forked from mindspore-Ecosystem/mindspore
245 lines
9.3 KiB
Python
245 lines
9.3 KiB
Python
# Copyright 2021 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 function jacfwd in graph mode"""
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import numpy as np
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import pytest
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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 Tensor
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from mindspore import jit
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from mindspore.ops import jacfwd
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context.set_context(mode=context.GRAPH_MODE)
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class SingleInputSingleOutputNet(nn.Cell):
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def construct(self, x):
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return x ** 3
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class SingleInputMultipleOutputsNet(nn.Cell):
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def construct(self, x):
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return x ** 3, 2 * x
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class MultipleInputsSingleOutputNet(nn.Cell):
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def construct(self, x, y, z):
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return x * y * z
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class MultipleInputsMultipleOutputsNet(nn.Cell):
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def construct(self, x, y, z):
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return x ** 2 + y ** 2 + z ** 2, x * y * z
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def function(x, y, z):
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return x ** 2 + y ** 2 + z ** 2, x * y * z
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def iteration_jac_function(x, y, z):
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return x ** 2 * y * z
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@jit
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def jac_wrap_with_jit_function(x, y, z):
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output = jacfwd(function, has_aux=True)(x, y, z)
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return output
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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_jac_single_input_single_output_cell_graph():
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"""
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Features: Function jacfwd.
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Description: Test ops.jacfwd with single input and single output net in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = SingleInputSingleOutputNet()
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expect_jac = np.array([[[[3, 0], [0, 0]], [[0, 12], [0, 0]]],
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[[[0, 0], [27, 0]], [[0, 0], [0, 48]]]]).astype(np.float32)
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jac = jacfwd(net)(x)
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assert np.allclose(jac.asnumpy(), expect_jac)
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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_jac_single_input_multiple_outputs_cell_graph():
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"""
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Features: Function jacfwd.
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Description: Test ops.jacfwd with single input and multiple outputs net in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = SingleInputMultipleOutputsNet()
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expect_jac_0 = np.array([[[[3, 0], [0, 0]], [[0, 12], [0, 0]]],
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[[[0, 0], [27, 0]], [[0, 0], [0, 48]]]]).astype(np.float32)
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expect_jac_1 = np.array([[[[2, 0], [0, 0]], [[0, 2], [0, 0]]],
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[[[0, 0], [2, 0]], [[0, 0], [0, 2]]]]).astype(np.float32)
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jac = jacfwd(net)(x)
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assert np.allclose(jac[0].asnumpy(), expect_jac_0)
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assert np.allclose(jac[1].asnumpy(), expect_jac_1)
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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_jac_multiple_inputs_single_output_cell_graph():
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"""
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Features: Function jacfwd.
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Description: Test ops.jacfwd with multiple inputs and single output net in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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y = Tensor(np.array([[-2, 3], [-1, 2]]).astype(np.float32))
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z = Tensor(np.array([[0, 3], [5, -1]]).astype(np.float32))
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net = MultipleInputsSingleOutputNet()
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expect_jac_0 = np.array([[[[0, 0], [0, 0]], [[0, 6], [0, 0]]],
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[[[0, 0], [15, 0]], [[0, 0], [0, -4]]]]).astype(np.float32)
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expect_jac_1 = np.array([[[[-2, 0], [0, 0]], [[0, 6], [0, 0]]],
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[[[0, 0], [-3, 0]], [[0, 0], [0, 8]]]]).astype(np.float32)
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jac = jacfwd(net, grad_position=(1, 2))(x, y, z)
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assert isinstance(jac, tuple)
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assert len(jac) == 2
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assert np.allclose(jac[0].asnumpy(), expect_jac_0)
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assert np.allclose(jac[1].asnumpy(), expect_jac_1)
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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_jac_multiple_inputs_multiple_outputs_cell_graph():
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"""
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Features: Function jacfwd.
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Description: Test ops.jacfwd with multiple inputs and multiple outputs net in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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y = Tensor(np.array([[-2, 3], [-1, 2]]).astype(np.float32))
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z = Tensor(np.array([[0, 3], [5, -1]]).astype(np.float32))
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net = MultipleInputsMultipleOutputsNet()
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expect_jac_0 = np.array([[[[-4, 0], [0, 0]], [[0, 6], [0, 0]]],
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[[[0, 0], [-2, 0]], [[0, 0], [0, 4]]]]).astype(np.float32)
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expect_jac_1 = np.array([[[[0, 0], [0, 0]], [[0, 6], [0, 0]]],
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[[[0, 0], [10, 0]], [[0, 0], [0, -2]]]]).astype(np.float32)
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expect_jac_2 = np.array([[[[0, 0], [0, 0]], [[0, 6], [0, 0]]],
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[[[0, 0], [15, 0]], [[0, 0], [0, -4]]]]).astype(np.float32)
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expect_jac_3 = np.array([[[[-2, 0], [0, 0]], [[0, 6], [0, 0]]],
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[[[0, 0], [-3, 0]], [[0, 0], [0, 8]]]]).astype(np.float32)
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jac = jacfwd(net, grad_position=(1, 2))(x, y, z)
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assert isinstance(jac, tuple)
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assert len(jac) == 2
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assert np.allclose(jac[0][0].asnumpy(), expect_jac_0)
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assert np.allclose(jac[0][1].asnumpy(), expect_jac_1)
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assert np.allclose(jac[1][0].asnumpy(), expect_jac_2)
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assert np.allclose(jac[1][1].asnumpy(), expect_jac_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_jac_wrap_with_jit_function_graph():
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"""
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Features: Function jacfwd.
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Description: Test ops.jacfwd warpped with @jit decorated function in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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y = Tensor(np.array([[-2, 3], [-1, 2]]).astype(np.float32))
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z = Tensor(np.array([[0, 3], [5, -1]]).astype(np.float32))
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expect_jac = np.array([[[[2, 0], [0, 0]], [[0, 4], [0, 0]]],
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[[[0, 0], [6, 0]], [[0, 0], [0, 8]]]]).astype(np.float32)
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expect_aux = np.array([[0, 18], [-15, -8]]).astype(np.float32)
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jac, aux = jac_wrap_with_jit_function(x, y, z)
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assert np.allclose(jac.asnumpy(), expect_jac)
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assert np.allclose(aux.asnumpy(), expect_aux)
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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_jac_with_grad_position_twice_graph():
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"""
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Features: Function jacfwd.
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Description: Test ops.jacfwd with function setting grad_position twice in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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y = Tensor(np.array([[1, 3], [5, 7]]).astype(np.float32))
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z = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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expect_jac_0 = np.array([[[[1, 0], [0, 0]], [[0, 3], [0, 0]]],
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[[[0, 0], [5, 0]], [[0, 0], [0, 7]]]]).astype(np.float32)
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expect_jac_1 = np.array([[[[1, 0], [0, 0]], [[0, 2], [0, 0]]],
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[[[0, 0], [3, 0]], [[0, 0], [0, 4]]]]).astype(np.float32)
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net = MultipleInputsSingleOutputNet()
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jac1 = jacfwd(net, grad_position=0)(x, y, z)
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jac2 = jacfwd(net, grad_position=(0, 1))(x, y, z)
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assert np.allclose(jac1.asnumpy(), expect_jac_0)
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assert np.allclose(jac2[1].asnumpy(), expect_jac_1)
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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_jac_with_has_aux_graph():
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"""
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Features: Function jacfwd.
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Description: Test ops.jacfwd with Cell setting grad_position in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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z = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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expect_jac = np.array([[[[2, 0], [0, 0]], [[0, 4], [0, 0]]],
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[[[0, 0], [6, 0]], [[0, 0], [0, 8]]]]).astype(np.float32)
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expect_aux = np.array([[1, 4], [9, 16]]).astype(np.float32)
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net = MultipleInputsMultipleOutputsNet()
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jac, aux = jacfwd(net, grad_position=0, has_aux=True)(x, y, z)
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assert np.allclose(jac.asnumpy(), expect_jac)
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assert np.allclose(aux.asnumpy(), expect_aux)
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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_jac_with_function_has_aux_graph():
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"""
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Features: Function jacfwd.
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Description: Test ops.jacfwd with function setting grad_position in graph mode.
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Expectation: No exception.
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"""
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def fn(x, y, z):
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return x ** 2 + y ** 2 + z ** 2, x * y * z
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def fn2(*args):
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x = args[0]
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y = args[1]
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z = args[2]
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return fn(x, y, z)
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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z = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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expect_jac = np.array([[[[2, 0], [0, 0]], [[0, 4], [0, 0]]],
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[[[0, 0], [6, 0]], [[0, 0], [0, 8]]]]).astype(np.float32)
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expect_aux = np.array([[1, 4], [9, 16]]).astype(np.float32)
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jac, aux = jacfwd(fn2, grad_position=0, has_aux=True)(x, y, z)
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assert np.allclose(jac.asnumpy(), expect_jac)
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assert np.allclose(aux.asnumpy(), expect_aux)
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