File size: 6,263 Bytes
3440f83 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
import math
import torch
import torch.nn.functional as F
from torch import nn
try:
from apex.normalization import FusedLayerNorm as LayerNorm
except ModuleNotFoundError:
from torch.nn import LayerNorm
from .multiway_network import MultiwayWrapper
from .xpos_relative_position import XPOS
class MultiheadAttention(nn.Module):
def __init__(
self,
args,
embed_dim,
num_heads,
dropout=0.0,
self_attention=False,
encoder_decoder_attention=False,
subln=False,
one_attn=False,
):
super().__init__()
self.args = args
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.scaling = self.head_dim ** (-0.5)
self.self_attention = self_attention
self.encoder_decoder_attention = encoder_decoder_attention
assert self.self_attention ^ self.encoder_decoder_attention
if one_attn:
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=True)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=True)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=True)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True)
else:
self.k_proj = MultiwayWrapper(args, nn.Linear(embed_dim, embed_dim, bias=True))
self.v_proj = MultiwayWrapper(args, nn.Linear(embed_dim, embed_dim, bias=True))
self.q_proj = MultiwayWrapper(args, nn.Linear(embed_dim, embed_dim, bias=True))
# self.qkv_proj = MultiwayWrapper(args, nn.Linear(embed_dim, embed_dim*3, bias=True))
self.out_proj = MultiwayWrapper(args, nn.Linear(embed_dim, embed_dim, bias=True))
self.inner_attn_ln = (
MultiwayWrapper(args, LayerNorm(self.embed_dim, eps=args.layernorm_eps))
if subln and self.self_attention
else None
)
self.dropout_module = torch.nn.Dropout(dropout)
self.xpos = XPOS(self.head_dim, args.xpos_scale_base) if args.xpos_rel_pos and self.self_attention else None
def reset_parameters(self):
nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.out_proj.weight)
nn.init.constant_(self.out_proj.bias, 0.0)
def forward(
self,
query,
key,
value,
incremental_state=None,
key_padding_mask=None,
attn_mask=None,
rel_pos=None,
):
bsz, tgt_len, embed_dim = query.size()
src_len = tgt_len
assert embed_dim == self.embed_dim, f"query dim {embed_dim} != {self.embed_dim}"
key_bsz, src_len, _ = key.size()
assert key_bsz == bsz, f"{query.size(), key.size()}"
assert value is not None
assert bsz, src_len == value.shape[:2]
# if query is key and key is value:
# qkv = self.qkv_proj(query)
# else:
# # W*(q+k+v) = W(q) + W(k) + W(v)
# qkv = self.qkv_proj(query+key+value)
# q,k,v = qkv.split(self.embed_dim, dim=-1)
q = self.q_proj(query)
k = self.k_proj(key)
v = self.v_proj(value)
q = (q * self.scaling).view(bsz, tgt_len, self.num_heads, self.head_dim).transpose(1, 2)
k = k.view(bsz, src_len, self.num_heads, self.head_dim).transpose(1, 2)
v = v.view(bsz, src_len, self.num_heads, self.head_dim).transpose(1, 2)
q = q.reshape(bsz * self.num_heads, tgt_len, self.head_dim)
k = k.reshape(bsz * self.num_heads, src_len, self.head_dim)
v = v.reshape(bsz * self.num_heads, src_len, self.head_dim)
if incremental_state is not None:
if "prev_key" in incremental_state:
prev_key = incremental_state["prev_key"].view(bsz * self.num_heads, -1, self.head_dim)
prev_value = incremental_state["prev_value"].view(bsz * self.num_heads, -1, self.head_dim)
k = torch.cat([prev_key, k], dim=1)
v = torch.cat([prev_value, v], dim=1)
incremental_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
incremental_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
src_len = k.size(1)
if self.xpos is not None:
if incremental_state is not None:
offset = src_len - 1
else:
offset = 0
k = self.xpos(k, offset=0, downscale=True)
q = self.xpos(q, offset=offset, downscale=False)
attn_weights = torch.bmm(q, k.transpose(1, 2))
if attn_mask is not None:
attn_weights = torch.nan_to_num(attn_weights)
if len(attn_mask.shape) != len(attn_weights.shape):
attn_mask = attn_mask.unsqueeze(0)
else:
attn_mask = attn_mask.repeat_interleave(self.num_heads, dim=0)
attn_weights += attn_mask
if key_padding_mask is not None:
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
float("-inf"),
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if rel_pos is not None:
rel_pos = rel_pos.view(attn_weights.size())
attn_weights = attn_weights + rel_pos
attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).type_as(attn_weights)
attn_probs = self.dropout_module(attn_weights)
attn = torch.bmm(attn_probs, v)
attn = attn.transpose(0, 1).reshape(tgt_len, bsz, embed_dim).transpose(0, 1)
if self.inner_attn_ln is not None:
attn = self.inner_attn_ln(attn)
attn = self.out_proj(attn)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
return attn, attn_weights
|