import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from utils import gelu, LayerNorm, get_incremental_state, set_incremental_state
import math
class TransformerLayer(nn.Module):
def __init__(self, embed_dim, ff_embed_dim, num_heads, dropout, with_external=False, weights_dropout = True):
super(TransformerLayer, self).__init__()
self.self_attn = MultiheadAttention(embed_dim, num_heads, dropout, weights_dropout)
self.fc1 = nn.Linear(embed_dim, ff_embed_dim)
self.fc2 = nn.Linear(ff_embed_dim, embed_dim)
self.attn_layer_norm = LayerNorm(embed_dim)
self.ff_layer_norm = LayerNorm(embed_dim)
self.with_external = with_external
self.dropout = dropout
if self.with_external:
self.external_attn = MultiheadAttention(embed_dim, num_heads, dropout, weights_dropout)
self.external_layer_norm = LayerNorm(embed_dim)
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.fc1.weight, std=0.02)
nn.init.normal_(self.fc2.weight, std=0.02)
nn.init.constant_(self.fc1.bias, 0.)
nn.init.constant_(self.fc2.bias, 0.)
def forward(self, x, kv = None,
self_padding_mask = None, self_attn_mask = None,
external_memories = None, external_padding_mask=None,
need_weights = False):
# x: seq_len x bsz x embed_dim
residual = x
if kv is None:
x, self_attn = self.self_attn(query=x, key=x, value=x, key_padding_mask=self_padding_mask, attn_mask=self_attn_mask, need_weights = need_weights)
else:
x, self_attn = self.self_attn(query=x, key=kv, value=kv, key_padding_mask=self_padding_mask, attn_mask=self_attn_mask, need_weights = need_weights)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.attn_layer_norm(residual + x)
if self.with_external:
residual = x
x, external_attn = self.external_attn(query=x, key=external_memories, value=external_memories, key_padding_mask=external_padding_mask, need_weights = need_weights)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.external_layer_norm(residual + x)
else:
external_attn = None
residual = x
x = gelu(self.fc1(x))
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.ff_layer_norm(residual + x)
return x, self_attn, external_attn
def work_incremental(self, x, self_padding_mask, self_attn_mask, incremental_state):
# x: seq_len x bsz x embed_dim
residual = x
x, self_attn = self.self_attn(query=x, key=x, value=x, key_padding_mask=self_padding_mask, attn_mask=self_attn_mask, incremental_state=incremental_state)
x = self.attn_layer_norm(residual + x)
residual = x
x = gelu(self.fc1(x))
x = self.fc2(x)
x = self.ff_layer_norm(residual + x)
return x, self_attn, None
class MultiheadAttention(nn.Module):
def __init__(self, embed_dim, num_heads, dropout=0., weights_dropout=True):
super(MultiheadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True)
self.weights_dropout = weights_dropout
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.in_proj_weight, std=0.02)
nn.init.normal_(self.out_proj.weight, std=0.02)
nn.init.constant_(self.in_proj_bias, 0.)
nn.init.constant_(self.out_proj.bias, 0.)
def forward(self, query, key, value, key_padding_mask=None, attn_mask=None, need_weights=False, incremental_state = None):
""" Input shape: Time x Batch x Channel
key_padding_mask: Time x batch
attn_mask: tgt_len x src_len
"""
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
bidx = self._get_bidx(incremental_state)
else:
saved_state = None
bidx = None
qkv_same = query.data_ptr() == key.data_ptr() == value.data_ptr()
kv_same = key.data_ptr() == value.data_ptr()
tgt_len, bsz, embed_dim = query.size()
assert key.size() == value.size()
if qkv_same:
# self-attention
q, k, v = self.in_proj_qkv(query)
elif kv_same:
# encoder-decoder attention
q = self.in_proj_q(query)
k, v = self.in_proj_kv(key)
else:
q = self.in_proj_q(query)
k = self.in_proj_k(key)
v = self.in_proj_v(value)
q *= self.scaling
q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if saved_state is not None:
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
if 'prev_key' in saved_state:
prev_key = saved_state['prev_key']
if bidx is not None:
prev_key = prev_key[bidx]
prev_key = prev_key.contiguous().view(bsz * self.num_heads, -1, self.head_dim)
k = torch.cat((prev_key, k), dim=1)
if 'prev_value' in saved_state:
prev_value = saved_state['prev_value']
if bidx is not None:
prev_value = prev_value[bidx]
prev_value = prev_value.contiguous().view(bsz * self.num_heads, -1, self.head_dim)
v = torch.cat((prev_value, v), dim=1)
saved_state['prev_key'] = k.view(bsz, self.num_heads, -1, self.head_dim)
saved_state['prev_value'] = v.view(bsz, self.num_heads, -1, self.head_dim)
self._set_input_buffer(incremental_state, saved_state)
src_len = k.size(1)
# k,v: bsz*heads x src_len x dim
# q: bsz*heads x tgt_len x dim
attn_weights = torch.bmm(q, k.transpose(1, 2))
assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
if attn_mask is not None:
attn_weights.masked_fill_(
attn_mask.unsqueeze(0),
float('-inf')
)
if key_padding_mask is not None:
# don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights.masked_fill_(
key_padding_mask.transpose(0, 1).unsqueeze(1).unsqueeze(2),
float('-inf')
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = F.softmax(attn_weights, dim=-1)
if self.weights_dropout:
attn_weights = F.dropout(attn_weights, p=self.dropout, training=self.training)
attn = torch.bmm(attn_weights, v)
if not self.weights_dropout:
attn = F.dropout(attn, p=self.dropout, training=self.training)
assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn = self.out_proj(attn)
if need_weights:
# maximum attention weight over heads
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights, _ = attn_weights.max(dim=1)
attn_weights = attn_weights.transpose(0, 1)
else:
attn_weights = None
return attn, attn_weights
def in_proj_qkv(self, query):
return self._in_proj(query).chunk(3, dim=-1)
def in_proj_kv(self, key):
return self._in_proj(key, start=self.embed_dim).chunk(2, dim=-1)
def in_proj_q(self, query):
return self._in_proj(query, end=self.embed_dim)
def in_proj_k(self, key):
return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)
def in_proj_v(self, value):
return self._in_proj(value, start=2 * self.embed_dim)
def _in_proj(self, input, start=0, end=None):
weight = self.in_proj_weight
bias = self.in_proj_bias
weight = weight[start:end, :]
if bias is not None:
bias = bias[start:end]
return F.linear(input, weight, bias)
def _get_input_buffer(self, incremental_state):
return get_incremental_state(
self,
incremental_state,
'attn_state',
) or {}
def _set_input_buffer(self, incremental_state, buffer):
set_incremental_state(
self,
incremental_state,
'attn_state',
buffer,)
def _get_bidx(self, incremental_state):
if "bidx" in incremental_state:
return incremental_state["bidx"]
else:
return None
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, std=0.02)
nn.init.constant_(m.weight[padding_idx], 0)
return m
class SelfAttentionMask(nn.Module):
def __init__(self, init_size = 100, device = 0):
super(SelfAttentionMask, self).__init__()
self.weights = SelfAttentionMask.get_mask(init_size)
self.device = device
@staticmethod
def get_mask(size):
weights = torch.triu(torch.ones((size, size), dtype = torch.bool), 1)
return weights
def forward(self, size):
if self.weights is None or size > self.weights.size(0):
self.weights = SelfAttentionMask.get_mask(size)
res = self.weights[:size,:size].to(self.device).detach()
return res
class LearnedPositionalEmbedding(nn.Module):
"""This module produces LearnedPositionalEmbedding.
"""
def __init__(self, embedding_dim, init_size=1024, device=0):
super(LearnedPositionalEmbedding, self).__init__()
self.weights = nn.Embedding(init_size, embedding_dim)
self.device= device
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.weights.weight, std=0.02)
def forward(self, input, offset=0):
"""Input is expected to be of size [seq_len x bsz]."""
seq_len, bsz = input.size()
positions = (offset + torch.arange(seq_len)).to(self.device)
res = self.weights(positions).unsqueeze(1).expand(-1, bsz, -1)
return res
class SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length.
"""
def __init__(self, embedding_dim, init_size=1024, device=0):
super(SinusoidalPositionalEmbedding, self).__init__()
self.embedding_dim = embedding_dim
self.weights = SinusoidalPositionalEmbedding.get_embedding(
init_size,
embedding_dim
)
self.device= device
@staticmethod
def get_embedding(num_embeddings, embedding_dim):
"""Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
return emb
def forward(self, input, offset=0):
"""Input is expected to be of size [seq_len x bsz]."""
seq_len, bsz = input.size()
mx_position = seq_len + offset
if self.weights is None or mx_position > self.weights.size(0):
# recompute/expand embeddings if needed
self.weights = SinusoidalPositionalEmbedding.get_embedding(
mx_position,
self.embedding_dim,
)
positions = offset + torch.arange(seq_len)
res = self.weights.index_select(0, positions).unsqueeze(1).expand(-1, bsz, -1).to(self.device).detach()
return res
