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comment some lines, random combine from 1/3 layers, on linear layers in combiner

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yaozengwei 2022-06-17 15:20:05 +08:00
parent 2a47db653b
commit 0c455db55d
1 changed files with 25 additions and 16 deletions
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41
egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py
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@ -87,10 +87,17 @@ class Conformer(EncoderInterface):
layer_dropout,
cnn_module_kernel,
)
# aux_layers from 1/3
self.encoder = ConformerEncoder(
encoder_layer,
num_encoder_layers,
aux_layers=list(range(0, num_encoder_layers - 1, aux_layer_period)),
aux_layers=list(
range(
num_encoder_layers // 3,
num_encoder_layers - 1,
aux_layer_period,
)
),
)
def forward(
@ -296,10 +303,10 @@ class ConformerEncoder(nn.Module):
assert num_layers - 1 not in aux_layers
self.aux_layers = set(aux_layers + [num_layers - 1])
num_channels = encoder_layer.norm_final.num_channels
# num_channels = encoder_layer.norm_final.num_channels
self.combiner = RandomCombine(
num_inputs=len(self.aux_layers),
num_channels=num_channels,
# num_channels=num_channels,
final_weight=0.5,
pure_prob=0.333,
stddev=2.0,
@ -1073,7 +1080,7 @@ class RandomCombine(nn.Module):
def __init__(
self,
num_inputs: int,
num_channels: int,
# num_channels: int,
final_weight: float = 0.5,
pure_prob: float = 0.5,
stddev: float = 2.0,
@ -1116,12 +1123,12 @@ class RandomCombine(nn.Module):
assert 0 < final_weight < 1, final_weight
assert num_inputs >= 1
self.linear = nn.ModuleList(
[
nn.Linear(num_channels, num_channels, bias=True)
for _ in range(num_inputs - 1)
]
)
# self.linear = nn.ModuleList(
# [
# nn.Linear(num_channels, num_channels, bias=True)
# for _ in range(num_inputs - 1)
# ]
# )
self.num_inputs = num_inputs
self.final_weight = final_weight
@ -1135,12 +1142,13 @@ class RandomCombine(nn.Module):
.log()
.item()
)
self._reset_parameters()
def _reset_parameters(self):
for i in range(len(self.linear)):
nn.init.eye_(self.linear[i].weight)
nn.init.constant_(self.linear[i].bias, 0.0)
# self._reset_parameters()
# def _reset_parameters(self):
# for i in range(len(self.linear)):
# nn.init.eye_(self.linear[i].weight)
# nn.init.constant_(self.linear[i].bias, 0.0)
def forward(self, inputs: List[Tensor]) -> Tensor:
"""Forward function.
@ -1163,7 +1171,8 @@ class RandomCombine(nn.Module):
mod_inputs = []
for i in range(num_inputs - 1):
mod_inputs.append(self.linear[i](inputs[i]))
# mod_inputs.append(self.linear[i](inputs[i]))
mod_inputs.append(inputs[i])
mod_inputs.append(inputs[num_inputs - 1])
ndim = inputs[0].ndim