Always smaller soft

no need to have half-size option as behavior can be reproduced with existing flags

fairseq/models/fconv.py

@@ -115,8 +115,6 @@ 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-half-size', action='store_true',
-                            help='if set, halves the dimensionality of adaptive softmax (as in original impl)')
         parser.add_argument('--decoder-attention', type=str, metavar='EXPR',
                             help='decoder attention [True, ...]')
         parser.add_argument('--normalization-constant', type=float, metavar='D',
@@ -145,7 +143,6 @@ class FConvLanguageModel(FairseqLanguageModel):
                 options.eval_str_list(args.adaptive_softmax_cutoff, type=int)
                 if args.criterion == 'adaptive_loss' else None
             ),
-            adaptive_softmax_half_size=args.adaptive_softmax_half_size,
             normalization_constant=args.normalization_constant,
         )
         return FConvLanguageModel(decoder)
@@ -347,7 +344,7 @@ 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, adaptive_softmax_half_size=False, normalization_constant=0.5,
+            adaptive_softmax_cutoff=None, normalization_constant=0.5,
             left_pad=False,
     ):
         super().__init__(dictionary)
@@ -409,7 +406,7 @@ 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, half_size=adaptive_softmax_half_size)
+                                                    dropout=dropout)
         else:
             self.fc2 = Linear(in_channels, out_embed_dim)
             if share_embed:
@@ -616,7 +613,6 @@ def base_lm_architecture(args):
     args.decoder_attention = getattr(args, 'decoder_attention', 'False')
     args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
     args.normalization_constant = getattr(args, 'normalization_constant', 0.5)
-    args.adaptive_softmax_half_size = getattr(args, 'adaptive_softmax_half_size', False)
 
 
 @register_model_architecture('fconv_lm', 'fconv_lm_dauphin_wikitext103')

fairseq/models/transformer.py

@@ -75,8 +75,6 @@ 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-half-size', action='store_true',
-                            help='if set, halves the dimensionality of adaptive softmax (as in original impl)')
 
     @classmethod
     def build_model(cls, args, task):
@@ -156,8 +154,6 @@ 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-half-size', action='store_true',
-                            help='if set, halves the dimensionality of adaptive softmax (as in original impl)')
         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',
@@ -314,7 +310,6 @@ class TransformerDecoder(FairseqIncrementalDecoder):
                 len(dictionary), args.decoder_embed_dim,
                 options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
                 dropout=args.dropout,
-                half_size=args.adaptive_softmax_half_size,
             )
         elif not self.share_input_output_embed:
             self.embed_out = nn.Parameter(torch.Tensor(len(dictionary), embed_dim))
@@ -579,7 +574,8 @@ def base_lm_architecture(args):
     args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
     args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
     args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
-    args.adaptive_softmax_half_size = getattr(args, 'adaptive_softmax_half_size', False)
+
+    args.character_embeddings = getattr(args, 'character_embeddings', False)
 
     # The model training is not stable without this
     args.decoder_normalize_before = True
@@ -627,7 +623,6 @@ 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_half_size = getattr(args, 'adaptive_softmax_half_size', False)
     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)

fairseq/modules/adaptive_softmax.py

@@ -18,7 +18,7 @@ class AdaptiveSoftmax(nn.Module):
     approximation for GPUs" (http://arxiv.org/abs/1609.04309).
     """
 
-    def __init__(self, vocab_size, input_dim, cutoff, dropout, half_size=False):
+    def __init__(self, vocab_size, input_dim, cutoff, dropout):
         super().__init__()
 
         if vocab_size > cutoff[-1]:
@@ -32,21 +32,11 @@ class AdaptiveSoftmax(nn.Module):
         self.vocab_size = vocab_size
         self.cutoff = cutoff
         self.dropout = dropout
+        self.input_dim = input_dim
 
         self.lsm = nn.LogSoftmax(dim=1)
         self.head = nn.Linear(input_dim, output_dim, bias=False)
-        self.tail = nn.ModuleList()
-
-        extra_denom = 1 if half_size else 0
-
-        for i in range(len(cutoff) - 1):
-            self.tail.append(
-                nn.Sequential(
-                    nn.Linear(input_dim, input_dim // 4 ** (i + extra_denom), bias=False),
-                    nn.Dropout(dropout),
-                    nn.Linear(input_dim // 4 ** (i + extra_denom), cutoff[i + 1] - cutoff[i], bias=False)
-                )
-            )
+        self._make_tail(True)
 
         def init_weights(m):
             if hasattr(m, 'weight'):
@@ -58,10 +48,24 @@ class AdaptiveSoftmax(nn.Module):
         # versions prior to 1 had a bug that offset indices on the head by 1
         self.buggy_offset = 0
 
+    def _make_tail(self, fix_exponent):
+        extra_denom = 1 if fix_exponent else 0
+
+        self.tail = nn.ModuleList()
+        for i in range(len(self.cutoff) - 1):
+            self.tail.append(
+                nn.Sequential(
+                    nn.Linear(self.input_dim, self.input_dim // 4 ** (i + extra_denom), bias=False),
+                    nn.Dropout(self.dropout),
+                    nn.Linear(self.input_dim // 4 ** (i + extra_denom), self.cutoff[i + 1] - self.cutoff[i], bias=False)
+                )
+            )
+
     def upgrade_state_dict_named(self, state_dict, name):
         version_name = name + '.version'
         if version_name not in state_dict:
             self.buggy_offset = 1
+            self._make_tail(False)
             state_dict[version_name] = torch.LongTensor([1])
 
     def adapt_target(self, target):