incorporate `pytorch` and `fast` architecture into sampling pipeline

modules/architecture/fast_transformer.py

@@ -114,3 +114,7 @@ class FastTransformer(nn.Module):
 
         # return logits
         return self.head[0](output_seq, *seq)  # [N, L, V]
+
+    def compute_logits(self, seq_layer, memory, idx, cls):
+        """ Alias for 'forward' to make this module compatible to old sampling pipeline. """
+        return self.forward([seq_layer], cls)

modules/architecture/pytorch_transformer.py

@@ -117,3 +117,7 @@ class PytorchTransformer(nn.Module):
 
         # return logits
         return self.head[0](output_seq, *seq)  # [N, L, V]
+
+    def compute_logits(self, seq_layer, memory, idx, cls):
+        """ Alias for 'forward' to make this module compatible to old sampling pipeline. """
+        return self.forward([seq_layer], cls)

sample/layer_sampler/sampler_factory.py

@@ -36,7 +36,7 @@ def create_sampler(
 
     if architecture == "autoencoder":
         return AutoencoderSampler(**kwargs)
-    elif architecture == "encoder_only":
+    elif architecture in ("encoder_only", "fast", "pytorch"):
         return EncoderOnlySampler(**kwargs)
     elif architecture == "encoder_decoder":
         return EncoderDecoderSampler(**kwargs)