Optimize Gelu module for caffe2 export (#918)

Summary:
Pull Request resolved: #918

TIL ONNX->Caffe2 is very memory inefficient, it creates an intermediate blob for each intermediate output. So, the Gelu operator creates a lot of intermediate ops since it does a bunch of math.

Fix is to use the caffe2 Gelu operator, so all that computation is captured in a single op.

https://pxl.cl/HzGf

Differential Revision: D16849396

fbshipit-source-id: a17908daff58b2c005afbc72fcb8dc46c37d075d

pytext/optimizer/activations.py

@@ -10,21 +10,33 @@
 
 class GeLU(nn.Module):
     """
-    Implements Gaussian Error Linear Units (GELUs). Note: x * x * x is used
-    instead of torch.pow(x, 3) due to issues with ONNX compatibility:
-    https://github.com/pytorch/pytorch/issues/18475
+    Implements Gaussian Error Linear Units (GELUs).
 
     Reference:
     Gaussian Error Linear Units (GELUs). Dan Hendrycks, Kevin Gimpel.
     Technical Report, 2017. https://arxiv.org/pdf/1606.08415.pdf
     """
 
     def forward(self, x):
-        return (
-            0.5
-            * x
-            * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * (x * x * x))))
-        )
+        if torch.onnx.is_in_onnx_export():
+            # ONNX -> Caffe2 conversion will create an intermediate blob for
+            # each intermediate math output, which is very memory inefficient.
+            # We use the Gelu operator directly to reduce the memory footprint
+            # in the exported model.
+            return torch.ops._caffe2.Gelu(x, True)
+        else:
+
+            return (
+                0.5
+                * x
+                * (
+                    # Note: x * x * x is used instead of torch.pow(x, 3) due to
+                    # issues with ONNX compatibility:
+                    # https://github.com/pytorch/pytorch/issues/18475
+                    1
+                    + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * (x * x * x)))
+                )
+            )
 
 
 def get_activation(name):