diff --git a/examples/fairseq/models/bert.py b/examples/fairseq/models/bert.py
index 7bbe382..d3ffa3f 100644
--- a/examples/fairseq/models/bert.py
+++ b/examples/fairseq/models/bert.py
@@ -391,7 +391,7 @@ class ClassificationHead(nn.Module):
         x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
         x = self.dropout(x)
         x = self.dense(x)
-        x = self.activation_fn(x)
+        x = self.activation_fn(x.float()).as_type(x)
         x = self.dropout(x)
         x = self.out_proj(x)
         return x
@@ -418,7 +418,7 @@ class LMHead(nn.Module):
             features = features[masked_tokens, :]
 
         x = self.dense(features)
-        x = self.activation_fn(x)
+        x = self.activation_fn(x.float()).as_type(x)
         x = self.layer_norm(x)
         # project back to size of vocabulary with bias
         x = F.linear(x, self.weight) + self.bias
diff --git a/torchscale/architecture/decoder.py b/torchscale/architecture/decoder.py
index 0463800..704e438 100644
--- a/torchscale/architecture/decoder.py
+++ b/torchscale/architecture/decoder.py
@@ -400,7 +400,7 @@ class Decoder(nn.Module):
         )
         x = x.transpose(0, 1)
 
-        # relative postion
+        # relative position
         self_attn_rel_pos_bias = None
         slen = prev_output_tokens.size(1)
         if self.self_attn_relative_position is not None:
diff --git a/torchscale/component/feedforward_network.py b/torchscale/component/feedforward_network.py
index 77aadb1..a423cdf 100644
--- a/torchscale/component/feedforward_network.py
+++ b/torchscale/component/feedforward_network.py
@@ -85,7 +85,7 @@ def get_activation_fn(activation):
     if activation == "relu":
         return F.relu
     elif activation == "gelu":
-        return lambda x: F.gelu(x.float()).type_as(x)
+        return F.gelu
     else:
         raise NotImplementedError
 
@@ -121,7 +121,7 @@ class FeedForwardNetwork(nn.Module):
         x_shape = x.shape
         x = x.reshape(-1, x.size(-1))
         x = self.fc1(x)
-        x = self.activation_fn(x)
+        x = self.activation_fn(x.float()).as_type(x)
         x = self.activation_dropout_module(x)
         if self.ffn_layernorm is not None:
             x = self.ffn_layernorm(x)