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Improve pos_embed calculation #20

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Improve pos_embed calculation #20

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Improve pos_embed calculation
ZhiyuanChen Nov 8, 2021
b5b1e0b
fix bug of using *= in tensor operation
ZhiyuanChen Nov 8, 2021
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13 changes: 5 additions & 8 deletions fairseq/modules/transformer_sentence_encoder.py
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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)
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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