forked from mindspore-Ecosystem/mindspore
151 lines
4.7 KiB
Python
151 lines
4.7 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import pytest
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import numpy as np
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from mindspore.ops import operations as P
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import mindspore.nn as nn
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from mindspore.common.parameter import Parameter
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from tests.mindspore_test_framework.utils.check_gradient import (
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check_jacobian, Tensor, OperationGradChecker, check_gradient, NNGradChecker)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_operation_grad_checker():
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"""
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Feature: Auto diff.
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Description: Check the result for GradOperation.
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Expectation: The result is expected.
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"""
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.matmul = P.MatMul()
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self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
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def construct(self, x, y):
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x = x * self.z
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out = self.matmul(x, y)
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return out
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check_gradient(Net(), Tensor(np.array([[0.65, 0.8, 0.8]], np.float32)),
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Tensor(np.array([[0.1], [0.2], [-.1]], np.float32)), grad_checker_class=OperationGradChecker,
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input_selector=[1], sampling_times=2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_grad_checker_primitive():
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"""
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Feature: Auto diff.
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Description: Check the result for GradOperation.
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Expectation: The result is expected.
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"""
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matmul = P.MatMul()
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def prim_f(x, y):
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return matmul(x, y)
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check_gradient(prim_f, Tensor(np.array([[0.65, 0.8, 0.8]], np.float32)),
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Tensor(np.array([[0.1], [0.2], [-.1]], np.float32)),
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grad_checker_class=OperationGradChecker, sampling_times=2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_nn_jacobian_checker():
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"""
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Feature: Auto diff.
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Description: Check the result for GradOperation.
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Expectation: The result is expected.
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"""
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.dense = nn.Dense(10, 10)
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def construct(self, x):
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out = self.dense(x)
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return out, x
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check_jacobian(Net(), Tensor(np.random.rand(1, 10).astype(np.float32)),
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delta=1e-3,
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max_error=1e-7,
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grad_checker_class=NNGradChecker,
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input_selector=[1],
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output_selector=[0])
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_nn_grad_checker():
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"""
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Feature: Auto diff.
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Description: Check the result for GradOperation.
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Expectation: The result is expected.
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"""
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.dense = nn.Dense(10, 10)
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def construct(self, x):
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out = self.dense(x)
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return out
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check_gradient(Net(), Tensor(np.random.rand(1, 10).astype(np.float32)),
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delta=1e-3,
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max_error=1e-3,
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grad_checker_class=NNGradChecker, sampling_times=3)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_operation_jacobian_checker():
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"""
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Feature: Auto diff.
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Description: Check the result for GradOperation.
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Expectation: The result is expected.
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"""
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.matmul = P.MatMul()
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self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
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def construct(self, x, y):
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x = x * self.z
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out = self.matmul(x, y)
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return x, out
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check_jacobian(Net(), Tensor(np.array([[0.65, 0.8, 0.8], [0.1, 0.2, 0.3]], np.float32)),
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Tensor(np.array([[0.1, 0.3], [0.2, 0.2], [-.1, 0.4]], np.float32)),
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grad_checker_class=OperationGradChecker, input_selector=[0],
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output_selector=[0])
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