Merge branch 'scaled_adam_exp106' into scaled_adam_exp108

# Conflicts:
#	egs/librispeech/ASR/pruned_transducer_stateless7/conformer.py

egs/librispeech/ASR/pruned_transducer_stateless7/conformer.py

@@ -304,18 +304,15 @@ class ConformerEncoderLayer(nn.Module):
         self,
         src: Tensor,
         pos_emb: Tensor,
-        attn_scores_in: Optional[Tensor] = None,
         src_mask: Optional[Tensor] = None,
         src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tuple[Tensor, Tensor]:
+    ) -> Tensor:
         """
         Pass the input through the encoder layer.
 
         Args:
             src: the sequence to the encoder layer (required).
             pos_emb: Positional embedding tensor (required).
-            attn_scores_in: something with the dimension fo attention weights (bsz, len, len, num_heads) that is
-                   passed from layer to layer.
             src_mask: the mask for the src sequence (optional).
             src_key_padding_mask: the mask for the src keys per batch (optional).
         batch_split: if not None, this layer will only be applied to
@@ -333,10 +330,9 @@ class ConformerEncoderLayer(nn.Module):
         src = src + self.feed_forward1(src)
 
         # multi-headed self-attention module
-        src_att, attn_weights, attn_scores_out = self.self_attn(
+        src_att, attn_weights = self.self_attn(
             src,
             pos_emb=pos_emb,
-            attn_scores_in=attn_scores_in,
             attn_mask=src_mask,
             key_padding_mask=src_key_padding_mask,
         )
@@ -362,7 +358,7 @@ class ConformerEncoderLayer(nn.Module):
             bypass_scale = bypass_scale.clamp(min=0.1, max=1.0)
         src = src_orig + delta * self.bypass_scale
 
-        return src, attn_scores_out
+        return src
 
 
 class ConformerEncoder(nn.Module):
@@ -520,7 +516,6 @@ class ConformerEncoder(nn.Module):
         output = src
 
         outputs = []
-        attn_scores = None
 
 
         rnd_seed = src.numel() + random.randint(0, 1000)
@@ -531,10 +526,9 @@ class ConformerEncoder(nn.Module):
         for i, mod in enumerate(self.layers):
             if i in layers_to_drop:
                 continue
-            output, attn_scores = mod(
+            output = mod(
                 output,
                 pos_emb,
-                attn_scores,
                 src_mask=mask,
                 src_key_padding_mask=src_key_padding_mask,
             )
@@ -856,8 +850,6 @@ class RelPositionMultiheadAttention(nn.Module):
         self.out_proj2 = ScaledLinear(embed_dim // 2, embed_dim, bias=True,
                                       initial_scale=0.05)
 
-        self.attn_scores_proj_in = nn.Parameter(torch.eye(num_heads))
-        self.attn_scores_proj_out = nn.Parameter(torch.zeros(num_heads, num_heads))
 
         # linear transformation for positional encoding.
         self.linear_pos = nn.Linear(embed_dim, embed_dim // 2, bias=False)
@@ -875,7 +867,6 @@ class RelPositionMultiheadAttention(nn.Module):
         self,
         x: Tensor,
         pos_emb: Tensor,
-        attn_scores_in: Optional[Tensor],
         key_padding_mask: Optional[Tensor] = None,
         attn_mask: Optional[Tensor] = None,
     ) -> Tuple[Tensor, Tensor, Tensor]:
@@ -897,7 +888,6 @@ class RelPositionMultiheadAttention(nn.Module):
             the embedding dimension.
             - pos_emb: :math:`(N, 2*L-1, E)` where L is the target sequence length, N is the batch size, E is
             the embedding dimension.
-            - attn_scores_in: :math:`(N, L, L, num_heads)`
             - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.
             If a ByteTensor is provided, the non-zero positions will be ignored while the position
             with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
@@ -910,19 +900,16 @@ class RelPositionMultiheadAttention(nn.Module):
             is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
             is provided, it will be added to the attention weight.
 
-            - Returns: (attn_output, attn_weights, attn_scores)
+            - Returns: (attn_output, attn_weights)
 
              - attn_output: :math:`(S, N, E)` where S is the sequence length, N is the batch size,
                 E is the embedding dimension.
               - attn_weights: :math:`(N * N, S, S)` where N is the batch size, H is the num-heads
                  and S is the sequence length.
-              - attn_scores: :math:`(N, S, S, H)`, these are the attn weights
-                before softmax.
         """
-        x, weights, scores = self.multi_head_attention_forward(
+        x, weights = self.multi_head_attention_forward(
             self.in_max_eig(self.in_balancer(self.in_proj(x))),
             pos_emb,
-            None if attn_scores_in is None else torch.matmul(attn_scores_in, self.attn_scores_proj_in),
             self.embed_dim,
             self.num_heads,
             self.in_proj.weight,
@@ -934,15 +921,7 @@ class RelPositionMultiheadAttention(nn.Module):
             key_padding_mask=key_padding_mask,
             attn_mask=attn_mask,
         )
-        if attn_scores_in is not None:
-            attn_scores_out = torch.matmul(scores, self.attn_scores_proj_out)
-            attn_scores_out = attn_scores_out + attn_scores_in
-        else:
-            # Here, add self.attn_scores_proj_in in order to make sure it has
-            # a grad.
-            attn_scores_out = torch.matmul(scores, self.attn_scores_proj_out +
-                                           self.attn_scores_proj_in)
-        return x, weights, attn_scores_out
+        return x, weights
 
     def rel_shift(self, x: Tensor) -> Tensor:
         """Compute relative positional encoding.
@@ -973,7 +952,6 @@ class RelPositionMultiheadAttention(nn.Module):
         self,
         x: Tensor,
         pos_emb: Tensor,
-        attn_scores_in: Optional[Tensor],
         embed_dim: int,
         num_heads: int,
         in_proj_weight: Tensor,
@@ -1028,8 +1006,6 @@ class RelPositionMultiheadAttention(nn.Module):
               E is the embedding dimension.
             - attn_weights: :math:`(N * H, S, S)` where N is the batch size,
               H is the num-heads, S is the sequence length.
-            - attn_scores: :math:`(N, S, S, H)` where N is the batch size,
-              S is the sequence length and H is the num-heads.
         """
 
         tgt_len, bsz, _ = x.size()
@@ -1142,10 +1118,6 @@ class RelPositionMultiheadAttention(nn.Module):
             matrix_ac + matrix_bd
         )  # (batch, head, time1, time2)
 
-        attn_scores_out = attn_output_weights.permute(0, 2, 3, 1) # (batch, time1, time2, head)
-
-        if attn_scores_in is not None:
-            attn_output_weights = attn_output_weights + attn_scores_in.permute(0, 3, 1, 2)
 
 
         attn_output_weights = attn_output_weights.view(
@@ -1192,7 +1164,7 @@ class RelPositionMultiheadAttention(nn.Module):
             attn_output, out_proj_weight, out_proj_bias
         )
 
-        return attn_output, attn_output_weights, attn_scores_out
+        return attn_output, attn_output_weights
 
 
     def forward2(