70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""test vmap in pynative mode"""
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import pytest
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import numpy as np
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import mindspore.context as context
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import mindspore.ops.functional as F
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from mindspore import dtype as mstype
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from mindspore.common import Tensor
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from mindspore.ops.functional import vmap
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from mindspore.common.api import ms_function
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context.set_context(mode=context.PYNATIVE_MODE)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_vmap_nested():
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"""
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Feature: vmap
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Description: This case mainly tests the following `vmap` application scenarios in PyNative mode:
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1.Calling nested `vmap` functions.
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2.`fn` is a function wrapped `ms_function`.
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3.Function contains free variables.
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Expectation: success
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"""
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outter_tensor = Tensor([1], mstype.float32)
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def add_fn(x):
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return F.add(x, outter_tensor)
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@ms_function
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def inner_vmap_fn(x, outter_tensor):
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vmap_funtion = vmap(add_fn, 1)
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out = vmap_funtion(x)
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output = out + outter_tensor
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return output
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def outter_vmap_fn(x):
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output = vmap(inner_vmap_fn, (0, None), 1)(x, outter_tensor)
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return output
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x_hat = Tensor([[[1., 2., 3.], [4., 5., 6.]],
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[[2., 3., 4.], [5., 6., 7.]],
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[[3., 4., 5.], [6., 7., 8.]],
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[[4., 5., 6.], [7., 8., 9.]]], mstype.float32)
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result = outter_vmap_fn(x_hat)
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expect_result = Tensor([[[3., 6.], [4., 7.], [5., 8.], [6., 9.]],
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[[4., 7.], [5., 8.], [6., 9.], [7., 10.]],
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[[5., 8.], [6., 9.], [7., 10.], [8., 11.]]], mstype.float32)
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assert np.allclose(result.asnumpy(), expect_result.asnumpy())
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