forked from mindspore-Ecosystem/mindspore
71 lines
2.6 KiB
Python
71 lines
2.6 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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import numpy as np
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import pytest
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import mindspore
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.ops.operations.sparse_ops as sparse_ops
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from mindspore import Tensor
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.op = sparse_ops.SparseSoftmax()
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def construct(self, indices, values, shape):
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return self.op(indices, values, shape)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_net_graph():
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'''
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Feature: SparseSoftmax gpu TEST (GRAPH_MODE).
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Description: (4, 3) int64 indices, (4, ) float64 values, (3, ) int64 shape
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Expectation: The result matches expected output.
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'''
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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indices = Tensor([[0, 0, 0], [1, 1, 1], [0, 1, 1], [0, 0, 1]], dtype=mindspore.int64)
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values = Tensor([1, 2, 3, 2], dtype=mindspore.float64)
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shape = Tensor([0, 0, 0], dtype=mindspore.int64)
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expect = np.array([0.26894142, 0.73105858, 1., 1.]).astype(np.float64)
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net = Net()
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output = net(indices, values, shape)
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np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=6)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_net_pynative():
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'''
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Feature: SparseSoftmax gpu TEST (PYNATIVE_MODE).
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Description: (4, 3) int64 indices, (4, ) float32 values, (3, ) int64 shape
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Expectation: The result matches expected output.
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'''
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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indices = Tensor([[0, 0, 0], [1, 1, 1], [0, 1, 1], [0, 0, 1]], dtype=mindspore.int64)
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values = Tensor([1, 2, 3, 2], dtype=mindspore.float32)
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shape = Tensor([0, 0, 0], dtype=mindspore.int64)
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expect = np.array([0.26894142, 0.73105858, 1., 1.]).astype(np.float32)
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net = Net()
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output = net(indices, values, shape)
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np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=6)
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