm2 = LayerNorm(d_model)
def forward(self, h, h_cache, key_pe):
# h = B x M x H
# h_cache = B x L x H
h_all = torch.cat([h_cache, h], dim=1) # B x (M+L) x H
attn_out = self.attn(h, h_all, h_all, key_pe)
h = self.norm1(h + attn_out) # B x M x H
if self.ff is not None:
ff_out = self.ff(h)
out = self.norm2(h + ff_out) # B x M x H
else:
out = h
return out
def get_cache_size(self):
return self.attn.attn.get_cache_size()
class TransformerSeq(nn.Module):
def __init__(
self,
vocab_size,
d_model,
n_head,
n_layer,
attn_span,
emb_dropout,
aux_loss_scaler,
adapt_span_layer,
**kargs
):
nn.Module.__init__(self)
# token embeddings
self.in_emb = nn.Embedding(vocab_size, d_model)
nn.init.normal_(self.in_emb.weight, mean=0, std=d_model ** -0.5)
self.out_emb = nn.Linear(d_model, vocab_size)
self.aux_loss_scaler = aux_loss_scaler
if emb_dropout > 0:
self.emb_dropout = nn.Dropout(emb_dropout)
else:
self.emb_dropout = None
# position embeddings
self.key_pe = nn.Parameter(torch.randn(1, d_model // n_head, attn_span))
self.layers = nn.ModuleList()
self.layers.extend(
TransformerSeqLayer(
d_model=d_model,
n_head=n_head,
attn_span=attn_span,
adapt_span_layer=adapt_span_layer,
**kargs
)
for _ in range(n_layer)
)
def forward(self, x, h_cache, target=None):
# x size = B x M
block_size = x.size(1)
h = self.in_emb(x) # B x M x H
if self.emb_dropout is not None:
h = self.emb_dropout(h)
h_cache_next = []
for l, layer in enumerate(self.layers):
cache_size = layer.attn.attn.get_cache_size()
if cache_size > block_size:
h_cache_next_l = torch.cat(
[h_cache[l][:, -cache_size + block_size :, :], h], dim=1
).detach()
else:
h_cache_next_l = h[:, -cache_size:, :].detach()
h_cache_next.append(h_cache_next_l)
h = layer(h, h_cache[l], self.key_pe) # B x M x H
if self.emb_dropout is not None:
h = self.emb_dropout(h)
out = F.log_softmax(self.out_emb(h).float(), dim=-1).type_as(h)
dummy_loss = None
return out, h_cache_next, dummy_loss
def get_aux_loss(self):
loss = 0.0
for layer in self.layers:
loss += layer.attn.attn.adaptive_span.get_loss()
return self.aux_loss_scaler * loss
def get_current_max_span(self):
max_span = 0.0
for layer in self.layers:
max_span = max(
max_span, layer.attn.attn.adaptive_span.get_current_max_span()
)
return max_span
def get_current_avg_span(self):
avg_span = 0.0
for layer in self.layers:
avg_span += layer.attn.attn.adaptive_span.get_current_avg_span()
return avg_span / len(self.layers)
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