Skip to content

Improve pos_embed calculation #20

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
ZhiyuanChen wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Improve pos_embed calculation #20

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
000ded0
Improve pos_embed calculation
ZhiyuanChen Nov 8, 2021
b5b1e0b
fix bug of using *= in tensor operation
ZhiyuanChen Nov 8, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 5 additions & 8 deletions fairseq/modules/transformer_sentence_encoder.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@ def relative_position_bucket(relative_position, bidirectional=True, num_buckets=
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))

ret += torch.where(is_small, n, val_if_large)
return ret
return nn.Parameter(ret, requires_grad=False)

class TransformerSentenceEncoder(nn.Module):
"""
Expand Down Expand Up @@ -127,8 +127,7 @@ def __init__(
self.attn_scale_factor = 2
self.num_attention_heads = num_attention_heads
self.pos = nn.Embedding(self.max_seq_len + 1, self.embedding_dim)
self.pos_q_linear = nn.Linear(self.embedding_dim, self.embedding_dim)
self.pos_k_linear = nn.Linear(self.embedding_dim, self.embedding_dim)
self.pos_proj = nn.Linear(self.embedding_dim, self.embedding_dim * 2)
Copy link
Owner

@guolinke guolinke Nov 12, 2021

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

this will break backward compatibility.

self.pos_scaling = float(self.embedding_dim / num_attention_heads * self.attn_scale_factor) ** -0.5
self.pos_ln = LayerNorm(self.embedding_dim, export=export)
self.layers = nn.ModuleList(
Expand Down Expand Up @@ -185,11 +184,9 @@ def __init__(

def get_rel_pos_bias(self, x):
# Assume the input is ordered. If your input token is permuted, you may need to update this accordingly
if self.rp_bucket.device != x.device:
self.rp_bucket = self.rp_bucket.to(x.device)
seq_len = x.size(1)
rp_bucket = self.rp_bucket[:seq_len, :seq_len]
values = F.embedding(rp_bucket, self.relative_attention_bias.weight)
values = self.relative_attention_bias(rp_bucket)
values = values.permute([2, 0, 1])
return values.contiguous()

Expand Down Expand Up @@ -227,8 +224,8 @@ def forward(
# 0 is for other-to-cls 1 is for cls-to-other
# Assume the input is ordered. If your input token is permuted, you may need to update this accordingly
weight = self.pos_ln(self.pos.weight[:seq_len + 1, :])
pos_q = self.pos_q_linear(weight).view(seq_len + 1, self.num_attention_heads, -1).transpose(0, 1) * self.pos_scaling
pos_k = self.pos_k_linear(weight).view(seq_len + 1, self.num_attention_heads, -1).transpose(0, 1)
pos_q, pos_k = self.pos_proj(weight).reshape(seq_len + 1, 2, self.num_attention_heads, -1).permute(1, 2, 0, 3)
pos_q = pos_q * self.pos_scaling
abs_pos_bias = torch.bmm(pos_q, pos_k.transpose(1, 2))
# p_0 \dot p_0 is cls to others
cls_2_other = abs_pos_bias[:, 0, 0]
Expand Down