add the local attention

.gitignore

@@ -135,3 +135,4 @@ experimental/*
 # Weights and Biases logs
 # Weights and Biases logs
 wandb/
 wandb/
 /examples/translation/iwslt14.tokenized.de-en/
 /examples/translation/iwslt14.tokenized.de-en/
+toy/

fairseq/models/speech_to_text/s2t_transformer.py

@@ -150,6 +150,7 @@ class S2TTransformerModel(FairseqEncoderDecoderModel):
                 "selfattn",
                 "selfattn",
                 "rel_selfattn",
                 "rel_selfattn",
                 "relative",
                 "relative",
+                "local",
             ],
             ],
             help="transformer encoder self-attention layer type"
             help="transformer encoder self-attention layer type"
         )
         )
@@ -187,6 +188,7 @@ class S2TTransformerModel(FairseqEncoderDecoderModel):
                 "selfattn",
                 "selfattn",
                 "rel_selfattn",
                 "rel_selfattn",
                 "relative",
                 "relative",
+                "local",
             ],
             ],
             help="transformer decoder self-attention layer type"
             help="transformer decoder self-attention layer type"
         )
         )
@@ -277,6 +279,29 @@ class S2TTransformerModel(FairseqEncoderDecoderModel):
             default="learnable_dense",
             default="learnable_dense",
             help='decoder layer history type'
             help='decoder layer history type'
         )
         )
+
+        parser.add_argument(
+            '--hard-mask-window',
+            type=float,
+            metavar="D",
+            default=0,
+            help='window size of local mask'
+        )
+        parser.add_argument(
+            '--gauss-mask-sigma',
+            type=float,
+            metavar="D",
+            default=0,
+            help='standard deviation of the gauss mask'
+        )
+        parser.add_argument(
+            '--init-mask-weight',
+            type=float,
+            metavar="D",
+            default=0.5,
+            help='initialized weight for local mask'
+        )
+
         pass
         pass
 
 
     @classmethod
     @classmethod
@@ -627,6 +652,10 @@ def base_architecture(args):
     args.max_decoder_relative_length = getattr(args, 'max_decoder_relative_length', -1)
     args.max_decoder_relative_length = getattr(args, 'max_decoder_relative_length', -1)
     args.k_only = getattr(args, 'k_only', True)
     args.k_only = getattr(args, 'k_only', True)
 
 
+    args.hard_mask_window = getattr(args, 'hard_mask_window', 0)
+    args.gauss_mask_sigma = getattr(args, 'gauss_mask_sigma', 0)
+    args.init_mask_weight = getattr(args, 'init_mask_weight', 0)
+
 
 
 @register_model_architecture("s2t_transformer", "s2t_transformer_s")
 @register_model_architecture("s2t_transformer", "s2t_transformer_s")
 def s2t_transformer_s(args):
 def s2t_transformer_s(args):

fairseq/modules/__init__.py

@@ -26,6 +26,7 @@ from .layer_norm import Fp32LayerNorm, LayerNorm
 from .learned_positional_embedding import LearnedPositionalEmbedding
 from .learned_positional_embedding import LearnedPositionalEmbedding
 from .lightweight_convolution import LightweightConv, LightweightConv1dTBC
 from .lightweight_convolution import LightweightConv, LightweightConv1dTBC
 from .linearized_convolution import LinearizedConvolution
 from .linearized_convolution import LinearizedConvolution
+from .local_multihead_attention import LocalMultiheadAttention
 from .multihead_attention import MultiheadAttention
 from .multihead_attention import MultiheadAttention
 from .positional_embedding import PositionalEmbedding
 from .positional_embedding import PositionalEmbedding
 from .rel_position_multihead_attention import RelPositionMultiheadAttention
 from .rel_position_multihead_attention import RelPositionMultiheadAttention
@@ -70,6 +71,7 @@ __all__ = [
     "LightweightConv1dTBC",
     "LightweightConv1dTBC",
     "LightweightConv",
     "LightweightConv",
     "LinearizedConvolution",
     "LinearizedConvolution",
+    "LocalMultiheadAttention",
     "MultiheadAttention",
     "MultiheadAttention",
     "PositionalEmbedding",
     "PositionalEmbedding",
     "RelPositionMultiheadAttention",
     "RelPositionMultiheadAttention",

fairseq/modules/transformer_layer.py

@@ -12,7 +12,8 @@ from fairseq.modules import (
     LayerNorm,
     LayerNorm,
     MultiheadAttention,
     MultiheadAttention,
     RelPositionMultiheadAttention,
     RelPositionMultiheadAttention,
-    RelativeMultiheadAttention
+    RelativeMultiheadAttention,
+    LocalMultiheadAttention,
 )
 )
 from fairseq.modules.fairseq_dropout import FairseqDropout
 from fairseq.modules.fairseq_dropout import FairseqDropout
 from fairseq.modules.quant_noise import quant_noise
 from fairseq.modules.quant_noise import quant_noise
@@ -103,6 +104,21 @@ class TransformerEncoderLayer(nn.Module):
             else:
             else:
                 print("The maximum encoder relative length %d can not be -1!" % max_relative_length)
                 print("The maximum encoder relative length %d can not be -1!" % max_relative_length)
                 exit(1)
                 exit(1)
+        elif self.attn_type == "local":
+            hard_mask_window = getattr(args, "hard_mask_window", 0)
+            gauss_mask_sigma = getattr(args, "gauss_mask_sigma", 0)
+            init_mask_weight = getattr(args, "init_mask_weight", 0)
+            return LocalMultiheadAttention(
+                embed_dim,
+                args.encoder_attention_heads,
+                dropout=args.attention_dropout,
+                self_attention=True,
+                q_noise=self.quant_noise,
+                qn_block_size=self.quant_noise_block_size,
+                hard_mask_window=hard_mask_window,
+                gauss_mask_sigma=gauss_mask_sigma,
+                init_mask_weight=init_mask_weight
+            )
         else:
         else:
             print("The encoder attention type %s is not supported!" % self.attn_type)
             print("The encoder attention type %s is not supported!" % self.attn_type)
             exit(1)
             exit(1)