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Remove the use of random_clamp in conformer.py.

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This commit is contained in:
Daniel Povey 2022-10-20 19:54:38 +08:00
parent dccff6b893
commit c5cb52fed1
1 changed files with 0 additions and 12 deletions
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12
egs/librispeech/ASR/pruned_transducer_stateless7/conformer.py
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@ -940,7 +940,6 @@ class RelPositionMultiheadAttention(nn.Module):
training=self.training,
key_padding_mask=key_padding_mask,
attn_mask=attn_mask,
attn_weights_max=5.0 if self.training else None,
)
return x, weights
@ -959,7 +958,6 @@ class RelPositionMultiheadAttention(nn.Module):
training: bool = True,
key_padding_mask: Optional[Tensor] = None,
attn_mask: Optional[Tensor] = None,
attn_weights_max: Optional[float] = None,
) -> Tuple[Tensor, Optional[Tensor]]:
r"""
Args:
@ -1111,16 +1109,6 @@ class RelPositionMultiheadAttention(nn.Module):
# caution: they are really scores at this point.
attn_output_weights = torch.matmul(q, k) + pos_weights
# The following is a soft way of encouraging the attention scores to not be too large;
# in training time, once they get outside a certain range, -5.0..5.0 currently, we
# randomly either leave them as-is or truncate them to that range.
if attn_weights_max is not None:
attn_output_weights = random_clamp(attn_output_weights,
min=-attn_weights_max,
max=attn_weights_max,
prob=0.5,
reflect=0.1)
if training and random.random() < 0.1:
# This is a harder way of limiting the attention scores to not be too large.
# It incurs a penalty if any of them has an absolute value greater than 50.0.