incorporate some einops suggestions from @arogozhnikov

conformer/conformer.py

@@ -3,6 +3,7 @@
 import torch.nn.functional as F
 
 from einops import rearrange
+from einops.layers.torch import Rearrange
 
 # helper functions
 
@@ -22,15 +23,6 @@ class Swish(nn.Module):
     def forward(self, x):
         return x * x.sigmoid()
 
-class Transpose(nn.Module):
-    def __init__(self, dims):
-        super().__init__()
-        assert len(dims) == 2, 'dims must be a tuple of two dimensions'
-        self.dims = dims
-
-    def forward(self, x):
-        return x.transpose(*self.dims)
-
 class GLU(nn.Module):
     def __init__(self, dim):
         super().__init__()
@@ -104,7 +96,7 @@ def forward(self, x, context = None, mask = None, context_mask = None):
 
         # shaw's relative positional embedding
         seq = torch.arange(n, device = device)
-        dist = seq[:, None] - seq[None, :]
+        dist = rearrange(seq, 'i -> i ()') - rearrange(seq, 'j -> () j')
         dist = dist.clip(-max_pos_emb, max_pos_emb) + max_pos_emb
         rel_pos_emb = self.rel_pos_emb(dist).to(q)
         pos_attn = einsum('b h n d, n r d -> b h n r', q, rel_pos_emb) * self.scale
@@ -114,7 +106,7 @@ def forward(self, x, context = None, mask = None, context_mask = None):
             mask = default(mask, lambda: torch.ones(*x.shape[:2], device = device))
             context_mask = default(context_mask, mask) if not has_context else default(context_mask, lambda: torch.ones(*context.shape[:2], device = device))
             mask_value = -torch.finfo(dots.dtype).max
-            mask = mask[:, None, :, None] * context_mask[:, None, None, :]
+            mask = rearrange(mask, 'b i -> b () i ()') * rearrange(context_mask, 'b j -> b () () j')
             dots.masked_fill_(~mask, mask_value)
 
         attn = dots.softmax(dim = -1)
@@ -158,14 +150,14 @@ def __init__(
 
         self.net = nn.Sequential(
             nn.LayerNorm(dim),
-            Transpose((1, 2)),
+            Rearrange('b n c -> b c n'),
             nn.Conv1d(dim, inner_dim * 2, 1),
             GLU(dim=1),
             DepthWiseConv1d(inner_dim, inner_dim, kernel_size = kernel_size, padding = padding),
             nn.BatchNorm1d(inner_dim) if not causal else nn.Identity(),
             Swish(),
             nn.Conv1d(inner_dim, dim, 1),
-            Transpose((1, 2)),
+            Rearrange('b c n -> b n c'),
             nn.Dropout(dropout)
         )