Rebase against main

Author: pearu

src/torchaudio/functional/filtering.py

@@ -957,18 +957,19 @@ def backward(ctx, dy):
         n_channel = x.size(1)
         n_order = b_coeffs.size(1)
 
-        db = F.conv1d(
-            F.pad(x, (n_order - 1, 0)).view(1, n_batch * n_channel, -1),
-            dy.view(n_batch * n_channel, 1, -1),
-            groups=n_batch * n_channel
-        ).view(
-            n_batch, n_channel, -1
-        ).sum(0).flip(1) if b_coeffs.requires_grad else None
-        dx = F.conv1d(
-            F.pad(dy, (0, n_order - 1)),
-            b_coeffs.unsqueeze(1),
-            groups=n_channel
-        ) if x.requires_grad else None
+        db = (
+            F.conv1d(
+                F.pad(x, (n_order - 1, 0)).view(1, n_batch * n_channel, -1),
+                dy.view(n_batch * n_channel, 1, -1),
+                groups=n_batch * n_channel,
+            )
+            .view(n_batch, n_channel, -1)
+            .sum(0)
+            .flip(1)
+            if b_coeffs.requires_grad
+            else None
+        )
+        dx = F.conv1d(F.pad(dy, (0, n_order - 1)), b_coeffs.unsqueeze(1), groups=n_channel) if x.requires_grad else None
         return (dx, db)
 
     @staticmethod

src/torchaudio/functional/functional.py

@@ -1735,33 +1735,14 @@ def backward(ctx, dy):
         return (result, None, None, None, None, None, None, None)
 
     @staticmethod
-    def ts_apply(
-            logits,
-            targets,
-            logit_lengths,
-            target_lengths,
-            blank: int,
-            clamp: float,
-            fused_log_softmax: bool):
+    def ts_apply(logits, targets, logit_lengths, target_lengths, blank: int, clamp: float, fused_log_softmax: bool):
         if torch.jit.is_scripting():
             output, saved = torch.ops.torchaudio.rnnt_loss_forward(
-                logits,
-                targets,
-                logit_lengths,
-                target_lengths,
-                blank,
-                clamp,
-                fused_log_softmax)
+                logits, targets, logit_lengths, target_lengths, blank, clamp, fused_log_softmax
+            )
             return output
         else:
-            return RnntLoss.apply(
-                logits,
-                targets,
-                logit_lengths,
-                target_lengths,
-                blank,
-                clamp,
-                fused_log_softmax)
+            return RnntLoss.apply(logits, targets, logit_lengths, target_lengths, blank, clamp, fused_log_softmax)
 
 
 def _rnnt_loss(