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Remove one feedforward module and give params to the other 2.

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Daniel Povey 2022-11-15 13:57:36 +08:00
parent 3d47335ab6
commit fc74ff63fb
2 changed files with 3 additions and 10 deletions
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2
egs/librispeech/ASR/pruned_transducer_stateless7/train.py
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@ -122,7 +122,7 @@ def add_model_arguments(parser: argparse.ArgumentParser):
parser.add_argument( parser.add_argument(
"--feedforward-dim", "--feedforward-dim",
type=str, type=str,
default="1024,1024,1536,1024,1024,1024", default="1536,1536,2048,1536,1536,1536",
help="Feedforward dimension of the zipformer encoder layers, per stack, comma separated.", help="Feedforward dimension of the zipformer encoder layers, per stack, comma separated.",
) )

11
egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
View File

@ -400,10 +400,6 @@ class ZipformerEncoderLayer(nn.Module):
feedforward_dim, feedforward_dim,
dropout) dropout)
self.feed_forward3 = FeedforwardModule(embed_dim,
feedforward_dim,
dropout)
#self.conv_module1 = ConvolutionModule(embed_dim, #self.conv_module1 = ConvolutionModule(embed_dim,
#cnn_module_kernel) #cnn_module_kernel)
self.nonlin_attention_module = NonlinAttentionModule(embed_dim) self.nonlin_attention_module = NonlinAttentionModule(embed_dim)
@ -469,9 +465,6 @@ class ZipformerEncoderLayer(nn.Module):
src_orig = src src_orig = src
# macaron style feed forward module
src = src + self.feed_forward1(src)
# dropout rate for non-feedforward submodules # dropout rate for non-feedforward submodules
dynamic_skip_prob = float(self.dynamic_skip_prob) if self.training else 0.0 dynamic_skip_prob = float(self.dynamic_skip_prob) if self.training else 0.0
# multi-headed self-attention module # multi-headed self-attention module
@ -490,7 +483,7 @@ class ZipformerEncoderLayer(nn.Module):
src = src + self.nonlin_attention_module(src, src = src + self.nonlin_attention_module(src,
attn_weights[0:1]) attn_weights[0:1])
src = src + self.feed_forward2(src) src = src + self.feed_forward1(src)
# pooling module # pooling module
if torch.jit.is_scripting() or use_self_attn: if torch.jit.is_scripting() or use_self_attn:
@ -503,7 +496,7 @@ class ZipformerEncoderLayer(nn.Module):
if torch.jit.is_scripting() or random.random() >= dynamic_skip_prob: if torch.jit.is_scripting() or random.random() >= dynamic_skip_prob:
src = src + self.conv_module(src, src_key_padding_mask=src_key_padding_mask) src = src + self.conv_module(src, src_key_padding_mask=src_key_padding_mask)
src = src + self.feed_forward3(src) src = src + self.feed_forward2(src)
# pooling module # pooling module
if torch.jit.is_scripting() or use_self_attn: if torch.jit.is_scripting() or use_self_attn: