add lora version of ActivationDropouAndLinear; currently a simple version

egs/librispeech/ASR/zipformer_lora/scaling.py

@@ -1740,6 +1740,45 @@ class ActivationDropoutAndLinear(torch.nn.Module):
             self.dropout_shared_dim,
         )
 
+class ActivationDropoutAndLinear_lora(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        bias: bool = True,
+        activation: str = "SwooshL",
+        dropout_p: FloatLike = 0.0,
+        dropout_shared_dim: Optional[int] = -1,
+        r: int=0,
+        lora_alpha: int=1,
+        lora_dropout: float=0.0,
+        initial_scale: float = 1.0,
+    ):
+        super().__init__()
+        # create a temporary module of nn.Linear that we'll steal the
+        # weights and bias from
+        self.l = ScaledLinear_lora(
+            in_features=in_channels,
+            out_features=out_channels,
+            r=r,
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+            initial_scale=initial_scale,
+            bias=bias,
+        )
+
+        self.activation = activation
+        self.dropout = Dropout3(dropout_p, dropout_shared_dim)
+
+    def forward(self, x: Tensor):
+        if self.activation == "SwooshL":
+            x = SwooshLForward(x)
+        elif self.activation == "SwooshR":
+            x = SwooshRForward(x)
+        else:
+            assert False, self.activation
+        return self.dropout(self.l(x))
+
 
 def convert_num_channels(x: Tensor, num_channels: int) -> Tensor:
     if num_channels <= x.shape[-1]:

egs/librispeech/ASR/zipformer_lora/zipformer.py

@@ -34,6 +34,7 @@ from scaling import (
 )
 from scaling import (
     ActivationDropoutAndLinear,
+    ActivationDropoutAndLinear_lora,
     Balancer,
     BiasNorm,
     ChunkCausalDepthwiseConv1d,
@@ -2066,7 +2067,6 @@ class FeedforwardModule(nn.Module):
         lora_dropout: float=0.0
     ):
         super(FeedforwardModule, self).__init__()
-        # self.in_proj = nn.Linear(embed_dim, feedforward_dim)
         self.in_proj = ScaledLinear_lora(
             in_features=embed_dim,
             out_features=feedforward_dim,
@@ -2086,13 +2086,16 @@ class FeedforwardModule(nn.Module):
         )
 
         # shared_dim=0 means we share the dropout mask along the time axis
-        self.out_proj = ActivationDropoutAndLinear(
+        self.out_proj = ActivationDropoutAndLinear_lora(
             feedforward_dim,
             embed_dim,
             activation="SwooshL",
             dropout_p=dropout,
             dropout_shared_dim=0,
             bias=True,
+            r=lora_r,
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
             initial_scale=0.1,
         )