forked from mindspore-Ecosystem/mindspore
143 lines
5.3 KiB
Python
143 lines
5.3 KiB
Python
# Copyright 2022 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 taylor differentiation in pynative mode"""
|
|
import pytest
|
|
import numpy as np
|
|
import mindspore.nn as nn
|
|
import mindspore.context as context
|
|
from mindspore.ops import operations as P
|
|
from mindspore import Tensor
|
|
from mindspore.ops.functional import jet, derivative
|
|
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
|
|
|
|
class SingleInputSingleOutputNet(nn.Cell):
|
|
def __init__(self):
|
|
super(SingleInputSingleOutputNet, self).__init__()
|
|
self.exp = P.Exp()
|
|
self.cos = P.Cos()
|
|
self.sin = P.Sin()
|
|
|
|
def construct(self, x):
|
|
out1 = self.sin(x)
|
|
out2 = self.cos(out1)
|
|
out3 = self.exp(out2)
|
|
out = out1 + out2 - out3
|
|
return out
|
|
|
|
|
|
class MultipleInputSingleOutputNet(nn.Cell):
|
|
def __init__(self):
|
|
super(MultipleInputSingleOutputNet, self).__init__()
|
|
self.exp = P.Exp()
|
|
self.cos = P.Cos()
|
|
self.sin = P.Sin()
|
|
|
|
def construct(self, x, y):
|
|
out1 = self.sin(x)
|
|
out2 = self.cos(y)
|
|
out3 = out1 * out2 + out1 / out2
|
|
out = self.exp(out3)
|
|
return out
|
|
|
|
|
|
@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
|
|
@pytest.mark.env_onecard
|
|
def test_jet_multiple_input_single_output_pynative_mode():
|
|
"""
|
|
Features: Function jet
|
|
Description: Test jet with multiple inputs in pynative mode.
|
|
Expectation: No exception.
|
|
"""
|
|
series = (Tensor([[1., 1.], [0., 0.], [0., 0.]]), Tensor([[1., 1.], [0., 0.], [0., 0.]]))
|
|
primals = (Tensor([1., 1.]), Tensor([1., 1.]))
|
|
net = MultipleInputSingleOutputNet()
|
|
expected_primals = np.array([7.47868, 7.47868]).astype(np.float32)
|
|
expected_series = np.array([[22.50614, 22.50614], [133.92517, 133.92517], [1237.959, 1237.959]]).astype(np.float32)
|
|
out_primals, out_series = jet(net, primals, series)
|
|
assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
|
|
assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
|
|
|
|
|
|
@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
|
|
@pytest.mark.env_onecard
|
|
def test_derivative_multiple_input_single_output_pynative_mode():
|
|
"""
|
|
Features: Function derivative
|
|
Description: Test derivative with multiple inputs in pynative mode.
|
|
Expectation: No exception.
|
|
"""
|
|
primals = (Tensor([1., 1.]), Tensor([1., 1.]))
|
|
order = 3
|
|
net = MultipleInputSingleOutputNet()
|
|
expected_primals = np.array([7.47868, 7.47868]).astype(np.float32)
|
|
expected_series = np.array([1237.959, 1237.959]).astype(np.float32)
|
|
out_primals, out_series = derivative(net, primals, order)
|
|
assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
|
|
assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
|
|
|
|
|
|
@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
|
|
@pytest.mark.env_onecard
|
|
def test_jet_single_input_single_output_pynative_mode():
|
|
"""
|
|
Features: Function jet
|
|
Description: Test jet with single input in pynative mode.
|
|
Expectation: No exception.
|
|
"""
|
|
primals = Tensor([1., 1.])
|
|
series = Tensor([[1., 1.], [0., 0.], [0., 0.]])
|
|
net = SingleInputSingleOutputNet()
|
|
expected_primals = np.array([-0.43931, -0.43931]).astype(np.float32)
|
|
expected_series = np.array([[0.92187, 0.92187], [-1.56750, -1.56750], [-0.74808, -0.74808]]).astype(np.float32)
|
|
out_primals, out_series = jet(net, primals, series)
|
|
assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
|
|
assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
|
|
|
|
|
|
@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
|
|
@pytest.mark.env_onecard
|
|
def test_derivative_single_input_single_output_pynative_mode():
|
|
"""
|
|
Features: Function derivative
|
|
Description: Test derivative with single input in pynative mode.
|
|
Expectation: No exception.
|
|
"""
|
|
primals = Tensor([1., 1.])
|
|
order = 3
|
|
net = SingleInputSingleOutputNet()
|
|
expected_primals = np.array([-0.43931, -0.43931]).astype(np.float32)
|
|
expected_series = np.array([-0.74808, -0.74808]).astype(np.float32)
|
|
out_primals, out_series = derivative(net, primals, order)
|
|
assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
|
|
assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
|