Add mkldnn sigmoid operator

Summary: Pull Request resolved: pytorch#20820

Reviewed By: dzhulgakov

Differential Revision: D15455866

fbshipit-source-id: 712b06dfbd441051dc284a1acdf94926df09bc1d

aten/src/ATen/native/mkldnn/UnaryOps.cpp

@@ -0,0 +1,46 @@
+#include <ATen/ATen.h>
+#include <ATen/Config.h>
+#include <ATen/NativeFunctions.h>
+
+#if !AT_MKLDNN_ENABLED()
+
+namespace at {
+namespace native {
+
+Tensor mkldnn_sigmoid(const Tensor& self) {
+  AT_ERROR("mkldnn_sigmoid: ATen not compiled with MKLDNN support");
+}
+
+Tensor& mkldnn_sigmoid_(Tensor& self) {
+  AT_ERROR("mkldnn_sigmoid_: ATen not compiled with MKLDNN support");
+}
+
+} // namespace native
+} // namespace at
+
+#else // AT_MKLDNN_EBABLED
+
+#include <ATen/native/mkldnn/MKLDNNCommon.h>
+
+namespace at {
+namespace native {
+
+Tensor mkldnn_sigmoid(const Tensor& self) {
+  ideep::tensor& x = itensor_from_mkldnn(self);
+  ideep::tensor y;
+  ideep::eltwise_forward::compute(
+      x, y, ideep::algorithm::eltwise_logistic, ideep::prop_kind::forward);
+  return new_with_itensor_mkldnn(std::move(y), self.options());
+}
+
+Tensor& mkldnn_sigmoid_(Tensor& self) {
+  ideep::tensor& x = itensor_from_mkldnn(self);
+  ideep::eltwise_forward::compute(
+      x, x, ideep::algorithm::eltwise_logistic, ideep::prop_kind::forward);
+  return self;
+}
+
+} // namespace native
+} // namespace at
+
+#endif // AT_MKLDNN_EBABLED

aten/src/ATen/native/native_functions.yaml

@@ -1593,12 +1593,17 @@
 
 - func: sigmoid(Tensor self) -> Tensor
   variants: function, method
+  dispatch:
+    CPU: sigmoid
+    CUDA: sigmoid
+    MkldnnCPU: mkldnn_sigmoid
 
 - func: sigmoid_(Tensor(a!) self) -> Tensor(a!)
   variants: function, method
   dispatch:
     CPU: _sigmoid__cpu
     CUDA: _sigmoid__cuda
+    MkldnnCPU: mkldnn_sigmoid_
 
 - func: sigmoid(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
   dispatch:

test/test_mkldnn.py

@@ -248,6 +248,18 @@ def test_linear(self):
             self._test_serialization(mkldnn_linear, (x.to_mkldnn(),))
             self._test_tracing(mkldnn_linear, (x.to_mkldnn(),))
 
+    def test_sigmoid(self):
+        x = torch.randn(4, 5, dtype=torch.float32) * 10
+        mkldnn_x = x.to_mkldnn()
+        self.assertEqual(
+            torch.sigmoid(x),
+            torch.sigmoid(mkldnn_x).to_dense(),
+        )
+        # inplace
+        torch.sigmoid_(x)
+        torch.sigmoid_(mkldnn_x)
+        self.assertEqual(x, mkldnn_x.to_dense())
+
     def _test_serialization(self, module, inputs):
         with TemporaryFileName() as fname:
             torch.jit.save(module, fname)