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test_sparse_unary_op.py
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test_sparse_unary_op.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import unittest
import numpy as np
import paddle
from paddle.fluid.framework import convert_np_dtype_to_dtype_
class TestSparseUnary(unittest.TestCase):
def to_sparse(self, x, format):
if format == 'coo':
return x.detach().to_sparse_coo(sparse_dim=x.ndim)
elif format == 'csr':
return x.detach().to_sparse_csr()
def check_result(self, dense_func, sparse_func, format, *args):
origin_x = paddle.rand([8, 16, 32], dtype='float32')
mask = paddle.randint(0, 2, [8, 16, 32]).astype('float32')
### check sparse coo with dense ###
dense_x = origin_x * mask
sp_x = self.to_sparse(dense_x, format)
sp_x.stop_gradient = False
if len(args) == 0:
sp_out = sparse_func(sp_x)
elif len(args) == 1:
sp_out = sparse_func(sp_x, args[0])
elif len(args) == 2:
sp_out = sparse_func(sp_x, args[0], args[1])
sp_out.backward()
dense_x.stop_gradient = False
if len(args) == 0:
dense_out = dense_func(dense_x)
elif len(args) == 1:
dense_out = dense_func(dense_x, args[0])
elif len(args) == 2:
if dense_func == paddle.cast:
dense_out = dense_func(dense_x, args[1])
int_dtype = convert_np_dtype_to_dtype_(args[0])
if sp_out.is_sparse_csr():
self.assertEqual(sp_out.crows().dtype, int_dtype)
self.assertEqual(sp_out.cols().dtype, int_dtype)
elif sp_out.is_sparse_coo():
self.assertEqual(sp_out.indices().dtype, int_dtype)
else:
dense_out = dense_func(dense_x, args[0], args[1])
dense_out.backward()
# compare forward
self.assertTrue(
np.allclose(sp_out.to_dense().numpy(), dense_out.numpy()))
# compare backward
if dense_func == paddle.sqrt:
expect_grad = np.nan_to_num(dense_x.grad.numpy(), 0., 0., 0.)
else:
expect_grad = (dense_x.grad * mask).numpy()
self.assertTrue(np.allclose(sp_x.grad.to_dense().numpy(), expect_grad))
def compare_with_dense(self, dense_func, sparse_func):
self.check_result(dense_func, sparse_func, 'coo')
self.check_result(dense_func, sparse_func, 'csr')
def compare_with_dense_one_attr(self, dense_func, sparse_func, attr1):
self.check_result(dense_func, sparse_func, 'coo', attr1)
self.check_result(dense_func, sparse_func, 'csr', attr1)
def compare_with_dense_two_attr(self, dense_func, sparse_func, attr1,
attr2):
self.check_result(dense_func, sparse_func, 'coo', attr1, attr2)
self.check_result(dense_func, sparse_func, 'csr', attr1, attr2)
def test_sparse_sin(self):
self.compare_with_dense(paddle.sin, paddle.incubate.sparse.sin)
def test_sparse_tan(self):
self.compare_with_dense(paddle.tan, paddle.incubate.sparse.tan)
def test_sparse_asin(self):
self.compare_with_dense(paddle.asin, paddle.incubate.sparse.asin)
def test_sparse_atan(self):
self.compare_with_dense(paddle.atan, paddle.incubate.sparse.atan)
def test_sparse_sinh(self):
self.compare_with_dense(paddle.sinh, paddle.incubate.sparse.sinh)
def test_sparse_tanh(self):
self.compare_with_dense(paddle.tanh, paddle.incubate.sparse.tanh)
def test_sparse_asinh(self):
self.compare_with_dense(paddle.asinh, paddle.incubate.sparse.asinh)
def test_sparse_atanh(self):
self.compare_with_dense(paddle.atanh, paddle.incubate.sparse.atanh)
def test_sparse_sqrt(self):
self.compare_with_dense(paddle.sqrt, paddle.incubate.sparse.sqrt)
def test_sparse_square(self):
self.compare_with_dense(paddle.square, paddle.incubate.sparse.square)
def test_sparse_log1p(self):
self.compare_with_dense(paddle.log1p, paddle.incubate.sparse.log1p)
def test_sparse_relu(self):
self.compare_with_dense(paddle.nn.ReLU(),
paddle.incubate.sparse.nn.ReLU())
def test_sparse_relu6(self):
self.compare_with_dense(paddle.nn.ReLU6(),
paddle.incubate.sparse.nn.ReLU6())
def test_sparse_leaky_relu(self):
self.compare_with_dense(paddle.nn.LeakyReLU(0.1),
paddle.incubate.sparse.nn.LeakyReLU(0.1))
def test_sparse_abs(self):
self.compare_with_dense(paddle.abs, paddle.incubate.sparse.abs)
def test_sparse_expm1(self):
self.compare_with_dense(paddle.expm1, paddle.incubate.sparse.expm1)
def test_sparse_deg2rad(self):
self.compare_with_dense(paddle.deg2rad, paddle.incubate.sparse.deg2rad)
def test_sparse_rad2deg(self):
self.compare_with_dense(paddle.rad2deg, paddle.incubate.sparse.rad2deg)
def test_sparse_neg(self):
self.compare_with_dense(paddle.neg, paddle.incubate.sparse.neg)
def test_sparse_pow(self):
self.compare_with_dense_one_attr(paddle.pow, paddle.incubate.sparse.pow,
3)
def test_sparse_mul_scalar(self):
self.compare_with_dense_one_attr(paddle.Tensor.__mul__,
paddle.incubate.sparse.multiply, 3)
def test_sparse_div_scalar(self):
self.compare_with_dense_one_attr(paddle.Tensor.__div__,
paddle.incubate.sparse.divide, 2)
def test_sparse_cast(self):
self.compare_with_dense_two_attr(paddle.cast,
paddle.incubate.sparse.cast, 'int16',
'float32')
self.compare_with_dense_two_attr(paddle.cast,
paddle.incubate.sparse.cast, 'int32',
'float64')
if __name__ == "__main__":
unittest.main()