forked from mindspore-Ecosystem/mindspore
339 lines
13 KiB
Python
339 lines
13 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 jvp 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 jit
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from mindspore.ops.functional import jvp
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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 SingleInputMultipleOutputNet(nn.Cell):
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def construct(self, x):
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return x**3, 2*x
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class MultipleInputSingleOutputNet(nn.Cell):
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def construct(self, x, y):
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return 2*x + 3*y
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class MultipleInputMultipleOutputNet(nn.Cell):
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def construct(self, x, y):
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return 2*x, y**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_jvp_single_input_single_output_default_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with single input, single output and default v 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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v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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net = SingleInputSingleOutputNet()
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expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
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expect_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
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primal, grad = jvp(net, x, v)
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(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_jvp_single_input_single_output_custom_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with single input, single output and custom v 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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v = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = SingleInputSingleOutputNet()
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expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
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expect_grad = Tensor(np.array([[3, 24], [81, 192]]).astype(np.float32))
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primal, grad = jvp(net, x, v)
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(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_jvp_single_input_multiple_outputs_default_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with single input, multiple outputs and default v 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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v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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net = SingleInputMultipleOutputNet()
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expect_primal_0 = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
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expect_primal_1 = Tensor(np.array([[2, 4], [6, 8]]).astype(np.float32))
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expect_grad_0 = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
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expect_grad_1 = Tensor(np.array([[2, 2], [2, 2]]).astype(np.float32))
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primal, grad = jvp(net, x, v)
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assert isinstance(primal, tuple)
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assert len(primal) == 2
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assert np.allclose(primal[0].asnumpy(), expect_primal_0.asnumpy())
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assert np.allclose(primal[1].asnumpy(), expect_primal_1.asnumpy())
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assert isinstance(grad, tuple)
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assert len(grad) == 2
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assert np.allclose(grad[0].asnumpy(), expect_grad_0.asnumpy())
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assert np.allclose(grad[1].asnumpy(), expect_grad_1.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_jvp_single_input_multiple_outputs_custom_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with single input, multiple outputs and custom v 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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v = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = SingleInputMultipleOutputNet()
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expect_primal_0 = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
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expect_primal_1 = Tensor(np.array([[2, 4], [6, 8]]).astype(np.float32))
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expect_grad_0 = Tensor(np.array([[3, 24], [81, 192]]).astype(np.float32))
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expect_grad_1 = Tensor(np.array([[2, 4], [6, 8]]).astype(np.float32))
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primal, grad = jvp(net, x, v)
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assert isinstance(primal, tuple)
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assert len(primal) == 2
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assert np.allclose(primal[0].asnumpy(), expect_primal_0.asnumpy())
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assert np.allclose(primal[1].asnumpy(), expect_primal_1.asnumpy())
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assert isinstance(grad, tuple)
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assert len(grad) == 2
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assert np.allclose(grad[0].asnumpy(), expect_grad_0.asnumpy())
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assert np.allclose(grad[1].asnumpy(), expect_grad_1.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_jvp_multiple_inputs_multiple_outputs_default_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with multiple inputs, multiple outputs and default v 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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v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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net = MultipleInputMultipleOutputNet()
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expect_primal_0 = Tensor(np.array([[2, 4], [6, 8]]).astype(np.float32))
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expect_primal_1 = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
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expect_grad_0 = Tensor(np.array([[2, 2], [2, 2]]).astype(np.float32))
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expect_grad_1 = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
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primal, grad = jvp(net, (x, y), (v, v))
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assert isinstance(primal, tuple)
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assert len(primal) == 2
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assert np.allclose(primal[0].asnumpy(), expect_primal_0.asnumpy())
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assert np.allclose(primal[1].asnumpy(), expect_primal_1.asnumpy())
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assert isinstance(grad, tuple)
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assert len(grad) == 2
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assert np.allclose(grad[0].asnumpy(), expect_grad_0.asnumpy())
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assert np.allclose(grad[1].asnumpy(), expect_grad_1.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_jvp_multiple_inputs_multiple_outputs_custom_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with multiple inputs, multiple outputs and custom v 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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v1 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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v2 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = MultipleInputMultipleOutputNet()
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expect_primal_0 = Tensor(np.array([[2, 4], [6, 8]]).astype(np.float32))
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expect_primal_1 = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
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expect_grad_0 = Tensor(np.array([[2, 2], [2, 2]]).astype(np.float32))
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expect_grad_1 = Tensor(np.array([[3, 24], [81, 192]]).astype(np.float32))
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primal, grad = jvp(net, (x, y), (v1, v2))
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assert isinstance(primal, tuple)
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assert len(primal) == 2
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assert np.allclose(primal[0].asnumpy(), expect_primal_0.asnumpy())
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assert np.allclose(primal[1].asnumpy(), expect_primal_1.asnumpy())
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assert isinstance(grad, tuple)
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assert len(grad) == 2
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assert np.allclose(grad[0].asnumpy(), expect_grad_0.asnumpy())
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assert np.allclose(grad[1].asnumpy(), expect_grad_1.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_jvp_multiple_inputs_single_output_default_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with multiple inputs, single output and default v 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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v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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net = MultipleInputSingleOutputNet()
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expect_primal = Tensor(np.array([[5, 10], [15, 20]]).astype(np.float32))
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expect_grad = Tensor(np.array([[5, 5], [5, 5]]).astype(np.float32))
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primal, grad = jvp(net, (x, y), (v, v))
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(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_jvp_multiple_inputs_single_output_custom_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with multiple inputs, single output and custom v 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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v1 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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v2 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = MultipleInputSingleOutputNet()
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expect_primal = Tensor(np.array([[5, 10], [15, 20]]).astype(np.float32))
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expect_grad = Tensor(np.array([[5, 8], [11, 14]]).astype(np.float32))
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primal, grad = jvp(net, (x, y), (v1, v2))
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(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_jvp_jit_function_single_input_single_output_default_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with @jit decorated function, single input, single output and default v 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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v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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net = SingleInputSingleOutputNet()
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@jit
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def jvp_with_jit_function(inputs, vectors):
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output, jvp_grad = jvp(net, inputs, vectors)
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return output, jvp_grad
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primal, grad = jvp_with_jit_function(x, v)
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expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
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expect_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(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_jvp_input_function_single_input_single_output_default_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with function, single input, single output and default v 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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v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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def test_function(inputs):
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return inputs**3
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primal, grad = jvp(test_function, x, v)
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expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
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expect_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(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_jvp_construct_single_input_single_output_default_v_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with Cell construct, single input, single output and default v 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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v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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class Net(nn.Cell):
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def __init__(self, network):
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super(Net, self).__init__()
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self.net = network
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def construct(self, inputs, vectors):
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net_out, jvp_out = jvp(self.net, inputs, vectors)
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return net_out, jvp_out
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test_net = Net(SingleInputSingleOutputNet())
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primal, grad = test_net(x, v)
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expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
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expect_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(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_jvp_multiple_outputs_with_has_aux_pynative():
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"""
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Features: Function jvp
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Description: Test jvp with multiple inputs, multiple outputs with set_aux as True in pynative mode.
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Expectation: No exception.
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"""
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def fn(x, y):
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return 2 * x + y, y ** 3
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def fn2(*args):
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return fn(*args)
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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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v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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expect_primal = Tensor(np.array([[3, 6], [9, 12]]).astype(np.float32))
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expect_grad = Tensor(np.array([[3, 3], [3, 3]]).astype(np.float32))
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expect_aux = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
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primal, jvp_out, aux = jvp(fn2, (x, y), (v, v), has_aux=True)
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(aux.asnumpy(), expect_aux.asnumpy())
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assert np.allclose(jvp_out.asnumpy(), expect_grad.asnumpy())
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