ruff fix

modules/F0Predictor/fcpe/pcmer.py

@@ -275,13 +275,7 @@ class FastAttention(nn.Module):
         self.no_projection = no_projection
 
         self.causal = causal
-        if causal:
-            try:
-                import fast_transformers.causal_product.causal_product_cuda
-                self.causal_linear_fn = partial(causal_linear_attention)
-            except ImportError:
-                print('unable to import cuda code for auto-regressive Performer. will default to the memory inefficient non-cuda version')
-                self.causal_linear_fn = causal_linear_attention_noncuda
+
     @torch.no_grad()
     def redraw_projection_matrix(self):
         projections = self.create_projection()
@@ -294,11 +288,6 @@ class FastAttention(nn.Module):
         if self.no_projection:
             q = q.softmax(dim = -1)
             k = torch.exp(k) if self.causal else k.softmax(dim = -2)
-
-        elif self.generalized_attention:
-            create_kernel = partial(generalized_kernel, kernel_fn = self.kernel_fn, projection_matrix = self.projection_matrix, device = device)
-            q, k = map(create_kernel, (q, k))
-
         else:
             create_kernel = partial(softmax_kernel, projection_matrix = self.projection_matrix, device = device)
             
@@ -341,7 +330,7 @@ class SelfAttention(nn.Module):
         #torch.nn.init.zeros_(self.name_embedding)
         #print (torch.sum(self.name_embedding))
     def forward(self, x, context = None, mask = None, context_mask = None, name=None, inference=False, **kwargs):
-        b, n, _, h, gh = *x.shape, self.heads, self.global_heads
+        _, _, _, h, gh = *x.shape, self.heads, self.global_heads
         
         cross_attend = exists(context)