make adaptive softmax dropout an optional arg

fairseq/models/fconv.py

@@ -115,6 +115,8 @@ class FConvLanguageModel(FairseqLanguageModel):
         parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
                             help='comma separated list of adaptive softmax cutoff points. '
                                  'Must be used with adaptive_loss criterion')
+        parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
+                            help='sets adaptive softmax dropout for the tail projections')
         parser.add_argument('--decoder-attention', type=str, metavar='EXPR',
                             help='decoder attention [True, ...]')
         parser.add_argument('--normalization-constant', type=float, metavar='D',
@@ -143,6 +145,7 @@ class FConvLanguageModel(FairseqLanguageModel):
                 options.eval_str_list(args.adaptive_softmax_cutoff, type=int)
                 if args.criterion == 'adaptive_loss' else None
             ),
+            adaptive_softmax_dropout=args.adaptive_softmax_dropout,
             normalization_constant=args.normalization_constant,
         )
         return FConvLanguageModel(decoder)
@@ -344,7 +347,7 @@ class FConvDecoder(FairseqIncrementalDecoder):
             self, dictionary, embed_dim=512, embed_dict=None, out_embed_dim=256,
             max_positions=1024, convolutions=((512, 3),) * 20, attention=True,
             dropout=0.1, share_embed=False, positional_embeddings=True,
-            adaptive_softmax_cutoff=None, normalization_constant=0.5,
+            adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, normalization_constant=0.5,
             left_pad=False,
     ):
         super().__init__(dictionary)
@@ -406,7 +409,7 @@ class FConvDecoder(FairseqIncrementalDecoder):
         if adaptive_softmax_cutoff is not None:
             assert not share_embed
             self.adaptive_softmax = AdaptiveSoftmax(num_embeddings, in_channels, adaptive_softmax_cutoff,
-                                                    dropout=dropout)
+                                                    dropout=adaptive_softmax_dropout)
         else:
             self.fc2 = Linear(in_channels, out_embed_dim)
             if share_embed:
@@ -612,6 +615,7 @@ def base_lm_architecture(args):
     args.decoder_layers = getattr(args, 'decoder_layers', '[(1268, 4)] * 13')
     args.decoder_attention = getattr(args, 'decoder_attention', 'False')
     args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
+    args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
     args.normalization_constant = getattr(args, 'normalization_constant', 0.5)
 
 

fairseq/models/transformer.py

@@ -75,6 +75,8 @@ class TransformerModel(FairseqModel):
         parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
                             help='comma separated list of adaptive softmax cutoff points. '
                                  'Must be used with adaptive_loss criterion'),
+        parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
+                            help='sets adaptive softmax dropout for the tail projections')
 
     @classmethod
     def build_model(cls, args, task):
@@ -154,6 +156,8 @@ class TransformerLanguageModel(FairseqLanguageModel):
         parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
                             help='comma separated list of adaptive softmax cutoff points. '
                                  'Must be used with adaptive_loss criterion')
+        parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
+                            help='sets adaptive softmax dropout for the tail projections')
         parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true',
                             help='if set, disables positional embeddings (outside self attention)')
         parser.add_argument('--share-decoder-input-output-embed', default=False, action='store_true',
@@ -309,7 +313,7 @@ class TransformerDecoder(FairseqIncrementalDecoder):
             self.adaptive_softmax = AdaptiveSoftmax(
                 len(dictionary), args.decoder_embed_dim,
                 options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
-                dropout=args.dropout,
+                dropout=args.adaptive_softmax_dropout,
             )
         elif not self.share_input_output_embed:
             self.embed_out = nn.Parameter(torch.Tensor(len(dictionary), embed_dim))
@@ -573,6 +577,7 @@ def base_lm_architecture(args):
     args.decoder_layers = getattr(args, 'decoder_layers', 6)
     args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
     args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
+    args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
     args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
 
     args.character_embeddings = getattr(args, 'character_embeddings', False)
@@ -623,6 +628,7 @@ def base_architecture(args):
     args.relu_dropout = getattr(args, 'relu_dropout', 0.)
     args.dropout = getattr(args, 'dropout', 0.1)
     args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
+    args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
     args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
     args.share_all_embeddings = getattr(args, 'share_all_embeddings', False)
     args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)