Remove Pre-norm option, since it is not used

Summary: As in the tittle

Reviewed By: XiaoliangDai

Differential Revision: D33413849

fbshipit-source-id: b891849c175edc7b8916bff2fcc40c76c4658f14

projects_oss/detr/detr/models/transformer.py

@@ -203,12 +203,12 @@ class TransformerEncoderLayer(nn.Module):
         self.dropout2 = nn.Dropout(dropout)
 
         self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
+        assert not normalize_before, "normalize_before is not supported"
 
     def with_pos_embed(self, tensor, pos: Optional[Tensor]):
         return tensor if pos is None else tensor + pos
 
-    def forward_post(
+    def forward(
         self,
         src,
         src_mask: Optional[Tensor] = None,
@@ -228,35 +228,6 @@ class TransformerEncoderLayer(nn.Module):
         src = self.norm2(src)
         return src
 
-    def forward_pre(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        src2 = self.norm1(src)
-        q = k = self.with_pos_embed(src2, pos)
-        src2 = self.self_attn(
-            q, k, src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
-        )[0]
-        src = src + self.dropout1(src2)
-        src2 = self.norm2(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
-        src = src + self.dropout2(src2)
-        return src
-
-    def forward(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        if self.normalize_before:
-            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
-        return self.forward_post(src, src_mask, src_key_padding_mask, pos)
-
 
 class TransformerDecoderLayer(nn.Module):
     def __init__(
@@ -284,12 +255,12 @@ class TransformerDecoderLayer(nn.Module):
         self.dropout3 = nn.Dropout(dropout)
 
         self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
+        assert not normalize_before, "normalize_before is not supported"
 
     def with_pos_embed(self, tensor, pos: Optional[Tensor]):
         return tensor if pos is None else tensor + pos
 
-    def forward_post(
+    def forward(
         self,
         tgt,
         memory,
@@ -327,70 +298,6 @@ class TransformerDecoderLayer(nn.Module):
         # return tgt shape (L, B, C)
         return tgt
 
-    def forward_pre(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        tgt2 = self.norm1(tgt)
-        q = k = self.with_pos_embed(tgt2, query_pos)
-        tgt2 = self.self_attn(
-            q, k, tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
-        )[0]
-        tgt = tgt + self.dropout1(tgt2)
-        tgt2 = self.norm2(tgt)
-        tgt2 = self.multihead_attn(
-            self.with_pos_embed(tgt2, query_pos),
-            self.with_pos_embed(memory, pos),
-            memory,
-            attn_mask=memory_mask,
-            key_padding_mask=memory_key_padding_mask,
-        )[0]
-        tgt = tgt + self.dropout2(tgt2)
-        tgt2 = self.norm3(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout3(tgt2)
-        return tgt
-
-    def forward(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        if self.normalize_before:
-            return self.forward_pre(
-                tgt,
-                memory,
-                tgt_mask,
-                memory_mask,
-                tgt_key_padding_mask,
-                memory_key_padding_mask,
-                pos,
-                query_pos,
-            )
-        return self.forward_post(
-            tgt,
-            memory,
-            tgt_mask,
-            memory_mask,
-            tgt_key_padding_mask,
-            memory_key_padding_mask,
-            pos,
-            query_pos,
-        )
-
 
 def _get_clones(module, N):
     return nn.ModuleList([copy.deepcopy(module) for i in range(N)])