forked from mindspore-Ecosystem/mindspore
119 lines
3.5 KiB
Python
119 lines
3.5 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 primitive cache """
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import pytest
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore import jit
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from mindspore.ops import operations as P
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from mindspore.ops._primitive_cache import _get_cache_prim
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# pylint: disable=W0235
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_jit_function_run_in_pynative():
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"""
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Feature: test @jit decorated function run in PyNative.
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Description: test @jit decorated function run in PyNative.
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Expectation: Success.
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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@jit
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def pow_function(x, y):
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_pow = _get_cache_prim(P.Pow)()
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return _pow(x, y)
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class Pow(nn.Cell):
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def __init__(self):
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super(Pow, self).__init__()
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def construct(self, x1, x2):
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return pow_function(x1, x2)
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x = Tensor(np.array([1.0, 2.0, 4.0]), ms.float32)
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y = 3
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output = Pow()(x, y)
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expect_output = np.array([1.0, 8.0, 64.0], dtype=np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_run_pynative_and_then_run_graph():
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"""
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Feature: test the cache key must be a str.
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Description: test run_pynative and then run_graph.
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Expectation: Success.
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"""
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class Pow(nn.Cell):
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def __init__(self):
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super(Pow, self).__init__()
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def construct(self, x1, x2):
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_pow = _get_cache_prim(P.Pow)()
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return _pow(x1, x2)
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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x = Tensor(np.array([1.0, 2.0, 4.0]), ms.float32)
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y = 3
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output1 = Pow()(x, y)
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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x = Tensor(np.array([1.0, 2.0, 4.0]), ms.float32)
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y = 3
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output2 = Pow()(x, y)
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np.testing.assert_almost_equal(output1.asnumpy(), output2.asnumpy())
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_continuous_cache():
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"""
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Feature: test continuous cache.
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Description: test continuous cache.
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Expectation: Success.
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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class AddSub(nn.Cell):
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def __init__(self):
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super(AddSub, self).__init__()
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def construct(self, x, y):
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y = y + 1
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add = _get_cache_prim(P.Add)()
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sub = _get_cache_prim(P.Sub)()
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z = add(x, y)
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out = sub(z, x)
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return out
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x = Tensor(np.array([2, 2, 1]), dtype=ms.int32)
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y = Tensor(np.array([1, 1, 1]), dtype=ms.int32)
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output = AddSub()(x, y)
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expect_output = np.array([2, 2, 2], dtype=np.int32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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