forked from mindspore-Ecosystem/mindspore
205 lines
7.7 KiB
Python
205 lines
7.7 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 grad in pynative 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 ms_function
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from mindspore.ops.functional import grad
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context.set_context(mode=context.PYNATIVE_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_grad_function(x, y, z):
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return x**2*y*z
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@ms_function
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def grad_warp_with_msfunction(x, y, z):
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output = grad(function)(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_grad_single_input_single_output_cell_pynative():
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"""
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Features: Function grad.
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Description: Test F.grad with single input and single output net in pynative 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_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
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real_grad = grad(net)(x)
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assert np.allclose(real_grad.asnumpy(), expect_grad.asnumpy())
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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_single_input_multiple_outputs_cell_pynative():
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"""
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Features: Function grad.
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Description: Test F.grad with single input and multiple outputs net in pynative 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_grad = Tensor(np.array([[5, 14], [29, 50]]).astype(np.float32))
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real_grad = grad(net)(x)
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assert np.allclose(real_grad.asnumpy(), expect_grad.asnumpy())
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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_multiple_inputs_single_output_cell_pynative():
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"""
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Features: Function grad.
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Description: Test F.grad with multiple inputs and single output net in pynative 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_grad1 = Tensor(np.array([[0, 6], [15, -4]]).astype(np.float32))
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expect_grad2 = Tensor(np.array([[-2, 6], [-3, 8]]).astype(np.float32))
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real_grad = grad(net, grad_position=(1, 2))(x, y, z)
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assert isinstance(real_grad, tuple)
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assert len(real_grad) == 2
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assert np.allclose(real_grad[0].asnumpy(), expect_grad1.asnumpy())
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assert np.allclose(real_grad[1].asnumpy(), expect_grad2.asnumpy())
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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_multiple_inputs_multiple_outputs_cell_pynative():
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"""
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Features: Function grad.
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Description: Test F.grad with multiple inputs and multiple outputs net in pynative 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_grad1 = Tensor(np.array([[-4, 12], [13, 0]]).astype(np.float32))
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expect_grad2 = Tensor(np.array([[-2, 12], [7, 6]]).astype(np.float32))
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real_grad = grad(net, grad_position=(1, 2))(x, y, z)
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assert isinstance(real_grad, tuple)
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assert len(real_grad) == 2
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assert np.allclose(real_grad[0].asnumpy(), expect_grad1.asnumpy())
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assert np.allclose(real_grad[1].asnumpy(), expect_grad2.asnumpy())
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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_function_with_sens_pynative():
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"""
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Features: Function grad.
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Description: Test F.grad with function setting sens_param in pynative 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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v = Tensor(np.array([[-1, 3], [2, 1]]).astype(np.float32))
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expect_grad1 = Tensor(np.array([[4, 36], [26, 0]]).astype(np.float32))
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expect_grad2 = Tensor(np.array([[2, 36], [14, 6]]).astype(np.float32))
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real_grad = grad(function, grad_position=(1, 2), sens_param=True)(x, y, z, (v, v))
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assert isinstance(real_grad, tuple)
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assert len(real_grad) == 2
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assert np.allclose(real_grad[0].asnumpy(), expect_grad1.asnumpy())
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assert np.allclose(real_grad[1].asnumpy(), expect_grad2.asnumpy())
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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_iteration_function_pynative():
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"""
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Features: Function grad.
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Description: Test calling F.grad iterative with function in pynative 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_grad1 = Tensor(np.array([[0, 12], [30, -8]]).astype(np.float32))
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expect_grad2 = Tensor(np.array([[-4, 12], [-6, 16]]).astype(np.float32))
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real_grad = grad(grad(iteration_grad_function), grad_position=(1, 2))(x, y, z)
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assert isinstance(real_grad, tuple)
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assert len(real_grad) == 2
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assert np.allclose(real_grad[0].asnumpy(), expect_grad1.asnumpy())
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assert np.allclose(real_grad[1].asnumpy(), expect_grad2.asnumpy())
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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_warp_with_msfunction_pynative():
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"""
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Features: Function grad.
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Description: Test F.grad warpped with ms_function in pynative 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_grad = Tensor(np.array([[2, 13], [1, 6]]).astype(np.float32))
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real_grad = grad_warp_with_msfunction(x, y, z)
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assert np.allclose(real_grad.asnumpy(), expect_grad.asnumpy())
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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_with_grad_position_twice_pynative():
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"""
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Features: Function grad.
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Description: Test F.grad with function setting grad_position twice in pynative 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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net = MultipleInputsSingleOutputNet()
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out1 = grad(net, grad_position=0)(x, y, z)
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out2 = grad(net, grad_position=(0, 1))(x, y, z)
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assert isinstance(out1, Tensor)
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assert isinstance(out2, tuple)
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