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Add another balancer for NonlinAttentionModule.

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This commit is contained in:
Daniel Povey 2022-12-06 11:11:28 +08:00
parent 0da228c587
commit 8f841e5b2b
1 changed files with 16 additions and 2 deletions
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18
egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
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@ -1477,7 +1477,7 @@ class NonlinAttentionModule(nn.Module):
# because we noticed that well-trained instances of this module have abs-value before the sigmoid
# starting from about 3, and poorly-trained instances of the module have smaller abs values
# before the sigmoid.
self.balancer = ActivationBalancer(
self.balancer1 = ActivationBalancer(
hidden_channels // ratio, channel_dim=-1,
min_positive=ScheduledFloat((0.0, 0.1), (8000.0, 0.05)),
max_positive=1.0,
@ -1491,6 +1491,19 @@ class NonlinAttentionModule(nn.Module):
bias=True,
initial_scale=0.05)
# Have very tight limits on min_positive and max_positive so that it beomes
# close to zero mean, as we found that large mean offsets after the
# multiplication are associated with poor convergence.
# We don't need min_abs and max_abs limits because sharing the in_proj
# between the sigmoid-input and activations dictates the scale of the
# activations at this point. The code applies those anyway, it's not optional
# right now, so just use the default values.
self.balancer2 = ActivationBalancer(
hidden_channels // ratio, channel_dim=-1,
min_positive=0.4, max_positive=0.6,
min_abs=0.5,
)
self.whiten = Whiten(num_groups=1,
whitening_limit=_whitening_schedule(5.0),
prob=(0.025, 0.25),
@ -1518,7 +1531,7 @@ attn_weights: a Tensor of shape (num_heads, batch_size, seq_len, seq_len)
s = x[..., hidden_channels:]
x = x[..., :hidden_channels]
s = self.balancer(s)
s = self.balancer1(s)
s = self.tanh(s)
s = s.unsqueeze(-1).expand(-1, -1, -1, self.ratio).reshape(seq_len, batch_size,
@ -1536,6 +1549,7 @@ attn_weights: a Tensor of shape (num_heads, batch_size, seq_len, seq_len)
# now x: (num_heads, batch_size, seq_len, head_dim)
x = x.permute(2, 1, 0, 3).reshape(seq_len, batch_size, -1)
x = self.balancer2(x)
x = self.whiten(x)
x = self.out_proj(x)
return x