Merge pull request #204 from glynpu/espnet_scaling_and_layernorm

espnet-style attn_output_weight scaling and extra after-norm layer

egs/librispeech/asr/simple_v1/mmi_att_transformer_decode.py

@@ -242,7 +242,7 @@ def main():
 
     output_beam_size = args.output_beam_size
 
-    exp_dir = Path('exp-' + model_type + '-noam-mmi-att-musan-sa-vgg')
+    exp_dir = Path('exp-' + model_type + '-mmi-att-sa-vgg-normlayer')
     setup_logger('{}/log/log-decode'.format(exp_dir), log_level='debug')
 
     logging.info(f'output_beam_size: {output_beam_size}')
@@ -285,7 +285,8 @@ def main():
             num_classes=len(phone_ids) + 1,  # +1 for the blank symbol
             subsampling_factor=4,
             num_decoder_layers=num_decoder_layers,
-            vgg_frontend=True)
+            vgg_frontend=True,
+            is_espnet_structure=True)
     elif model_type == "contextnet":
         model = ContextNet(
         num_features=80,

egs/librispeech/asr/simple_v1/mmi_att_transformer_train.py

@@ -465,7 +465,7 @@ def run(rank, world_size, args):
     fix_random_seed(42)
     setup_dist(rank, world_size, args.master_port)
 
-    exp_dir = Path('exp-' + model_type + '-noam-mmi-att-musan-sa-vgg')
+    exp_dir = Path('exp-' + model_type + '-mmi-att-sa-vgg-normlayer')
     setup_logger(f'{exp_dir}/log/log-train-{rank}')
     if args.tensorboard and rank == 0:
         tb_writer = SummaryWriter(log_dir=f'{exp_dir}/tensorboard')
@@ -526,7 +526,8 @@ def run(rank, world_size, args):
             num_classes=len(phone_ids) + 1,  # +1 for the blank symbol
             subsampling_factor=4,
             num_decoder_layers=num_decoder_layers,
-            vgg_frontend=True)
+            vgg_frontend=True,
+            is_espnet_structure=True)
     elif model_type == "contextnet":
         model = ContextNet(
         num_features=80,

snowfall/models/conformer.py

@@ -36,16 +36,21 @@ def __init__(self, num_features: int, num_classes: int, subsampling_factor: int
                  d_model: int = 256, nhead: int = 4, dim_feedforward: int = 2048,
                  num_encoder_layers: int = 12, num_decoder_layers: int = 6,
                  dropout: float = 0.1, cnn_module_kernel: int = 31,
-                 normalize_before: bool = True, vgg_frontend: bool = False) -> None:
+                 normalize_before: bool = True, vgg_frontend: bool = False,
+                 is_espnet_structure: bool = False) -> None:
         super(Conformer, self).__init__(num_features=num_features, num_classes=num_classes, subsampling_factor=subsampling_factor,
                  d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward,
                  num_encoder_layers=num_encoder_layers, num_decoder_layers=num_decoder_layers,
                  dropout=dropout, normalize_before=normalize_before, vgg_frontend=vgg_frontend)
 
         self.encoder_pos = RelPositionalEncoding(d_model, dropout)
 
-        encoder_layer = ConformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, cnn_module_kernel, normalize_before)
+        encoder_layer = ConformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, cnn_module_kernel, normalize_before, is_espnet_structure)
         self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers)
+        self.normalize_before = normalize_before
+        self.is_espnet_structure = is_espnet_structure
+        if self.normalize_before and self.is_espnet_structure:
+            self.after_norm = nn.LayerNorm(d_model)
 
     def encode(self, x: Tensor, supervisions: Optional[Dict] = None) -> Tuple[Tensor, Optional[Tensor]]:
         """
@@ -66,6 +71,9 @@ def encode(self, x: Tensor, supervisions: Optional[Dict] = None) -> Tuple[Tensor
         mask = mask.to(x.device) if mask != None else None
         x = self.encoder(x, pos_emb, src_key_padding_mask=mask)  # (T, B, F)
 
+        if self.normalize_before and self.is_espnet_structure:
+            x = self.after_norm(x)
+
         return x, mask
 
 
@@ -90,9 +98,10 @@ class ConformerEncoderLayer(nn.Module):
     """
 
     def __init__(self, d_model: int, nhead: int, dim_feedforward: int = 2048, dropout: float = 0.1,
-                 cnn_module_kernel: int = 31, normalize_before: bool = True) -> None:
+                 cnn_module_kernel: int = 31, normalize_before: bool = True,
+                 is_espnet_structure: bool = False) -> None:
         super(ConformerEncoderLayer, self).__init__()
-        self.self_attn = RelPositionMultiheadAttention(d_model, nhead, dropout=0.0)
+        self.self_attn = RelPositionMultiheadAttention(d_model, nhead, dropout=0.0, is_espnet_structure=is_espnet_structure)
 
         self.feed_forward = nn.Sequential(
             nn.Linear(d_model, dim_feedforward),
@@ -319,7 +328,8 @@ class RelPositionMultiheadAttention(nn.Module):
         >>> attn_output, attn_output_weights = multihead_attn(query, key, value, pos_emb)
     """
 
-    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.) -> None:
+    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.,
+                 is_espnet_structure: bool = False) -> None:
         super(RelPositionMultiheadAttention, self).__init__()
         self.embed_dim = embed_dim
         self.num_heads = num_heads
@@ -339,6 +349,8 @@ def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.) -> None:
 
         self._reset_parameters()
 
+        self.is_espnet_structure = is_espnet_structure
+
     def _reset_parameters(self) -> None:
         nn.init.xavier_uniform_(self.in_proj.weight)
         nn.init.constant_(self.in_proj.bias, 0.)
@@ -538,7 +550,8 @@ def multi_head_attention_forward(self, query: Tensor,
                 _b = _b[_start:]
             v = nn.functional.linear(value, _w, _b)
 
-        q = q * scaling
+        if not self.is_espnet_structure:
+            q = q * scaling
 
         if attn_mask is not None:
             assert attn_mask.dtype == torch.float32 or attn_mask.dtype == torch.float64 or \
@@ -596,7 +609,10 @@ def multi_head_attention_forward(self, query: Tensor,
         matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) # (batch, head, time1, 2*time1-1)
         matrix_bd = self.rel_shift(matrix_bd)
 
-        attn_output_weights = (matrix_ac + matrix_bd)  # (batch, head, time1, time2)
+        if not self.is_espnet_structure:
+            attn_output_weights = (matrix_ac + matrix_bd)  # (batch, head, time1, time2)
+        else:
+            attn_output_weights = (matrix_ac + matrix_bd) * scaling  # (batch, head, time1, time2)
 
         attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, -1)