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- # Copyright 2021 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 function grad in pynative mode"""
- import numpy as np
- import mindspore.nn as nn
- import mindspore.context as context
- from mindspore import Tensor
- from mindspore.ops.functional import grad
-
- context.set_context(mode=context.PYNATIVE_MODE)
-
-
- class SingleInputSingleOutputNet(nn.Cell):
- def construct(self, x):
- return x**3
-
-
- class MultipleInputsMultipleOutputsNet(nn.Cell):
- def construct(self, x, y, z):
- return x**2 + y**2 + z**2, x*y*z
-
-
- def function(x, y, z):
- return x**2 + y**2 + z**2, x*y*z
-
-
- def test_grad_single_input_single_output_cell_pynative():
- """
- Features: Function grad.
- Description: Test F.grad with single input and single output net in pynative mode.
- Expectation: No exception.
- """
- x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
- net = SingleInputSingleOutputNet()
- grad(net)(x)
-
-
- def test_grad_multiple_inputs_multiple_outputs_cell_pynative():
- """
- Features: Function grad.
- Description: Test F.grad with multiple inputs and multiple outputs net in pynative mode.
- Expectation: No exception.
- """
- x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
- y = Tensor(np.array([[-2, 3], [-1, 2]]).astype(np.float32))
- z = Tensor(np.array([[0, 3], [5, -1]]).astype(np.float32))
- net = MultipleInputsMultipleOutputsNet()
- grad(net, grad_position=(1, 2))(x, y, z)
-
-
- def test_grad_function_with_sens_pynative():
- """
- Features: Function grad.
- Description: Test F.grad with function setting sens_param in pynative mode.
- Expectation: No exception.
- """
- x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
- y = Tensor(np.array([[-2, 3], [-1, 2]]).astype(np.float32))
- z = Tensor(np.array([[0, 3], [5, -1]]).astype(np.float32))
- v = Tensor(np.array([[-1, 3], [2, 1]]).astype(np.float32))
- grad(function, grad_position=(1, 2), sens_param=True)(x, y, z, (v, v))
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