Convert swish nonlinearities to ReLU

egs/librispeech/ASR/conformer_ctc/subsampling.py

@@ -49,15 +49,13 @@ class Conv2dSubsampling(nn.Module):
             ),
             DerivBalancer(channel_dim=1, threshold=0.05,
                           max_factor=0.025),
-            nn.ReLU(),
+            ExpScaleRelu(odim, 1, 1, speed=20.0),
-            ExpScale(odim, 1, 1, speed=20.0),
             nn.Conv2d(
                 in_channels=odim, out_channels=odim, kernel_size=3, stride=2
             ),
             DerivBalancer(channel_dim=1, threshold=0.05,
                           max_factor=0.025),
-            nn.ReLU(),
+            ExpScaleRelu(odim, 1, 1, speed=20.0),
-            ExpScale(odim, 1, 1, speed=20.0),
         )
         self.out = nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim)
         self.out_norm = nn.LayerNorm(odim, elementwise_affine=False)
@@ -253,6 +251,60 @@ class ExpScaleSwish(torch.nn.Module):
         # return x * (self.scale * self.speed).exp()
 
 
+def _exp_scale_relu(x: Tensor, scale: Tensor, speed: float) -> Tensor:
+    return (x * (scale * speed).exp()).relu()
+
+
+class ExpScaleReluFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor, scale: Tensor, speed: float) -> Tensor:
+        ctx.save_for_backward(x.detach(), scale.detach())
+        ctx.speed = speed
+        return _exp_scale_swish(x, scale, speed)
+
+    @staticmethod
+    def backward(ctx, y_grad: Tensor) -> Tensor:
+        x, scale = ctx.saved_tensors
+        x.requires_grad = True
+        scale.requires_grad = True
+        with torch.enable_grad():
+            y = _exp_scale_swish(x, scale, ctx.speed)
+            y.backward(gradient=y_grad)
+            return x.grad, scale.grad, None
+
+
+
+class ExpScaleReluFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor, scale: Tensor, speed: float) -> Tensor:
+        ctx.save_for_backward(x.detach(), scale.detach())
+        ctx.speed = speed
+        return _exp_scale_relu(x, scale, speed)
+
+    @staticmethod
+    def backward(ctx, y_grad: Tensor) -> Tensor:
+        x, scale = ctx.saved_tensors
+        x.requires_grad = True
+        scale.requires_grad = True
+        with torch.enable_grad():
+            y = _exp_scale_relu(x, scale, ctx.speed)
+            y.backward(gradient=y_grad)
+            return x.grad, scale.grad, None
+
+class ExpScaleRelu(torch.nn.Module):
+    # combines ExpScale and Relu.
+    # caution: need to specify name for speed, e.g. ExpScaleRelu(50, speed=4.0)
+    def __init__(self, *shape, speed: float = 1.0):
+        super(ExpScaleRelu, self).__init__()
+        self.scale = nn.Parameter(torch.zeros(*shape))
+        self.speed = speed
+
+    def forward(self, x: Tensor) -> Tensor:
+        return ExpScaleReluFunction.apply(x, self.scale, self.speed)
+    # return (x * torch.sigmoid(x)) * (self.scale * self.speed).exp()
+        # return x * (self.scale * self.speed).exp()
+
+
 
 
 class DerivBalancerFunction(torch.autograd.Function):
@@ -335,6 +387,23 @@ def _test_exp_scale_swish():
     y2.sum().backward()
     assert torch.allclose(x1.grad, x2.grad)
 
+def _test_exp_scale_relu():
+
+    x1 = torch.randn(50, 60).detach()
+    x2 = x1.detach()
+
+    m1 = ExpScaleRelu(50, 1, speed=4.0)
+    m2 = torch.nn.Sequential(nn.ReLU(), ExpScale(50, 1, speed=4.0))
+    x1.requires_grad = True
+    x2.requires_grad = True
+
+    y1 = m1(x1)
+    y2 = m2(x2)
+    assert torch.allclose(y1, y2)
+    y1.sum().backward()
+    y2.sum().backward()
+    assert torch.allclose(x1.grad, x2.grad)
+
 
 
 def _test_deriv_balancer():
@@ -360,3 +429,4 @@ def _test_deriv_balancer():
 if __name__ == '__main__':
     _test_deriv_balancer()
     _test_exp_scale_swish()
+    _test_exp_scale_relu()

egs/librispeech/ASR/transducer_stateless/conformer.py

@@ -19,7 +19,7 @@ import copy
 import math
 import warnings
 from typing import Optional, Tuple, Sequence
-from subsampling import PeLU, ExpScale, ExpScaleSwish, DerivBalancer
+from subsampling import PeLU, ExpScale, ExpScaleSwish, ExpScaleRelu, DerivBalancer
 
 import torch
 from torch import Tensor, nn
@@ -158,7 +158,7 @@ class ConformerEncoderLayer(nn.Module):
             nn.Linear(d_model, dim_feedforward),
             DerivBalancer(channel_dim=-1, threshold=0.05,
                           max_factor=0.025),
-            ExpScaleSwish(dim_feedforward, speed=20.0),
+            ExpScaleRelu(dim_feedforward, speed=20.0),
             nn.Dropout(dropout),
             nn.Linear(dim_feedforward, d_model),
         )
@@ -167,7 +167,7 @@ class ConformerEncoderLayer(nn.Module):
             nn.Linear(d_model, dim_feedforward),
             DerivBalancer(channel_dim=-1, threshold=0.05,
                           max_factor=0.025),
-            ExpScaleSwish(dim_feedforward, speed=20.0),
+            ExpScaleRelu(dim_feedforward, speed=20.0),
             nn.Dropout(dropout),
             nn.Linear(dim_feedforward, d_model),
         )
@@ -877,8 +877,10 @@ class ConvolutionModule(nn.Module):
             groups=channels,
             bias=bias,
         )
+        self.balancer = DerivBalancer(channel_dim=1, threshold=0.05,
+                                      max_factor=0.025)
         # shape: (channels, 1), broadcasts with (batch, channel, time).
-        self.activation = ExpScaleSwish(channels, 1, speed=20.0)
+        self.activation = ExpScaleRelu(channels, 1, speed=20.0)
 
         self.pointwise_conv2 = nn.Conv1d(
             channels,
@@ -910,6 +912,7 @@ class ConvolutionModule(nn.Module):
         x = self.depthwise_conv(x)
         # x is (batch, channels, time)
 
+        x = self.balancer(x)
         x = self.activation(x)
 
         x = self.pointwise_conv2(x)  # (batch, channel, time)

egs/librispeech/ASR/transducer_stateless/train.py

@@ -110,7 +110,7 @@ def get_parser():
     parser.add_argument(
         "--exp-dir",
         type=str,
-        default="transducer_stateless/specaugmod_baseline_randcombine1_expscale5_brelu2",
+        default="transducer_stateless/specaugmod_baseline_randcombine1_expscale5_brelu2relu",
         help="""The experiment dir.
         It specifies the directory where all training related
         files, e.g., checkpoints, log, etc, are saved