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Implement dropout for scores in AttentionDownsample

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This commit is contained in:
Daniel Povey 2022-12-10 16:09:51 +08:00
parent 2f617fec43
commit cb12014c31
1 changed files with 19 additions and 4 deletions
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23
egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
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@ -202,6 +202,7 @@ class Zipformer(EncoderInterface):
input_dim=encoder_dim[i-1] if i > 0 else encoder_dim[0], input_dim=encoder_dim[i-1] if i > 0 else encoder_dim[0],
output_dim=encoder_dim[i], output_dim=encoder_dim[i],
downsample=downsampling_factor[i], downsample=downsampling_factor[i],
dropout=dropout,
) )
encoders.append(encoder) encoders.append(encoder)
self.encoders = nn.ModuleList(encoders) self.encoders = nn.ModuleList(encoders)
@ -211,7 +212,8 @@ class Zipformer(EncoderInterface):
self.downsample_output = AttentionDownsample(encoder_dim[-1], self.downsample_output = AttentionDownsample(encoder_dim[-1],
encoder_dim[-1], encoder_dim[-1],
downsample=output_downsampling_factor) downsample=output_downsampling_factor,
dropout=dropout)
def _init_skip_modules(self): def _init_skip_modules(self):
@ -677,10 +679,12 @@ class DownsampledZipformerEncoder(nn.Module):
encoder: nn.Module, encoder: nn.Module,
input_dim: int, input_dim: int,
output_dim: int, output_dim: int,
downsample: int): downsample: int,
dropout: FloatLike):
super(DownsampledZipformerEncoder, self).__init__() super(DownsampledZipformerEncoder, self).__init__()
self.downsample_factor = downsample self.downsample_factor = downsample
self.downsample = AttentionDownsample(input_dim, output_dim, downsample) self.downsample = AttentionDownsample(input_dim, output_dim,
downsample, dropout)
self.encoder = encoder self.encoder = encoder
self.upsample = SimpleUpsample(output_dim, downsample) self.upsample = SimpleUpsample(output_dim, downsample)
self.out_combiner = SimpleCombiner(input_dim, self.out_combiner = SimpleCombiner(input_dim,
@ -794,7 +798,8 @@ class AttentionDownsample(torch.nn.Module):
def __init__(self, def __init__(self,
in_channels: int, in_channels: int,
out_channels: int, out_channels: int,
downsample: int): downsample: int,
dropout: FloatLike):
""" """
Require out_channels > in_channels. Require out_channels > in_channels.
""" """
@ -802,6 +807,7 @@ class AttentionDownsample(torch.nn.Module):
self.query = nn.Parameter(torch.randn(in_channels) * (in_channels ** -0.5)) self.query = nn.Parameter(torch.randn(in_channels) * (in_channels ** -0.5))
self.name = None # will be set from training code self.name = None # will be set from training code
self.dropout = copy.deepcopy(dropout)
# fill in the extra dimensions with a projection of the input # fill in the extra dimensions with a projection of the input
if out_channels > in_channels: if out_channels > in_channels:
@ -832,6 +838,7 @@ class AttentionDownsample(torch.nn.Module):
assert src.shape[0] == d_seq_len * ds assert src.shape[0] == d_seq_len * ds
src = src.reshape(d_seq_len, ds, batch_size, in_channels) src = src.reshape(d_seq_len, ds, batch_size, in_channels)
# scores: (d_seq_len, downsample, batch_size)
scores = (src * self.query).sum(dim=-1, keepdim=True) scores = (src * self.query).sum(dim=-1, keepdim=True)
scores = penalize_abs_values_gt(scores, scores = penalize_abs_values_gt(scores,
@ -839,6 +846,14 @@ class AttentionDownsample(torch.nn.Module):
penalty=1.0e-04, penalty=1.0e-04,
name=self.name) name=self.name)
dropout = float(self.dropout)
if dropout > 0.0:
# the 0:1, done on the axis of size 'downsample', selects just
# one dimension while keeping the dim. We'll then broadcast when
# we multiply.
dropout_mask = torch.rand_like(scores[:, 0:1]) > dropout
scores = scores * dropout_mask
weights = scores.softmax(dim=1) weights = scores.softmax(dim=1)
# ans1 is the first `in_channels` channels of the output # ans1 is the first `in_channels` channels of the output