diff --git a/mindspore/ccsrc/kernel/gpu/nn/activation_gpu_kernel.cc b/mindspore/ccsrc/kernel/gpu/nn/activation_gpu_kernel.cc
index 0246707d16..5e80cccd75 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/activation_gpu_kernel.cc
+++ b/mindspore/ccsrc/kernel/gpu/nn/activation_gpu_kernel.cc
@@ -27,5 +27,10 @@ MS_REG_GPU_KERNEL_ONE(Tanh, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOut
                       ActivationGpuFwdKernel, float)
 MS_REG_GPU_KERNEL_ONE(Tanh, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
                       ActivationGpuFwdKernel, half)
+
+MS_REG_GPU_KERNEL_ONE(Sigmoid, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                      ActivationGpuFwdKernel, float)
+MS_REG_GPU_KERNEL_ONE(Sigmoid, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+                      ActivationGpuFwdKernel, half)
 }  // namespace kernel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/kernel/gpu/nn/activation_grad_kernel.cc b/mindspore/ccsrc/kernel/gpu/nn/activation_grad_kernel.cc
index 506d2268f7..35d11f8b47 100644
--- a/mindspore/ccsrc/kernel/gpu/nn/activation_grad_kernel.cc
+++ b/mindspore/ccsrc/kernel/gpu/nn/activation_grad_kernel.cc
@@ -35,5 +35,14 @@ MS_REG_GPU_KERNEL_ONE(
   TanhGrad,
   KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
   ActivationGradGpuKernel, half)
+
+MS_REG_GPU_KERNEL_ONE(
+  SigmoidGrad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+  ActivationGradGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(
+  SigmoidGrad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+  ActivationGradGpuKernel, half)
 }  // namespace kernel
 }  // namespace mindspore
diff --git a/tests/st/ops/gpu/test_sigmoid_grad_op.py b/tests/st/ops/gpu/test_sigmoid_grad_op.py
new file mode 100644
index 0000000000..92d1d4d9f7
--- /dev/null
+++ b/tests/st/ops/gpu/test_sigmoid_grad_op.py
@@ -0,0 +1,61 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops.operations import _grad_ops as G
+
+
+class NetSigmoidGrad(nn.Cell):
+    def __init__(self):
+        super(NetSigmoidGrad, self).__init__()
+        self.sigmoid_grad = G.SigmoidGrad()
+
+    def construct(self, y, dy):
+        return self.sigmoid_grad(y, dy)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sigmoid_grad():
+    y = Tensor(np.array([[[[-1, 1, 2],
+                           [1, -1, 1],
+                           [2, 1, -1]]]]).astype(np.float32))
+    dy = Tensor(np.array([[[[-11, 2, 4],
+                            [-1, 1, -1],
+                            [-4, 4, -4]]]]).astype(np.float32))
+
+    expect = np.array([[[[22, 0, -8],
+                         [0, -2, 0],
+                         [8, 0, 8]]]]).astype(np.float32)
+
+    error = np.ones(shape=[1, 1, 3, 3]) * 1.0e-6
+
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    sigmoid_grad = NetSigmoidGrad()
+    output = sigmoid_grad(y, dy)
+    diff = output.asnumpy() - expect
+    assert np.all(abs(diff) < error)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    sigmoid_grad = NetSigmoidGrad()
+    output = sigmoid_grad(y, dy)
+    diff = output.asnumpy() - expect
+    assert np.all(abs(diff) < error)
diff --git a/tests/st/ops/gpu/test_sigmoid_op.py b/tests/st/ops/gpu/test_sigmoid_op.py
new file mode 100644
index 0000000000..f3d724a35b
--- /dev/null
+++ b/tests/st/ops/gpu/test_sigmoid_op.py
@@ -0,0 +1,57 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+
+class NetSigmoid(nn.Cell):
+    def __init__(self):
+        super(NetSigmoid, self).__init__()
+        self.sigmoid = P.Sigmoid()
+
+    def construct(self, x):
+        return self.sigmoid(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sigmoid():
+    x = Tensor(np.array([[[[-1, 1, 10],
+                           [1, -1, 1],
+                           [10, 1, -1]]]]).astype(np.float32))
+    expect = np.array([[[[0.268941, 0.731059, 0.999955],
+                         [0.731059, 0.268941, 0.731059],
+                         [0.999955, 0.731059, 0.268941]]]]).astype(np.float32)
+
+    error = np.ones(shape=[1, 1, 3, 3]) * 1.0e-6
+
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    sigmoid = NetSigmoid()
+    output = sigmoid(x)
+    diff = output.asnumpy() - expect
+    assert np.all(abs(diff) < error)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    sigmoid = NetSigmoid()
+    output = sigmoid(x)
+    diff = output.asnumpy() - expect
+    assert np.all(abs(diff) < error)