forked from huawei/mindspore2022
!22509 Add ut and st for Jvp, Vjp and Grad
Merge pull request !22509 from LiangZhibo/fix
This commit is contained in:
Gitee
commit
ff2e34b9f9
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# 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 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.nn.grad import Vjp
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context.set_context(mode=context.PYNATIVE_MODE)
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class SingleInputNet(nn.Cell):
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def construct(self, x):
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return x**3
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class MultipleInputsOutputNet(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_vjp_single_input_graph():
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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 = SingleInputNet()
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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 = Vjp(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_vjp_multiple_inputs_default_v_graph():
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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 = MultipleInputsOutputNet()
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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 = Vjp(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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@ -0,0 +1,148 @@
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# 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 jvp 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.nn.grad import Jvp
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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 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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def test_jvp_single_input_single_output_default_v_graph():
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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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Jvp(net)(x, v)
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def test_jvp_single_input_single_output_custom_v_graph():
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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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Jvp(net)(x, v)
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def test_jvp_single_input_multiple_outputs_default_v_graph():
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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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Jvp(net)(x, v)
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def test_jvp_single_input_multiple_outputs_custom_v_graph():
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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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Jvp(net)(x, v)
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def test_jvp_multiple_inputs_single_output_default_v_graph():
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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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Jvp(net)(x, y, (v, v))
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def test_jvp_multiple_inputs_single_output_custom_v_graph():
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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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Jvp(net)(x, y, (v1, v2))
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def test_jvp_multiple_inputs_multiple_outputs_default_v_graph():
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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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Jvp(net)(x, y, (v, v))
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def test_jvp_multiple_inputs_multiple_outputs_custom_v_graph():
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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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Jvp(net)(x, y, (v1, v2))
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def test_jvp_wrong_input_v_graph():
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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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with pytest.raises(TypeError):
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Jvp(net)(x, (v, v))
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def test_jvp_wrong_input_v_2_graph():
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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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with pytest.raises(TypeError):
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Jvp(net)(x, (v,))
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def test_jvp_wrong_input_graph():
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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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with pytest.raises(TypeError):
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Jvp(net)(x, x, v)
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def test_jvp_wrong_input_2_graph():
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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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with pytest.raises(TypeError):
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Jvp(net)((x, y), (v, v))
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def test_jvp_wrong_input_3_graph():
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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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with pytest.raises(TypeError):
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Jvp(net)(x, y, v)
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@ -0,0 +1,147 @@
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# 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 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.nn.grad 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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def test_jvp_single_input_single_output_default_v_pynative():
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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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Jvp(net)(x, v)
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def test_jvp_single_input_single_output_custom_v_pynative():
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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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Jvp(net)(x, v)
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def test_jvp_single_input_multiple_outputs_default_v_pynative():
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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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Jvp(net)(x, v)
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def test_jvp_single_input_multiple_outputs_custom_v_pynative():
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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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Jvp(net)(x, v)
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def test_jvp_multiple_inputs_multiple_outputs_default_v_pynative():
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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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Jvp(net)(x, y, (v, v))
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def test_jvp_multiple_inputs_multiple_outputs_custom_v_pynative():
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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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Jvp(net)(x, y, (v1, v2))
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def test_jvp_multiple_inputs_single_output_default_v_pynative():
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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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Jvp(net)(x, y, (v, v))
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def test_jvp_multiple_inputs_single_output_custom_v_pynative():
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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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Jvp(net)(x, y, (v1, v2))
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def test_jvp_wrong_input_v_pynative():
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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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with pytest.raises(TypeError):
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Jvp(net)(x, (v, v))
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def test_jvp_wrong_input_v_2_pynative():
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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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with pytest.raises(TypeError):
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Jvp(net)(x, (v,))
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def test_jvp_wrong_input_pynative():
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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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with pytest.raises(TypeError):
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Jvp(net)(x, x, v)
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def test_jvp_wrong_input_2_pynative():
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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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with pytest.raises(TypeError):
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Jvp(net)((x, y), (v, v))
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def test_jvp_wrong_input_3_pynative():
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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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with pytest.raises(TypeError):
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Jvp(net)(x, y, v)
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@ -0,0 +1,82 @@
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# 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.
|
||||
# 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.
|
||||
# ============================================================================
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"""test jvp 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.nn.grad import Vjp
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context.set_context(mode=context.GRAPH_MODE)
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class SingleInputNet(nn.Cell):
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def construct(self, x):
|
||||
return x**3
|
||||
|
||||
|
||||
class MultipleInputsOutputNet(nn.Cell):
|
||||
def construct(self, x, y):
|
||||
return 2*x, y**3
|
||||
|
||||
|
||||
def test_vjp_single_input_graph():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = SingleInputNet()
|
||||
Vjp(net)(x, v)
|
||||
|
||||
|
||||
def test_vjp_multiple_inputs_default_v_graph():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = MultipleInputsOutputNet()
|
||||
Vjp(net)(x, y, (v, v))
|
||||
|
||||
|
||||
def test_vjp_wrong_input_v_graph():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = SingleInputNet()
|
||||
with pytest.raises(TypeError):
|
||||
Vjp(net)(x, (v, v))
|
||||
|
||||
|
||||
def test_vjp_wrong_input_v_2_graph():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = SingleInputNet()
|
||||
with pytest.raises(TypeError):
|
||||
Vjp(net)(x, (v,))
|
||||
|
||||
|
||||
def test_vjp_wrong_input_graph():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = SingleInputNet()
|
||||
with pytest.raises(TypeError):
|
||||
Vjp(net)(x, y, v)
|
||||
|
||||
|
||||
def test_vjp_wrong_input_2_graph():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = MultipleInputsOutputNet()
|
||||
with pytest.raises(TypeError):
|
||||
Vjp(net)((x, y), (v, v))
|
||||
|
|
@ -0,0 +1,82 @@
|
|||
# Copyright 2021 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 jvp in pynative mode"""
|
||||
import numpy as np
|
||||
import pytest
|
||||
import mindspore.nn as nn
|
||||
import mindspore.context as context
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.grad import Vjp
|
||||
|
||||
context.set_context(mode=context.PYNATIVE_MODE)
|
||||
|
||||
|
||||
class SingleInputNet(nn.Cell):
|
||||
def construct(self, x):
|
||||
return x**3
|
||||
|
||||
|
||||
class MultipleInputsOutputNet(nn.Cell):
|
||||
def construct(self, x, y):
|
||||
return 2*x, y**3
|
||||
|
||||
|
||||
def test_vjp_single_input_pynative():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = SingleInputNet()
|
||||
Vjp(net)(x, v)
|
||||
|
||||
|
||||
def test_vjp_multiple_inputs_default_v_pynative():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = MultipleInputsOutputNet()
|
||||
Vjp(net)(x, y, (v, v))
|
||||
|
||||
|
||||
def test_vjp_wrong_input_v_pynative():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = SingleInputNet()
|
||||
with pytest.raises(TypeError):
|
||||
Vjp(net)(x, (v, v))
|
||||
|
||||
|
||||
def test_vjp_wrong_input_v_2_pynative():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = SingleInputNet()
|
||||
with pytest.raises(TypeError):
|
||||
Vjp(net)(x, (v,))
|
||||
|
||||
|
||||
def test_vjp_wrong_input_pynative():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = SingleInputNet()
|
||||
with pytest.raises(TypeError):
|
||||
Vjp(net)(x, y, v)
|
||||
|
||||
|
||||
def test_vjp_wrong_input_2_pynative():
|
||||
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
||||
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
||||
net = MultipleInputsOutputNet()
|
||||
with pytest.raises(TypeError):
|
||||
Vjp(net)((x, y), (v, v))
|
||||
Loading…
Reference in New Issue