!10332 add grad definitions for the following operations: ReluGrad, SqrtGrad, AtanGrad, TanhGrad, AsinhGrad, AcoshGrad.

From: @david-he91
Reviewed-by: @liangchenghui
Signed-off-by: @liangchenghui

mindspore/ops/_grad/grad_math_ops.py

@@ -420,6 +420,19 @@ def get_bprop_sqrt(self):
     return bprop
 
 
+@bprop_getters.register(G.SqrtGrad)
+def get_bprop_sqrt_grad(self):
+    """Grad definition for `SqrtGrad` operation."""
+
+    def bprop(y, grad, out, dout):
+        gy = dout / y
+        dy = -gy * out
+        dgrad = 0.5 * gy
+        return dy, dgrad
+
+    return bprop
+
+
 @bprop_getters.register(P.Rsqrt)
 def get_bprop_rsqrt(self):
     """Grad definition for `Rsqrt` operation."""
@@ -962,6 +975,19 @@ def get_bprop_asinh(self):
     return bprop
 
 
+@bprop_getters.register(G.AsinhGrad)
+def get_bprop_asinh_grad(self):
+    """Grad definition for `AsinhGrad` operation."""
+    input_grad = G.AsinhGrad()
+    tanh = P.Tanh()
+
+    def bprop(y, grad, out, dout):
+        dy = dout * out * -1.0 * tanh(y)
+        dgrad = input_grad(y, dout)
+        return dy, dgrad
+    return bprop
+
+
 @bprop_getters.register(P.Sinh)
 def get_bprop_sinh(self):
     """Grad definition for `Sinh` operation."""
@@ -1026,6 +1052,20 @@ def get_bprop_acosh(self):
     return bprop
 
 
+@bprop_getters.register(G.AcoshGrad)
+def get_bprop_acosh_grad(self):
+    """Grad definition for `AcoshGrad` operation."""
+    input_grad = G.AcoshGrad()
+    tanh = P.Tanh()
+
+    def bprop(y, grad, out, dout):
+        dy = dout * out * -1.0 / tanh(y)
+        dgrad = input_grad(y, dout)
+        return dy, dgrad
+
+    return bprop
+
+
 @bprop_getters.register(P.Cosh)
 def get_bprop_cosh(self):
     """Grad definition for `Cosh` operation."""
@@ -1150,6 +1190,18 @@ def get_bprop_atan(self):
     return bprop
 
 
+@bprop_getters.register(G.AtanGrad)
+def get_bprop_atan_grad(self):
+    """Grad definition for `AtanGrad` operation."""
+    input_grad = G.AtanGrad()
+
+    def bprop(x, grad, out, dout):
+        dgrad = input_grad(x, dout)
+        dx = out * dgrad * -2.0 * x
+        return dx, dgrad
+    return bprop
+
+
 @bprop_getters.register(P.Tan)
 def get_bprop_tan(self):
     """Grad definition for `Tan` operation."""

mindspore/ops/_grad/grad_nn_ops.py

@@ -422,6 +422,18 @@ def get_bprop_relu(self):
     return bprop
 
 
+@bprop_getters.register(G.ReluGrad)
+def get_bprop_relu_grad(self):
+    """Grad definition for `ReLUGrad` operation."""
+    input_grad = G.ReluGrad()
+
+    def bprop(grad, y, out, dout):
+        dgrad = input_grad(dout, y)
+        return dgrad, zeros_like(y)
+
+    return bprop
+
+
 @bprop_getters.register(P.ReLU6)
 def get_bprop_relu6(self):
     """Grad definition for `ReLU6` operation."""
@@ -501,9 +513,9 @@ def get_bprop_sigmoid_grad(self):
     sigmoid_grad = G.SigmoidGrad()
 
     def bprop(y, grad, out, dout):
-        ddy = dout * grad * (1. - 2 * y)
-        d2x = sigmoid_grad(y, dout)
-        return (ddy, d2x)
+        dy = dout * grad * (1. - 2 * y)
+        dgrad = sigmoid_grad(y, dout)
+        return dy, dgrad
 
     return bprop
 
@@ -598,6 +610,19 @@ def get_bprop_tanh(self):
     return bprop
 
 
+@bprop_getters.register(G.TanhGrad)
+def get_bprop_tanh_grad(self):
+    """Grad definition for `TanhGrad` operation."""
+    tanh_grad = G.TanhGrad()
+
+    def bprop(y, grad, out, dout):
+        dy = dout * -2.0 * grad * y
+        dgrad = tanh_grad(y, dout)
+        return dy, dgrad
+
+    return bprop
+
+
 @bprop_getters.register(P.Gelu)
 def get_bprop_gelu(self):
     """Grad definition for `Gelu` operation."""

tests/st/ops/gpu/test_acosh_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.ops.operations import _grad_ops as G
+from mindspore.ops import composite as C
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+class NetAcoshGrad(nn.Cell):
+    def __init__(self):
+        super(NetAcoshGrad, self).__init__()
+        self.acosh_grad = G.AcoshGrad()
+
+    def construct(self, y, grad):
+        return self.acosh_grad(y, grad)
+
+
+class NetAcoshGradGrad(nn.Cell):
+    def __init__(self, forward_net):
+        super(NetAcoshGradGrad, self).__init__()
+        self.forward_net = forward_net
+        self.gradOps = C.GradOperation(get_all=True, sens_param=True)
+
+    def construct(self, y, grad, dout):
+        backward_net = self.gradOps(self.forward_net)
+        return backward_net(y, grad, dout)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def acosh_grad_grad_base(dtype, loss):
+    np.random.seed(1)
+    shape = (4, 2)
+    y_np = (np.random.rand(*shape) * 10).astype(dtype)
+    grad_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+    dout_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+
+    y_np_32 = y_np.astype(np.float32)
+    grad_np_32 = grad_np.astype(np.float32)
+    dout_np_32 = dout_np.astype(np.float32)
+    out_np_32 = grad_np_32 / np.sinh(y_np_32)
+    dy_np = (dout_np_32 * out_np_32 * (-1.0) / np.tanh(y_np_32)).astype(dtype)
+    dgrad_np = (dout_np_32 / np.sinh(y_np_32)).astype(dtype)
+
+    y_ms = Tensor(y_np)
+    grad_ms = Tensor(grad_np)
+    dout_ms = Tensor(dout_np)
+    forward_net = NetAcoshGrad()
+    net = NetAcoshGradGrad(forward_net)
+    dy_ms, dgrad_ms = net(y_ms, grad_ms, dout_ms)
+
+    assert np.allclose(dy_ms.asnumpy(), dy_np, loss, loss)
+    assert np.allclose(dgrad_ms.asnumpy(), dgrad_np, loss, loss)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_acosh_grad_grad_float16():
+    acosh_grad_grad_base(np.float16, 2e-3)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_acosh_grad_grad_float32():
+    acosh_grad_grad_base(np.float32, 1e-4)

tests/st/ops/gpu/test_asinh_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.ops.operations import _grad_ops as G
+from mindspore.ops import composite as C
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+class NetAsinhGrad(nn.Cell):
+    def __init__(self):
+        super(NetAsinhGrad, self).__init__()
+        self.asinh_grad = G.AsinhGrad()
+
+    def construct(self, y, grad):
+        return self.asinh_grad(y, grad)
+
+
+class NetAsinhGradGrad(nn.Cell):
+    def __init__(self, forward_net):
+        super(NetAsinhGradGrad, self).__init__()
+        self.forward_net = forward_net
+        self.gradOps = C.GradOperation(get_all=True, sens_param=True)
+
+    def construct(self, y, grad, dout):
+        backward_net = self.gradOps(self.forward_net)
+        return backward_net(y, grad, dout)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def asinh_grad_grad_base(dtype, loss):
+    np.random.seed(1)
+    shape = (4, 2)
+    y_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+    grad_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+    dout_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+
+    y_np_32 = y_np.astype(np.float32)
+    grad_np_32 = grad_np.astype(np.float32)
+    dout_np_32 = dout_np.astype(np.float32)
+    out_np_32 = grad_np_32 / np.cosh(y_np_32)
+    dy_np = (dout_np_32 * out_np_32 * (-1.0) * np.tanh(y_np_32)).astype(dtype)
+    dgrad_np = (dout_np_32 / np.cosh(y_np_32)).astype(dtype)
+
+    y_ms = Tensor(y_np)
+    grad_ms = Tensor(grad_np)
+    dout_ms = Tensor(dout_np)
+    forward_net = NetAsinhGrad()
+    net = NetAsinhGradGrad(forward_net)
+    dy_ms, dgrad_ms = net(y_ms, grad_ms, dout_ms)
+
+    assert np.allclose(dy_ms.asnumpy(), dy_np, loss, loss)
+    assert np.allclose(dgrad_ms.asnumpy(), dgrad_np, loss, loss)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_asinh_grad_grad_float16():
+    asinh_grad_grad_base(np.float16, 1e-3)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_asinh_grad_grad_float32():
+    asinh_grad_grad_base(np.float32, 1e-4)

tests/st/ops/gpu/test_atan_grad_grad_op.py

@@ -0,0 +1,87 @@
+# 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
+from mindspore.ops import composite as C
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+class NetAtanGrad(nn.Cell):
+    def __init__(self):
+        super(NetAtanGrad, self).__init__()
+        self.atan_grad = G.AtanGrad()
+
+    def construct(self, x, grad):
+        return self.atan_grad(x, grad)
+
+
+class NetAtanGradGrad(nn.Cell):
+    def __init__(self, forward_net):
+        super(NetAtanGradGrad, self).__init__()
+        self.forward_net = forward_net
+        self.gradOps = C.GradOperation(get_all=True, sens_param=True)
+
+    def construct(self, x, grad, dout):
+        backward_net = self.gradOps(self.forward_net)
+        return backward_net(x, grad, dout)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def atan_grad_grad_base(dtype, loss):
+    np.random.seed(1)
+    shape = (4, 2)
+    x_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+    grad_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+    dout_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+
+    x_np_32 = x_np.astype(np.float32)
+    grad_np_32 = grad_np.astype(np.float32)
+    dout_np_32 = dout_np.astype(np.float32)
+    out_np_32 = grad_np_32 / (1 + x_np_32 * x_np_32)
+    dgrad_np_32 = dout_np_32 / (1 + x_np_32 * x_np_32)
+    dx_np = (out_np_32 * dgrad_np_32 * (-2.0) * x_np_32).astype(dtype)
+    dgrad_np = dgrad_np_32.astype(dtype)
+
+    x_ms = Tensor(x_np)
+    grad_ms = Tensor(grad_np)
+    dout_ms = Tensor(dout_np)
+    forward_net = NetAtanGrad()
+    net = NetAtanGradGrad(forward_net)
+    dx_ms, dgrad_ms = net(x_ms, grad_ms, dout_ms)
+
+    assert np.allclose(dx_ms.asnumpy(), dx_np, loss, loss)
+    assert np.allclose(dgrad_ms.asnumpy(), dgrad_np, loss, loss)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_atan_grad_grad_float16():
+    atan_grad_grad_base(np.float16, 1e-3)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_atan_grad_grad_float32():
+    atan_grad_grad_base(np.float32, 1e-4)

tests/st/ops/gpu/test_relu_grad_grad_op.py

@@ -0,0 +1,115 @@
+# 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
+from mindspore.ops import composite as C
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+class NetReluGrad(nn.Cell):
+    def __init__(self):
+        super(NetReluGrad, self).__init__()
+        self.reluGrad = G.ReluGrad()
+
+    def construct(self, grad, y):
+        return self.reluGrad(grad, y)
+
+
+class NetReluGradGrad(nn.Cell):
+    def __init__(self, forward_net):
+        super(NetReluGradGrad, self).__init__()
+        self.forward_net = forward_net
+        self.gradOps = C.GradOperation(get_all=True, sens_param=True)
+
+    def construct(self, grad, y, dout):
+        backward_net = self.gradOps(self.forward_net)
+        return backward_net(grad, y, dout)
+
+
+def relu_grad_grad_base(dtype, loss):
+    np.random.seed(1)
+    shape = (4, 2)
+    y_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+    y_np[y_np < 0] = 0
+    grad_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+    dout_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+
+    y_np_32 = y_np.astype(np.float32)
+    dout_np_32 = dout_np.astype(np.float32)
+    dgrad_np = ((y_np_32 > 0) * dout_np_32).astype(dtype)
+
+    y_ms = Tensor(y_np)
+    grad_ms = Tensor(grad_np)
+    dout_ms = Tensor(dout_np)
+    relu_grad = NetReluGrad()
+    net = NetReluGradGrad(relu_grad)
+    dgrad_ms, _ = net(grad_ms, y_ms, dout_ms)
+
+    assert np.allclose(dgrad_ms.asnumpy(), dgrad_np, loss, loss)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_relu_grad_grad_float16():
+    relu_grad_grad_base(np.float16, 1e-3)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_relu_grad_grad_float32():
+    relu_grad_grad_base(np.float32, 1e-4)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_relu_grad_grad_float64():
+    relu_grad_grad_base(np.float64, 1e-5)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_relu_grad_grad_int8():
+    relu_grad_grad_base(np.int8, 1e-5)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_relu_grad_grad_int16():
+    relu_grad_grad_base(np.int16, 1e-5)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_relu_grad_grad_int32():
+    relu_grad_grad_base(np.int32, 1e-5)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_relu_grad_grad_int64():
+    relu_grad_grad_base(np.int64, 1e-5)

tests/st/ops/gpu/test_sqrt_grad_grad_op.py

@@ -0,0 +1,87 @@
+# 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
+from mindspore.ops import composite as C
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+class NetSqrtGrad(nn.Cell):
+    def __init__(self):
+        super(NetSqrtGrad, self).__init__()
+        self.sqrt_grad = G.SqrtGrad()
+
+    def construct(self, y, grad):
+        return self.sqrt_grad(y, grad)
+
+
+class NetSqrtGradGrad(nn.Cell):
+    def __init__(self, forward_net):
+        super(NetSqrtGradGrad, self).__init__()
+        self.forward_net = forward_net
+        self.gradOps = C.GradOperation(get_all=True, sens_param=True)
+
+    def construct(self, y, grad, dout):
+        backward_net = self.gradOps(self.forward_net)
+        return backward_net(y, grad, dout)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def sqrt_grad_grad_base(dtype, loss):
+    np.random.seed(1)
+    shape = (4, 2)
+    y_np = (np.random.rand(*shape) * 10).astype(dtype)
+    grad_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+    dout_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+
+    y_np_32 = y_np.astype(np.float32)
+    grad_np_32 = grad_np.astype(np.float32)
+    dout_np_32 = dout_np.astype(np.float32)
+    gy_np_32 = dout_np_32 / y_np_32
+    out_np_32 = 0.5 * grad_np_32 / y_np_32
+    dy_np = (-gy_np_32 * out_np_32).astype(dtype)
+    dgrad_np = (0.5 * gy_np_32).astype(dtype)
+
+    y_ms = Tensor(y_np)
+    grad_ms = Tensor(grad_np)
+    dout_ms = Tensor(dout_np)
+    forward_net = NetSqrtGrad()
+    net = NetSqrtGradGrad(forward_net)
+    dy_ms, dgrad_ms = net(y_ms, grad_ms, dout_ms)
+
+    assert np.allclose(dy_ms.asnumpy(), dy_np, loss, loss)
+    assert np.allclose(dgrad_ms.asnumpy(), dgrad_np, loss, loss)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sqrt_grad_grad_float16():
+    sqrt_grad_grad_base(np.float16, 1e-3)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sqrt_grad_grad_float32():
+    sqrt_grad_grad_base(np.float32, 1e-4)

tests/st/ops/gpu/test_tanh_grad_grad_op.py

@@ -0,0 +1,85 @@
+# 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
+from mindspore.ops import composite as C
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+class NetTanhGrad(nn.Cell):
+    def __init__(self):
+        super(NetTanhGrad, self).__init__()
+        self.tanh_grad = G.TanhGrad()
+
+    def construct(self, y, grad):
+        return self.tanh_grad(y, grad)
+
+
+class NetTanhGradGrad(nn.Cell):
+    def __init__(self, forward_net):
+        super(NetTanhGradGrad, self).__init__()
+        self.forward_net = forward_net
+        self.gradOps = C.GradOperation(get_all=True, sens_param=True)
+
+    def construct(self, y, grad, dout):
+        backward_net = self.gradOps(self.forward_net)
+        return backward_net(y, grad, dout)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def tanh_grad_grad_base(dtype, loss):
+    np.random.seed(1)
+    shape = (4, 2)
+    y_np = (np.random.rand(*shape) * 2 - 1).astype(dtype)
+    grad_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+    dout_np = (np.random.rand(*shape) * 20 - 10).astype(dtype)
+
+    y_np_32 = y_np.astype(np.float32)
+    grad_np_32 = grad_np.astype(np.float32)
+    dout_np_32 = dout_np.astype(np.float32)
+    dy_np = (dout_np_32 * grad_np_32 * (-2.0) * y_np_32).astype(dtype)
+    dgrad_np = (dout_np_32 * (1 - y_np_32 * y_np_32)).astype(dtype)
+
+    y_ms = Tensor(y_np)
+    grad_ms = Tensor(grad_np)
+    dout_ms = Tensor(dout_np)
+    forward_net = NetTanhGrad()
+    net = NetTanhGradGrad(forward_net)
+    dy_ms, dgrad_ms = net(y_ms, grad_ms, dout_ms)
+
+    assert np.allclose(dy_ms.asnumpy(), dy_np, loss, loss)
+    assert np.allclose(dgrad_ms.asnumpy(), dgrad_np, loss, loss)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_tanh_grad_grad_float16():
+    tanh_grad_grad_base(np.float16, 1e-3)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_tanh_grad_grad_float32():
+    tanh_grad_grad_base(np.float32, 1e-4)