Pass args around to cleanup parameter lists

fairseq/models/transformer.py

@@ -89,44 +89,16 @@ class TransformerModel(FairseqModel):
             encoder_embed_tokens = build_embedding(src_dict, args.encoder_embed_dim)
             decoder_embed_tokens = build_embedding(dst_dict, args.decoder_embed_dim)
 
-        encoder = TransformerEncoder(
-            src_dict,
-            encoder_embed_tokens,
-            ffn_inner_dim=args.encoder_ffn_embed_dim,
-            num_layers=args.encoder_layers,
-            num_attn_heads=args.encoder_attention_heads,
-            dropout=args.dropout,
-            attention_dropout=args.attention_dropout,
-            relu_dropout=args.relu_dropout,
-            normalize_before=args.encoder_normalize_before,
-            learned_pos_embed=args.encoder_learned_pos,
-        )
-        decoder = TransformerDecoder(
-            dst_dict,
-            decoder_embed_tokens,
-            ffn_inner_dim=args.decoder_ffn_embed_dim,
-            num_layers=args.decoder_layers,
-            num_attn_heads=args.decoder_attention_heads,
-            dropout=args.dropout,
-            attention_dropout=args.attention_dropout,
-            relu_dropout=args.relu_dropout,
-            normalize_before=args.encoder_normalize_before,
-            learned_pos_embed=args.decoder_learned_pos,
-            share_input_output_embed=args.share_decoder_input_output_embed,
-        )
-
+        encoder = TransformerEncoder(args, src_dict, encoder_embed_tokens)
+        decoder = TransformerDecoder(args, dst_dict, decoder_embed_tokens)
         return TransformerModel(encoder, decoder)
 
 
 class TransformerEncoder(FairseqEncoder):
     """Transformer encoder."""
-    def __init__(
-        self, dictionary, embed_tokens, ffn_inner_dim=2048,
-        num_layers=6, num_attn_heads=8, dropout=0.1, attention_dropout=0.,
-        relu_dropout=0., normalize_before=False, learned_pos_embed=False,
-    ):
+    def __init__(self, args, dictionary, embed_tokens):
         super().__init__(dictionary)
-        self.dropout = dropout
+        self.dropout = args.dropout
 
         embed_dim = embed_tokens.embedding_dim
         self.padding_idx = embed_tokens.padding_idx
@@ -136,17 +108,13 @@ class TransformerEncoder(FairseqEncoder):
         self.embed_positions = PositionalEmbedding(
             1024, embed_dim, self.padding_idx,
             left_pad=LanguagePairDataset.LEFT_PAD_SOURCE,
-            learned=learned_pos_embed,
+            learned=args.encoder_learned_pos,
         )
 
         self.layers = nn.ModuleList([])
         self.layers.extend([
-            TransformerEncoderLayer(
-                embed_dim, ffn_inner_dim, num_attn_heads, dropout=dropout,
-                attention_dropout=attention_dropout, relu_dropout=relu_dropout,
-                normalize_before=normalize_before,
-            )
-            for i in range(num_layers)
+            TransformerEncoderLayer(args)
+            for i in range(args.encoder_layers)
         ])
 
         self.reset_parameters()
@@ -186,15 +154,10 @@ class TransformerEncoder(FairseqEncoder):
 
 class TransformerDecoder(FairseqDecoder):
     """Transformer decoder."""
-    def __init__(
-        self, dictionary, embed_tokens, ffn_inner_dim=2048,
-        num_layers=6, num_attn_heads=8, dropout=0.1, attention_dropout=0.,
-        relu_dropout=0., normalize_before=False, learned_pos_embed=False,
-        share_input_output_embed=False,
-    ):
+    def __init__(self, args, dictionary, embed_tokens):
         super().__init__(dictionary)
-        self.dropout = dropout
-        self.share_input_output_embed = share_input_output_embed
+        self.dropout = args.dropout
+        self.share_input_output_embed = args.share_decoder_input_output_embed
 
         embed_dim = embed_tokens.embedding_dim
         padding_idx = embed_tokens.padding_idx
@@ -204,20 +167,16 @@ class TransformerDecoder(FairseqDecoder):
         self.embed_positions = PositionalEmbedding(
             1024, embed_dim, padding_idx,
             left_pad=LanguagePairDataset.LEFT_PAD_TARGET,
-            learned=learned_pos_embed,
+            learned=args.decoder_learned_pos,
         )
 
         self.layers = nn.ModuleList([])
         self.layers.extend([
-            TransformerDecoderLayer(
-                embed_dim, ffn_inner_dim, num_attn_heads, dropout=dropout,
-                attention_dropout=attention_dropout, relu_dropout=relu_dropout,
-                normalize_before=normalize_before,
-            )
-            for i in range(num_layers)
+            TransformerDecoderLayer(args)
+            for i in range(args.decoder_layers)
         ])
 
-        if not share_input_output_embed:
+        if not self.share_input_output_embed:
             self.embed_out = nn.Parameter(torch.Tensor(len(dictionary), embed_dim))
 
         self.reset_parameters()
@@ -276,19 +235,19 @@ class TransformerEncoderLayer(nn.Module):
     We default to the approach in the paper, but the tensor2tensor approach can
     be enabled by setting `normalize_before=True`.
     """
-    def __init__(
-        self, embed_dim, ffn_inner_dim, num_attn_heads, dropout=0.1,
-        attention_dropout=0., relu_dropout=0., normalize_before=False,
-    ):
+    def __init__(self, args):
         super().__init__()
-        self.embed_dim = embed_dim
-        self.self_attn = MultiheadAttention(embed_dim, num_attn_heads, dropout=attention_dropout)
-        self.dropout = dropout
-        self.relu_dropout = relu_dropout
-        self.normalize_before = normalize_before
-        self.fc1 = nn.Linear(embed_dim, ffn_inner_dim)
-        self.fc2 = nn.Linear(ffn_inner_dim, embed_dim)
-        self.layer_norms = nn.ModuleList([LayerNorm(embed_dim) for i in range(2)])
+        self.embed_dim = args.encoder_embed_dim
+        self.self_attn = MultiheadAttention(
+            self.embed_dim, args.encoder_attention_heads,
+            dropout=args.attention_dropout,
+        )
+        self.dropout = args.dropout
+        self.relu_dropout = args.relu_dropout
+        self.normalize_before = args.encoder_normalize_before
+        self.fc1 = nn.Linear(self.embed_dim, args.encoder_ffn_embed_dim)
+        self.fc2 = nn.Linear(args.encoder_ffn_embed_dim, self.embed_dim)
+        self.layer_norms = nn.ModuleList([LayerNorm(self.embed_dim) for i in range(2)])
 
     def forward(self, x, encoder_padding_mask):
         residual = x
@@ -318,20 +277,23 @@ class TransformerEncoderLayer(nn.Module):
 
 class TransformerDecoderLayer(nn.Module):
     """Decoder layer block."""
-    def __init__(
-        self, embed_dim, ffn_inner_dim, num_attn_heads, dropout=0.1,
-        attention_dropout=0., relu_dropout=0., normalize_before=False,
-    ):
+    def __init__(self, args):
         super().__init__()
-        self.embed_dim = embed_dim
-        self.self_attn = MultiheadAttention(embed_dim, num_attn_heads, dropout=attention_dropout)
-        self.dropout = dropout
-        self.relu_dropout = relu_dropout
-        self.normalize_before = normalize_before
-        self.encoder_attn = MultiheadAttention(embed_dim, num_attn_heads, dropout=attention_dropout)
-        self.fc1 = nn.Linear(embed_dim, ffn_inner_dim)
-        self.fc2 = nn.Linear(ffn_inner_dim, embed_dim)
-        self.layer_norms = nn.ModuleList([LayerNorm(embed_dim) for i in range(3)])
+        self.embed_dim = args.decoder_embed_dim
+        self.self_attn = MultiheadAttention(
+            self.embed_dim, args.decoder_attention_heads,
+            dropout=args.attention_dropout,
+        )
+        self.dropout = args.dropout
+        self.relu_dropout = args.relu_dropout
+        self.normalize_before = args.encoder_normalize_before
+        self.encoder_attn = MultiheadAttention(
+            self.embed_dim, args.decoder_attention_heads,
+            dropout=args.attention_dropout,
+        )
+        self.fc1 = nn.Linear(self.embed_dim, args.decoder_ffn_embed_dim)
+        self.fc2 = nn.Linear(args.decoder_ffn_embed_dim, self.embed_dim)
+        self.layer_norms = nn.ModuleList([LayerNorm(self.embed_dim) for i in range(3)])
 
     def forward(self, x, encoder_out, encoder_padding_mask):
         residual = x