enforce monotonic attention in forward attention y for batches

layers/common_layers.py

@@ -203,16 +203,13 @@ class Attention(nn.Module):
         alpha = (((1 - self.u) * self.alpha.clone().to(inputs.device) +
                   self.u * prev_alpha) + 1e-8) * alignment
         # force incremental alignment - TODO: make configurable
-        if not self.training and alignment.shape[0] == 1:
+        if not self.training:
             _, n = prev_alpha.max(1)
             val, n2 = alpha.max(1)
             for b in range(alignment.shape[0]):
                 alpha[b, n + 2:] = 0
-                alpha[b, :(
-                    n - 1
-                )] = 0  # ignore all previous states to prevent repetition.
-                alpha[b, (
-                    n - 2)] = 0.01 * val  # smoothing factor for the prev step
+                alpha[b, :(n - 1)] = 0  # ignore all previous states to prevent repetition.
+                alpha[b, (n - 2)] = 0.01 * val  # smoothing factor for the prev step
         # compute attention weights
         self.alpha = alpha / alpha.sum(dim=1).unsqueeze(1)
         # compute context