mindspore/tests/st/sparse/test_row.py

109 lines
3.6 KiB
Python

# Copyright 2022 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.
# ============================================================================
"""smoke tests for RowTensor operations"""
import pytest
import numpy as np
from mindspore import Tensor, RowTensor, nn, context
from mindspore.common import dtype as mstype
def compare_row(row1, row2):
assert isinstance(row1, RowTensor)
assert isinstance(row2, RowTensor)
assert (row1.indices.asnumpy() == row1.indices.asnumpy()).all()
assert (row2.values.asnumpy() == row2.values.asnumpy()).all()
assert row1.dense_shape == row2.dense_shape
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_make_row():
"""
Feature: Test RowTensor Constructor in Graph and PyNative.
Description: Test RowTensor(indices, values, shape) and RowTensor(COOTensor)
Expectation: Success.
"""
indices = Tensor([0, 1], dtype=mstype.int32)
values = Tensor([[1, 2], [3, 4]], dtype=mstype.float32)
dense_shape = (3, 2)
def test_pynative():
return RowTensor(indices, values, dense_shape)
row1 = test_pynative()
compare_row(row1, row1)
row2 = RowTensor(row_tensor=row1)
compare_row(row1, row2)
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_coo_tensor_with_control_if():
"""
Feature: Test COOTensor in if.
Description: Test COOTensor computation in while loop.
Expectation: Success.
"""
class RowTensorValuesDouble(nn.Cell):
def construct(self, x):
indices = x.indices
values = x.values * 2
shape = x.dense_shape
return RowTensor(indices, values, shape)
class RowTensorValuesAdd2(nn.Cell):
def construct(self, x):
indices = x.indices
values = x.values + 2
shape = x.dense_shape
return RowTensor(indices, values, shape)
class RowTensorWithControlIf(nn.Cell):
def __init__(self, shape):
super(RowTensorWithControlIf, self).__init__()
self.op1 = RowTensorValuesDouble()
self.op2 = RowTensorValuesAdd2()
self.shape = shape
def construct(self, a, b, indices, values):
x = RowTensor(indices, values, self.shape)
if a > b:
x = self.op1(x)
else:
x = self.op2(x)
return x.indices, x.values, x.dense_shape
context.set_context(mode=context.PYNATIVE_MODE)
a = Tensor(0, mstype.int32)
b = Tensor(2, mstype.int32)
indices = Tensor([0, 1], dtype=mstype.int32)
values = Tensor([[1, 2], [3, 4]], dtype=mstype.float32)
shape = (3, 2)
net = RowTensorWithControlIf(shape)
out = net(a, b, indices, values)
assert np.allclose(out[0].asnumpy(), indices.asnumpy(), .0, .0)
assert np.allclose(out[1].asnumpy(), values.asnumpy() + 2, .0, .0)
assert out[2] == shape