forked from mindspore-Ecosystem/mindspore
109 lines
3.6 KiB
Python
109 lines
3.6 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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"""smoke tests for RowTensor operations"""
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import pytest
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import numpy as np
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from mindspore import Tensor, RowTensor, nn, context
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from mindspore.common import dtype as mstype
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def compare_row(row1, row2):
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assert isinstance(row1, RowTensor)
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assert isinstance(row2, RowTensor)
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assert (row1.indices.asnumpy() == row1.indices.asnumpy()).all()
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assert (row2.values.asnumpy() == row2.values.asnumpy()).all()
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assert row1.dense_shape == row2.dense_shape
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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_make_row():
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"""
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Feature: Test RowTensor Constructor in Graph and PyNative.
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Description: Test RowTensor(indices, values, shape) and RowTensor(COOTensor)
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Expectation: Success.
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"""
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indices = Tensor([0, 1], dtype=mstype.int32)
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values = Tensor([[1, 2], [3, 4]], dtype=mstype.float32)
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dense_shape = (3, 2)
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def test_pynative():
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return RowTensor(indices, values, dense_shape)
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row1 = test_pynative()
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compare_row(row1, row1)
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row2 = RowTensor(row_tensor=row1)
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compare_row(row1, row2)
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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.env_onecard
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def test_coo_tensor_with_control_if():
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"""
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Feature: Test COOTensor in if.
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Description: Test COOTensor computation in while loop.
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Expectation: Success.
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"""
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class RowTensorValuesDouble(nn.Cell):
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def construct(self, x):
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indices = x.indices
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values = x.values * 2
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shape = x.dense_shape
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return RowTensor(indices, values, shape)
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class RowTensorValuesAdd2(nn.Cell):
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def construct(self, x):
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indices = x.indices
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values = x.values + 2
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shape = x.dense_shape
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return RowTensor(indices, values, shape)
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class RowTensorWithControlIf(nn.Cell):
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def __init__(self, shape):
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super(RowTensorWithControlIf, self).__init__()
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self.op1 = RowTensorValuesDouble()
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self.op2 = RowTensorValuesAdd2()
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self.shape = shape
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def construct(self, a, b, indices, values):
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x = RowTensor(indices, values, self.shape)
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if a > b:
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x = self.op1(x)
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else:
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x = self.op2(x)
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return x.indices, x.values, x.dense_shape
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context.set_context(mode=context.PYNATIVE_MODE)
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a = Tensor(0, mstype.int32)
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b = Tensor(2, mstype.int32)
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indices = Tensor([0, 1], dtype=mstype.int32)
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values = Tensor([[1, 2], [3, 4]], dtype=mstype.float32)
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shape = (3, 2)
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net = RowTensorWithControlIf(shape)
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out = net(a, b, indices, values)
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assert np.allclose(out[0].asnumpy(), indices.asnumpy(), .0, .0)
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assert np.allclose(out[1].asnumpy(), values.asnumpy() + 2, .0, .0)
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assert out[2] == shape
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