add grad definition for sigmoidGrad operation

mindspore/ops/_grad/grad_nn_ops.py

@@ -472,6 +472,19 @@ def get_bprop_sigmoid(self):
     return bprop
 
 
+@bprop_getters.register(G.SigmoidGrad)
+def get_bprop_sigmoid_grad(self):
+    """Grad definition for `SigmoidGrad` operation."""
+    sigmoid_grad = G.SigmoidGrad()
+
+    def bprop(y, grad, out, dout):
+        ddy = dout * grad * (1. - 2 * y)
+        d2x = sigmoid_grad(y, dout)
+        return (ddy, d2x)
+
+    return bprop
+
+
 @bprop_getters.register(P.Softmax)
 def get_bprop_softmax(self):
     """Grad definition for `Softmax` operation."""

tests/st/ops/gpu/test_sigmoid_grad_grad_op.py

@@ -0,0 +1,86 @@
+# 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.common.api import ms_function
+from mindspore.ops.operations import _grad_ops as G
+from mindspore.ops.composite import GradOperation
+
+
+class NetSigmoidGrad(nn.Cell):
+    def __init__(self):
+        super(NetSigmoidGrad, self).__init__()
+        self.sigmoid_grad = G.SigmoidGrad()
+
+    @ms_function
+    def construct(self, y, dy):
+        return self.sigmoid_grad(y, dy)
+
+
+class Grad(nn.Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = GradOperation(get_all=True, sens_param=True)
+        self.network = network
+
+    @ms_function
+    def construct(self, y, y_grad, dout):
+        return self.grad(self.network)(y, y_grad, dout)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sigmoid_grad_grad():
+    y = Tensor(np.array([[[[-1, 1, 2],
+                           [1, -1, 1],
+                           [2, 1, -1]]]]).astype(np.float32))
+    y_grad = Tensor(np.array([[[[-11, 2, 4],
+                                [-1, 1, -1],
+                                [-4, 4, -4]]]]).astype(np.float32))
+    dout = Tensor(np.array([[[[-11, 2, 4],
+                              [-1, 1, -1],
+                              [-4, 4, -4]]]]).astype(np.float32))
+
+    expect_ddy = np.array([[[[363., -4., -48.],
+                             [-1., 3., -1.],
+                             [-48., -16., 48.]]]]).astype(np.float32)
+
+    expect_d2x = 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_grad = Grad(NetSigmoidGrad())
+    ddy, d2x = sigmoid_grad_grad(y, y_grad, dout)
+    diff0 = ddy.asnumpy() - expect_ddy
+    diff1 = d2x.asnumpy() - expect_d2x
+    assert np.all(abs(diff0) < error)
+    assert np.all(abs(diff1) < error)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    sigmoid_grad_grad = Grad(NetSigmoidGrad())
+    ddy, d2x = sigmoid_grad_grad(y, y_grad, dout)
+    diff0 = ddy.asnumpy() - expect_ddy
+    diff1 = d2x.asnumpy() - expect_d2x
+    assert np.all(abs(diff0) < error)
+    assert np.all(abs(diff1) < error)