high grad

Signed-off-by: Daniel <chentanjie@huawei.com>

tests/st/high_grad/test_highgrad_param.py

@@ -0,0 +1,75 @@
+# 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.
+# ============================================================================
+"""Test high order grad with respect to parameter first, then input."""
+
+import pytest
+import numpy as np
+import mindspore.nn as nn
+import mindspore.ops as ops
+from mindspore import Tensor, context
+from mindspore import ParameterTuple, Parameter
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.mul = ops.Mul()
+        weight_np = np.array([2, 2]).astype(np.float32)
+        self.weight = Parameter(Tensor(weight_np), name="weight", requires_grad=True)
+
+    def construct(self, x):
+        x_square = self.mul(x, x)
+        x_square_z = self.mul(x_square, self.weight)
+        output = self.mul(x_square_z, self.weight)
+        return output
+
+
+class Grad(nn.Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = ops.GradOperation(get_by_list=True, sens_param=False)
+        self.network = network
+        self.params = ParameterTuple(network.trainable_params())
+
+    def construct(self, x):
+        output = self.grad(self.network, self.params)(x)
+        return output
+
+
+class GradSec(nn.Cell):
+    def __init__(self, network):
+        super(GradSec, self).__init__()
+        self.grad = ops.GradOperation(get_all=True, sens_param=False)
+        self.network = network
+
+    def construct(self, x):
+        output = self.grad(self.network)(x)
+        return output
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_sit_high_order_grad_params():
+    context.set_context(mode=context.GRAPH_MODE)
+    x = Tensor(np.array([1, 1]).astype(np.float32))
+    net = Net()
+    first_grad = Grad(net)
+    second_grad = GradSec(first_grad)
+    grad = second_grad(x)
+    assert (grad[0].asnumpy() == np.array([8, 8])).all()