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Make weight in SimpleCombine a vector

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This commit is contained in:
Daniel Povey 2022-11-02 11:07:57 +08:00
parent e08f5c1bce
commit 0379ab57a2
1 changed files with 4 additions and 13 deletions
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17
egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
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@ -341,11 +341,6 @@ class ZipformerEncoderLayer(nn.Module):
feedforward_dim, feedforward_dim,
dropout) dropout)
self.feed_forward3 = FeedforwardModule(d_model,
feedforward_dim,
dropout)
self.conv_module1 = ConvolutionModule(d_model, self.conv_module1 = ConvolutionModule(d_model,
cnn_module_kernel) cnn_module_kernel)
@ -451,16 +446,13 @@ class ZipformerEncoderLayer(nn.Module):
if torch.jit.is_scripting() or random.random() > dynamic_dropout: if torch.jit.is_scripting() or random.random() > dynamic_dropout:
src = src + self.conv_module1(src, src_key_padding_mask=src_key_padding_mask) src = src + self.conv_module1(src, src_key_padding_mask=src_key_padding_mask)
src = src + self.feed_forward2(src)
if torch.jit.is_scripting() or use_self_attn: if torch.jit.is_scripting() or use_self_attn:
src = src + self.self_attn.forward2(src, attn_weights) src = src + self.self_attn.forward2(src, attn_weights)
if torch.jit.is_scripting() or random.random() > dynamic_dropout: if torch.jit.is_scripting() or random.random() > dynamic_dropout:
src = src + self.conv_module2(src, src_key_padding_mask=src_key_padding_mask) src = src + self.conv_module2(src, src_key_padding_mask=src_key_padding_mask)
src = src + self.feed_forward3(src) src = src + self.feed_forward2(src)
src = self.norm_final(self.balancer(src)) src = self.norm_final(self.balancer(src))
@ -855,7 +847,7 @@ class SimpleCombiner(torch.nn.Module):
min_weight: Tuple[float] = (0., 0.)): min_weight: Tuple[float] = (0., 0.)):
super(SimpleCombiner, self).__init__() super(SimpleCombiner, self).__init__()
assert dim2 >= dim1 assert dim2 >= dim1
self.weight1 = nn.Parameter(torch.zeros(())) self.weight1 = nn.Parameter(torch.ones(dim2) * min_weight[0])
self.min_weight = min_weight self.min_weight = min_weight
def forward(self, def forward(self,
@ -878,9 +870,6 @@ class SimpleCombiner(torch.nn.Module):
max=1.0-self.min_weight[1]) max=1.0-self.min_weight[1])
src1 = src1 * weight1
src2 = src2 * (1.0 - weight1)
src1_dim = src1.shape[-1] src1_dim = src1.shape[-1]
src2_dim = src2.shape[-1] src2_dim = src2.shape[-1]
if src1_dim != src2_dim: if src1_dim != src2_dim:
@ -893,6 +882,8 @@ class SimpleCombiner(torch.nn.Module):
else: else:
src1 = src1[:src2_dim] src1 = src1[:src2_dim]
src1 = src1 * weight1
src2 = src2 * (1.0 - weight1)
return src1 + src2 return src1 + src2