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Remove whitening in SelfAttention module.

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This commit is contained in:
Daniel Povey 2022-11-22 20:01:09 +08:00
parent 19683aa516
commit 1a2632d0a2
1 changed files with 0 additions and 10 deletions
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10
egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
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@ -1211,15 +1211,6 @@ class SelfAttention(nn.Module):
num_heads * value_head_dim,
bias=True)
# attempt to make the output of `in_proj` uncorrelated within each head
# and all heads having roughly the same magnitude. the hope is to
# improve learning dynamics; this loses no power as there is no constraint
# on the condition number of out_proj.
self.whiten_values = Whiten(num_groups=num_heads,
whitening_limit=2.0,
prob=(0.025, 0.25),
grad_scale=0.025)
self.out_proj = ScaledLinear(num_heads * value_head_dim,
embed_dim, bias=True,
initial_scale=0.05)
@ -1252,7 +1243,6 @@ class SelfAttention(nn.Module):
assert attn_weights.shape == (num_heads, batch_size, seq_len, seq_len)
x = self.in_proj(x) # (seq_len, batch_size, num_heads * value_head_dim)
x = self.whiten_values(x) # does nothing in the forward pass.
x = x.reshape(seq_len, batch_size, num_heads, -1).permute(2, 1, 0, 3)
# now x: (num_heads, batch_size, seq_len, value_head_dim)
value_head_dim = x.shape[-1]