forked from mindspore-Ecosystem/mindspore
286 lines
10 KiB
Python
286 lines
10 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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"""test taylor differentiation in graph mode"""
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import pytest
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import numpy as np
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore import ops
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from mindspore import Tensor
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from mindspore.ops.functional import jet, derivative
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context.set_context(mode=context.GRAPH_MODE)
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class MultipleInputSingleOutputNet(nn.Cell):
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def __init__(self):
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super(MultipleInputSingleOutputNet, self).__init__()
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self.sin = ops.Sin()
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self.cos = ops.Cos()
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self.exp = ops.Exp()
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def construct(self, x, y):
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out1 = self.sin(x)
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out2 = self.cos(y)
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out3 = out1 * out2 + out1 / out2
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out = self.exp(out3)
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return out
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class MultipleInputMultipleOutputNet(nn.Cell):
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def __init__(self):
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super(MultipleInputMultipleOutputNet, self).__init__()
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self.sin = ops.Sin()
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self.cos = ops.Cos()
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def construct(self, x, y):
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out1 = self.sin(x)
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out2 = self.cos(y)
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return out1, out2
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class SingleInputSingleOutputNet(nn.Cell):
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def __init__(self):
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super(SingleInputSingleOutputNet, self).__init__()
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self.sin = ops.Sin()
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self.cos = ops.Cos()
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self.exp = ops.Exp()
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def construct(self, x):
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out1 = self.sin(x)
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out2 = self.cos(out1)
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out3 = self.exp(out2)
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out = out1 + out2 - out3
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return out
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def function_graph(x):
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y = ops.exp(x)
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z = ops.tan(y)
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return z
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class SingleInputSingleOutputWithScalarNet(nn.Cell):
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def __init__(self):
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super(SingleInputSingleOutputWithScalarNet, self).__init__()
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self.log = ops.Log()
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def construct(self, x):
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out1 = self.log(x)
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out = 1 / out1 + 2
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return out * 3
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_jet_single_input_single_output_graph_mode():
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"""
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Features: Function jet
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Description: Test jet with single input in graph mode.
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Expectation: No exception.
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"""
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primals = Tensor([1., 1.])
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series = Tensor([[1., 1.], [0., 0.], [0., 0.]])
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net = SingleInputSingleOutputNet()
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expected_primals = np.array([-0.43931, -0.43931]).astype(np.float32)
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expected_series = np.array([[0.92187, 0.92187], [-1.56750, -1.56750], [-0.74808, -0.74808]]).astype(np.float32)
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out_primals, out_series = jet(net, primals, series)
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assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
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assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_jet_single_input_single_output_with_scalar_graph_mode():
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"""
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Features: Function jet
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Description: Test jet with single input with scalar in graph mode.
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Expectation: No exception.
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"""
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primals = Tensor([2., 2.])
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series = Tensor([[1., 1.], [0., 0.], [0., 0.]])
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net = SingleInputSingleOutputWithScalarNet()
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out_primals, out_series = jet(net, primals, series)
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expected_primals = np.array([10.328085, 10.328085]).astype(np.float32)
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expected_series = np.array([[-3.1220534, -3.1220534], [6.0652323, 6.0652323],
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[-18.06463, -18.06463]]).astype(np.float32)
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assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
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assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_derivative_single_input_single_output_graph_mode():
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"""
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Features: Function derivative
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Description: Test derivative with single input in graph mode.
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Expectation: No exception.
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"""
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primals = Tensor([1., 1.])
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order = 3
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net = SingleInputSingleOutputNet()
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expected_primals = np.array([-0.43931, -0.43931]).astype(np.float32)
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expected_series = np.array([-0.74808, -0.74808]).astype(np.float32)
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out_primals, out_series = derivative(net, primals, order)
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assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
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assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_jet_multiple_input_single_output_graph_mode():
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"""
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Features: Function jet
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Description: Test jet with multiple inputs in graph mode.
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Expectation: No exception.
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"""
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primals = (Tensor([1., 1.]), Tensor([1., 1.]))
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series = (Tensor([[1., 1.], [0., 0.], [0., 0.]]), Tensor([[1., 1.], [0., 0.], [0., 0.]]))
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net = MultipleInputSingleOutputNet()
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expected_primals = np.array([7.47868, 7.47868]).astype(np.float32)
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expected_series = np.array([[22.50614, 22.50614], [133.92517, 133.92517], [1237.959, 1237.959]]).astype(np.float32)
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out_primals, out_series = jet(net, primals, series)
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assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
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assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_derivative_multiple_input_single_output_graph_mode():
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"""
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Features: Function derivative
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Description: Test derivative with multiple inputs in graph mode.
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Expectation: No exception.
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"""
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primals = (Tensor([1., 1.]), Tensor([1., 1.]))
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order = 3
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net = MultipleInputSingleOutputNet()
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expected_primals = np.array([7.47868, 7.47868]).astype(np.float32)
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expected_series = np.array([1237.959, 1237.959]).astype(np.float32)
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out_primals, out_series = derivative(net, primals, order)
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assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
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assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_jet_construct_graph_mode():
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"""
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Features: Function jet
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Description: Test jet in construct with multiple inputs in graph mode.
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Expectation: No exception.
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"""
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class Net(nn.Cell):
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def __init__(self, net):
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super(Net, self).__init__()
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self.net = net
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def construct(self, x, y):
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res_primals, res_series = jet(self.net, x, y)
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return res_primals, res_series
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primals = Tensor([2., 2.])
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series = Tensor([[1., 1.], [0., 0.], [0., 0.]])
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net = SingleInputSingleOutputWithScalarNet()
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hod_net = Net(net)
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expected_primals = np.array([10.328085, 10.328085]).astype(np.float32)
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expected_series = np.array([[-3.1220534, -3.1220534], [6.0652323, 6.0652323],
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[-18.06463, -18.06463]]).astype(np.float32)
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out_primals, out_series = hod_net(primals, series)
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assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
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assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_derivative_construct_graph_mode():
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"""
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Features: Function derivative
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Description: Test derivative in construct with multiple inputs in graph mode.
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Expectation: No exception.
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"""
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class Net(nn.Cell):
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def __init__(self, net, order):
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super(Net, self).__init__()
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self.net = net
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self.order = order
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def construct(self, x, y):
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res_primals, res_series = derivative(self.net, (x, y), self.order)
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return res_primals, res_series
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primals_x = Tensor([1., 1.])
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primals_y = Tensor([1., 1.])
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net = MultipleInputMultipleOutputNet()
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hod_net = Net(net, order=3)
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expected_primals_x = np.array([0.841470957, 0.841470957]).astype(np.float32)
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expected_primals_y = np.array([0.540302277, 0.540302277]).astype(np.float32)
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expected_series_x = np.array([-0.540302277, -0.540302277]).astype(np.float32)
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expected_series_y = np.array([0.841470957, 0.841470957]).astype(np.float32)
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out_primals, out_series = hod_net(primals_x, primals_y)
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assert np.allclose(out_primals[0].asnumpy(), expected_primals_x, atol=1.e-4)
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assert np.allclose(out_primals[1].asnumpy(), expected_primals_y, atol=1.e-4)
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assert np.allclose(out_series[0].asnumpy(), expected_series_x, atol=1.e-4)
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assert np.allclose(out_series[1].asnumpy(), expected_series_y, atol=1.e-4)
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_jet_function_graph_mode():
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"""
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Features: Function jet
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Description: Test function in graph mode.
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Expectation: No exception.
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"""
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primals = Tensor([1., 1.])
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series = Tensor([[1., 1.], [0., 0.], [0., 0.]])
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out_primals, out_series = jet(function_graph, primals, series)
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expected_primals = np.array([-0.450549, -0.450549]).astype(np.float32)
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expected_series = np.array([[3.270079, 3.270079], [-4.739784, -4.739784],
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[56.995613, 56.995613]]).astype(np.float32)
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assert np.allclose(out_series.asnumpy(), expected_series, atol=1.e-4)
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assert np.allclose(out_primals.asnumpy(), expected_primals, atol=1.e-4)
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