Fix bug in MSA2 (mixed k and v)

RT, another bug

fastNLP/modules/encoder/star_transformer.py

@@ -7,7 +7,6 @@ import numpy as NP
 class StarTransformer(nn.Module):
     """Star-Transformer Encoder part。
     paper: https://arxiv.org/abs/1902.09113
-
     :param hidden_size: int, 输入维度的大小。同时也是输出维度的大小。
     :param num_layers: int, star-transformer的层数
     :param num_head: int，head的数量。
@@ -46,6 +45,7 @@ class StarTransformer(nn.Module):
             return f(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
 
         B, L, H = data.size()
+        mask = (mask == 0) # flip the mask for masked_fill_
         smask = torch.cat([torch.zeros(B, 1, ).byte().to(mask), mask], 1)
 
         embs = data.permute(0, 2, 1)[:,:,:,None] # B H L 1
@@ -57,7 +57,6 @@ class StarTransformer(nn.Module):
         nodes = embs
         relay = embs.mean(2, keepdim=True)
         ex_mask = mask[:, None, :, None].expand(B, H, L, 1)
-        ex_mask = ex_mask.ne(1) # reverse mask for next masked_fill.
         r_embs = embs.view(B, H, 1, L)
         for i in range(self.iters):
             ax = torch.cat([r_embs, relay.expand(B, H, 1, L)], 2)
@@ -137,11 +136,10 @@ class MSA2(nn.Module):
 
         q = q.view(B, nhead, 1, head_dim)  # B, H, 1, 1 -> B, N, 1, h
         k = k.view(B, nhead, head_dim, L)  # B, H, L, 1 -> B, N, h, L
-        v = k.view(B, nhead, head_dim, L).permute(0, 1, 3, 2)  # B, H, L, 1 -> B, N, L, h
+        v = v.view(B, nhead, head_dim, L).permute(0, 1, 3, 2)  # B, H, L, 1 -> B, N, L, h
         pre_a = torch.matmul(q, k) / NP.sqrt(head_dim)
         if mask is not None:
             pre_a = pre_a.masked_fill(mask[:, None, None, :], -float('inf'))
         alphas = self.drop(F.softmax(pre_a, 3))  # B, N, 1, L
         att = torch.matmul(alphas, v).view(B, -1, 1, 1)  # B, N, 1, h -> B, N*h, 1, 1
         return self.WO(att)
-