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# --------------------------------------------------------
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Modified by RainbowSecret from:
# https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/activation.py#L852
# --------------------------------------------------------
import copy
import math
import warnings
import torch
import torch.nn.functional as F
from torch import nn, Tensor
from torch.nn.modules.module import Module
from torch._jit_internal import Optional, Tuple
from torch.overrides import has_torch_function, handle_torch_function
from torch.nn.functional import linear, pad, softmax, dropout
class MultiheadAttention(Module):
bias_k: Optional[torch.Tensor]
bias_v: Optional[torch.Tensor]
def __init__(
self,
embed_dim,
num_heads,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
kdim=None,
vdim=None,
):
super(MultiheadAttention, self).__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == 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.k_proj = nn.Linear(self.kdim, embed_dim, bias=bias)
self.v_proj = nn.Linear(self.vdim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim)
self.in_proj_bias = None
self.in_proj_weight = None
self.bias_k = self.bias_v = None
self.q_proj_weight = None
self.k_proj_weight = None
self.v_proj_weight = None
self.add_zero_attn = add_zero_attn
def __setstate__(self, state):
# Support loading old MultiheadAttention checkpoints generated by v1.1.0
if "_qkv_same_embed_dim" not in state:
state["_qkv_same_embed_dim"] = True
super(MultiheadAttention, self).__setstate__(state)
def forward(
self,
query,
key,
value,
key_padding_mask=None,
need_weights=False,
attn_mask=None,
residual_attn=None,
):
if not self._qkv_same_embed_dim:
return self.multi_head_attention_forward(
query,
key,
value,
self.embed_dim,
self.num_heads,
self.in_proj_weight,
self.in_proj_bias,
self.bias_k,
self.bias_v,
self.add_zero_attn,
self.dropout,
self.out_proj.weight,
self.out_proj.bias,
training=self.training,
key_padding_mask=key_padding_mask,
need_weights=need_weights,
attn_mask=attn_mask,
use_separate_proj_weight=True,
q_proj_weight=self.q_proj_weight,
k_proj_weight=self.k_proj_weight,
v_proj_weight=self.v_proj_weight,
out_dim=self.vdim,
residual_attn=residual_attn,
)
else:
return self.multi_head_attention_forward(
query,
key,
value,
self.embed_dim,
self.num_heads,
self.in_proj_weight,
self.in_proj_bias,
self.bias_k,
self.bias_v,
self.add_zero_attn,
self.dropout,
self.out_proj.weight,
self.out_proj.bias,
training=self.training,
key_padding_mask=key_padding_mask,
need_weights=need_weights,
attn_mask=attn_mask,
out_dim=self.vdim,
residual_attn=residual_attn,
)
def multi_head_attention_forward(
self,
query: Tensor,
key: Tensor,
value: Tensor,
embed_dim_to_check: int,
num_heads: int,
in_proj_weight: Tensor,
in_proj_bias: Tensor,
bias_k: Optional[Tensor],
bias_v: Optional[Tensor],
add_zero_attn: bool,
dropout_p: float,
out_proj_weight: Tensor,
out_proj_bias: Tensor,
training: bool = True,
key_padding_mask: Optional[Tensor] = None,
need_weights: bool = False,
attn_mask: Optional[Tensor] = None,
use_separate_proj_weight: bool = False,
q_proj_weight: Optional[Tensor] = None,
k_proj_weight: Optional[Tensor] = None,
v_proj_weight: Optional[Tensor] = None,
static_k: Optional[Tensor] = None,
static_v: Optional[Tensor] = None,
out_dim: Optional[Tensor] = None,
residual_attn: Optional[Tensor] = None,
) -> Tuple[Tensor, Optional[Tensor]]:
if not torch.jit.is_scripting():
tens_ops = (
query,
key,
value,
in_proj_weight,
in_proj_bias,
bias_k,
bias_v,
out_proj_weight,
out_proj_bias,
)
if any([type(t) is not Tensor for t in tens_ops]) and has_torch_function(
tens_ops
):
return handle_torch_function(
multi_head_attention_forward,
tens_ops,
query,
key,
value,
embed_dim_to_check,
num_heads,
in_proj_weight,
in_proj_bias,
bias_k,
bias_v,
add_zero_attn,
dropout_p,
out_proj_weight,
out_proj_bias,
training=training,
key_padding_mask=key_padding_mask,
need_weights=need_weights,
attn_mask=attn_mask,
use_separate_proj_weight=use_separate_proj_weight,
q_proj_weight=q_proj_weight,
k_proj_weight=k_proj_weight,
v_proj_weight=v_proj_weight,
static_k=static_k,
static_v=static_v,
)
tgt_len, bsz, embed_dim = query.size()
key = query if key is None else key
value = query if value is None else value
assert embed_dim == embed_dim_to_check
# allow MHA to have different sizes for the feature dimension
assert key.size(0) == value.size(0) and key.size(1) == value.size(1)
head_dim = embed_dim // num_heads
v_head_dim = out_dim // num_heads
assert (
head_dim * num_heads == embed_dim
), "embed_dim must be divisible by num_heads"
scaling = float(head_dim) ** -0.5
q = self.q_proj(query) * scaling
k = self.k_proj(key)
v = self.v_proj(value)
if attn_mask is not None:
assert (
attn_mask.dtype == torch.float32
or attn_mask.dtype == torch.float64
or attn_mask.dtype == torch.float16
or attn_mask.dtype == torch.uint8
or attn_mask.dtype == torch.bool
), "Only float, byte, and bool types are supported for attn_mask, not {}".format(
attn_mask.dtype
)
if attn_mask.dtype == torch.uint8:
warnings.warn(
"Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead."
)
attn_mask = attn_mask.to(torch.bool)
if attn_mask.dim() == 2:
attn_mask = attn_mask.unsqueeze(0)
if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
raise RuntimeError("The size of the 2D attn_mask is not correct.")
elif attn_mask.dim() == 3:
if list(attn_mask.size()) != [
bsz * num_heads,
query.size(0),
key.size(0),
]:
raise RuntimeError("The size of the 3D attn_mask is not correct.")
else:
raise RuntimeError(
"attn_mask's dimension {} is not supported".format(attn_mask.dim())
)
# convert ByteTensor key_padding_mask to bool
if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
warnings.warn(
"Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead."
)
key_padding_mask = key_padding_mask.to(torch.bool)
q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
if k is not None:
k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
if v is not None:
v = v.contiguous().view(-1, bsz * num_heads, v_head_dim).transpose(0, 1)
src_len = k.size(1)
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if add_zero_attn:
src_len += 1
k = torch.cat(
[
k,
torch.zeros(
(k.size(0), 1) + k.size()[2:], dtype=k.dtype, device=k.device
),
],
dim=1,
)
v = torch.cat(
[
v,
torch.zeros(
(v.size(0), 1) + v.size()[2:], dtype=v.dtype, device=v.device
),
],
dim=1,
)
if attn_mask is not None:
attn_mask = pad(attn_mask, (0, 1))
if key_padding_mask is not None:
key_padding_mask = pad(key_padding_mask, (0, 1))
attn_output_weights = torch.bmm(q, k.transpose(1, 2))
assert list(attn_output_weights.size()) == [bsz * num_heads, tgt_len, src_len]
"""
Attention weight for the invalid region is -inf
"""
if attn_mask is not None:
if attn_mask.dtype == torch.bool:
attn_output_weights.masked_fill_(attn_mask, float("-inf"))
else:
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = attn_output_weights.view(
bsz, num_heads, tgt_len, src_len
)
attn_output_weights = attn_output_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2),
float("-inf"),
)
attn_output_weights = attn_output_weights.view(
bsz * num_heads, tgt_len, src_len
)
if residual_attn is not None:
attn_output_weights = attn_output_weights.view(
bsz, num_heads, tgt_len, src_len
)
attn_output_weights += residual_attn.unsqueeze(0)
attn_output_weights = attn_output_weights.view(
bsz * num_heads, tgt_len, src_len
)
"""
Reweight the attention map before softmax().
attn_output_weights: (b*n_head, n, hw)
"""
attn_output_weights = softmax(attn_output_weights, dim=-1)
attn_output_weights = dropout(
attn_output_weights, p=dropout_p, training=training
)
attn_output = torch.bmm(attn_output_weights, v)
assert list(attn_output.size()) == [bsz * num_heads, tgt_len, v_head_dim]
attn_output = (
attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, out_dim)
)
attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
if need_weights:
# average attention weights over heads
attn_output_weights = attn_output_weights.view(
bsz, num_heads, tgt_len, src_len
)
return attn_output, attn_output_weights.sum(dim=1) / num_heads
else:
return attn_output
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