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test_sparse_utils_op.py
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test_sparse_utils_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.
from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid.core as core
from paddle.fluid.framework import _test_eager_guard
devices = ['cpu', 'gpu']
class TestSparseCreate(unittest.TestCase):
def test_create_coo_by_tensor(self):
with _test_eager_guard():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
dense_indices = paddle.to_tensor(indices)
dense_elements = paddle.to_tensor(values, dtype='float32')
coo = paddle.sparse.sparse_coo_tensor(
dense_indices, dense_elements, dense_shape, stop_gradient=False)
# test the to_string.py
print(coo)
assert np.array_equal(indices, coo.indices().numpy())
assert np.array_equal(values, coo.values().numpy())
def test_create_coo_by_np(self):
with _test_eager_guard():
indices = [[0, 1, 2], [1, 2, 0]]
values = [1.0, 2.0, 3.0]
dense_shape = [3, 3]
coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
assert np.array_equal(indices, coo.indices().numpy())
assert np.array_equal(values, coo.values().numpy())
def test_create_csr_by_tensor(self):
with _test_eager_guard():
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
dense_crows = paddle.to_tensor(crows)
dense_cols = paddle.to_tensor(cols)
dense_elements = paddle.to_tensor(values, dtype='float32')
stop_gradient = False
csr = paddle.sparse.sparse_csr_tensor(
dense_crows,
dense_cols,
dense_elements,
dense_shape,
stop_gradient=stop_gradient)
def test_create_csr_by_np(self):
with _test_eager_guard():
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
csr = paddle.sparse.sparse_csr_tensor(crows, cols, values,
dense_shape)
# test the to_string.py
print(csr)
assert np.array_equal(crows, csr.crows().numpy())
assert np.array_equal(cols, csr.cols().numpy())
assert np.array_equal(values, csr.values().numpy())
def test_place(self):
with _test_eager_guard():
place = core.CPUPlace()
indices = [[0, 1], [0, 1]]
values = [1.0, 2.0]
dense_shape = [2, 2]
coo = paddle.sparse.sparse_coo_tensor(
indices, values, dense_shape, place=place)
assert coo.place.is_cpu_place()
assert coo.values().place.is_cpu_place()
assert coo.indices().place.is_cpu_place()
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
csr = paddle.sparse.sparse_csr_tensor(
crows, cols, values, [3, 5], place=place)
assert csr.place.is_cpu_place()
assert csr.crows().place.is_cpu_place()
assert csr.cols().place.is_cpu_place()
assert csr.values().place.is_cpu_place()
def test_dtype(self):
with _test_eager_guard():
indices = [[0, 1], [0, 1]]
values = [1.0, 2.0]
dense_shape = [2, 2]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
coo = paddle.sparse.sparse_coo_tensor(
indices, values, dense_shape, dtype='float64')
assert coo.dtype == paddle.float64
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
csr = paddle.sparse.sparse_csr_tensor(
crows, cols, values, [3, 5], dtype='float16')
assert csr.dtype == paddle.float16
def test_create_coo_no_shape(self):
with _test_eager_guard():
indices = [[0, 1], [0, 1]]
values = [1.0, 2.0]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
coo = paddle.sparse.sparse_coo_tensor(indices, values)
assert [2, 2] == coo.shape
class TestSparseConvert(unittest.TestCase):
def test_to_sparse_coo(self):
with _test_eager_guard():
x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]]
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
dense_x = paddle.to_tensor(x, dtype='float32', stop_gradient=False)
out = dense_x.to_sparse_coo(2)
assert np.array_equal(out.indices().numpy(), indices)
assert np.array_equal(out.values().numpy(), values)
#test to_sparse_coo_grad backward
out_grad_indices = [[0, 1], [0, 1]]
out_grad_values = [2.0, 3.0]
out_grad = paddle.sparse.sparse_coo_tensor(
paddle.to_tensor(out_grad_indices),
paddle.to_tensor(out_grad_values),
shape=out.shape,
stop_gradient=True)
out.backward(out_grad)
assert np.array_equal(dense_x.grad.numpy(),
out_grad.to_dense().numpy())
def test_coo_to_dense(self):
with _test_eager_guard():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
sparse_x = paddle.sparse.sparse_coo_tensor(
paddle.to_tensor(indices),
paddle.to_tensor(values),
shape=[3, 4],
stop_gradient=False)
dense_tensor = sparse_x.to_dense()
#test to_dense_grad backward
out_grad = [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]]
dense_tensor.backward(paddle.to_tensor(out_grad))
#mask the out_grad by sparse_x.indices()
correct_x_grad = [2.0, 4.0, 7.0, 9.0, 10.0]
assert np.array_equal(correct_x_grad,
sparse_x.grad.values().numpy())
paddle.device.set_device("cpu")
sparse_x_cpu = paddle.sparse.sparse_coo_tensor(
paddle.to_tensor(indices),
paddle.to_tensor(values),
shape=[3, 4],
stop_gradient=False)
dense_tensor_cpu = sparse_x_cpu.to_dense()
dense_tensor_cpu.backward(paddle.to_tensor(out_grad))
assert np.array_equal(correct_x_grad,
sparse_x_cpu.grad.values().numpy())
def test_to_sparse_csr(self):
with _test_eager_guard():
x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]]
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5]
dense_x = paddle.to_tensor(x)
out = dense_x.to_sparse_csr()
assert np.array_equal(out.crows().numpy(), crows)
assert np.array_equal(out.cols().numpy(), cols)
assert np.array_equal(out.values().numpy(), values)
dense_tensor = out.to_dense()
assert np.array_equal(dense_tensor.numpy(), x)
def test_coo_values_grad(self):
with _test_eager_guard():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
sparse_x = paddle.sparse.sparse_coo_tensor(
paddle.to_tensor(indices),
paddle.to_tensor(values),
shape=[3, 4],
stop_gradient=False)
values_tensor = sparse_x.values()
out_grad = [2.0, 3.0, 5.0, 8.0, 9.0]
# test coo_values_grad
values_tensor.backward(paddle.to_tensor(out_grad))
assert np.array_equal(out_grad, sparse_x.grad.values().numpy())
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0],
[5.0, 5.0]]
sparse_x = paddle.sparse.sparse_coo_tensor(
paddle.to_tensor(indices),
paddle.to_tensor(values),
shape=[3, 4, 2],
stop_gradient=False)
values_tensor = sparse_x.values()
out_grad = [[2.0, 2.0], [3.0, 3.0], [5.0, 5.0], [8.0, 8.0],
[9.0, 9.0]]
# test coo_values_grad
values_tensor.backward(paddle.to_tensor(out_grad))
assert np.array_equal(out_grad, sparse_x.grad.values().numpy())
def test_sparse_coo_tensor_grad(self):
with _test_eager_guard():
for device in devices:
if device == 'cpu' or (device == 'gpu' and
paddle.is_compiled_with_cuda()):
paddle.device.set_device(device)
indices = [[0, 1], [0, 1]]
values = [1, 2]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(
values, dtype='float32', stop_gradient=False)
sparse_x = paddle.sparse.sparse_coo_tensor(
indices, values, shape=[2, 2], stop_gradient=False)
grad_indices = [[0, 1], [1, 1]]
grad_values = [2, 3]
grad_indices = paddle.to_tensor(grad_indices, dtype='int32')
grad_values = paddle.to_tensor(grad_values, dtype='float32')
sparse_out_grad = paddle.sparse.sparse_coo_tensor(
grad_indices, grad_values, shape=[2, 2])
sparse_x.backward(sparse_out_grad)
correct_values_grad = [0, 3]
assert np.array_equal(correct_values_grad,
values.grad.numpy())
# test the non-zero values is a vector
values = [[1, 1], [2, 2]]
values = paddle.to_tensor(
values, dtype='float32', stop_gradient=False)
sparse_x = paddle.sparse.sparse_coo_tensor(
indices, values, shape=[2, 2, 2], stop_gradient=False)
grad_values = [[2, 2], [3, 3]]
grad_values = paddle.to_tensor(grad_values, dtype='float32')
sparse_out_grad = paddle.sparse.sparse_coo_tensor(
grad_indices, grad_values, shape=[2, 2, 2])
sparse_x.backward(sparse_out_grad)
correct_values_grad = [[0, 0], [3, 3]]
assert np.array_equal(correct_values_grad,
values.grad.numpy())
def test_sparse_coo_tensor_sorted(self):
with _test_eager_guard():
for device in devices:
if device == 'cpu' or (device == 'gpu' and
paddle.is_compiled_with_cuda()):
paddle.device.set_device(device)
#test unsorted and duplicate indices
indices = [[1, 0, 0], [0, 1, 1]]
values = [1.0, 2.0, 3.0]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
indices_sorted = [[0, 1], [1, 0]]
values_sorted = [5.0, 1.0]
assert np.array_equal(indices_sorted,
sparse_x.indices().numpy())
assert np.array_equal(values_sorted,
sparse_x.values().numpy())
# test the non-zero values is a vector
values = [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]]
values = paddle.to_tensor(values, dtype='float32')
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
values_sorted = [[5.0, 5.0], [1.0, 1.0]]
assert np.array_equal(indices_sorted,
sparse_x.indices().numpy())
assert np.array_equal(values_sorted,
sparse_x.values().numpy())
class TestCooError(unittest.TestCase):
def test_small_shape(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
indices = [[2, 3], [0, 2]]
values = [1, 2]
# 1. the shape too small
dense_shape = [2, 2]
sparse_x = paddle.sparse.sparse_coo_tensor(
indices, values, shape=dense_shape)
def test_same_nnz(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
# 2. test the nnz of indices must same as nnz of values
indices = [[1, 2], [1, 0]]
values = [1, 2, 3]
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
def test_same_dimensions(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
indices = [[1, 2], [1, 0]]
values = [1, 2, 3]
shape = [2, 3, 4]
sparse_x = paddle.sparse.sparse_coo_tensor(
indices, values, shape=shape)
def test_indices_dtype(self):
with _test_eager_guard():
with self.assertRaises(TypeError):
indices = [[1.0, 2.0], [0, 1]]
values = [1, 2]
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
class TestCsrError(unittest.TestCase):
def test_dimension1(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [0, 1, 2, 3]
cols = [0, 1, 2]
values = [1, 2, 3]
shape = [3]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
def test_dimension2(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [0, 1, 2, 3]
cols = [0, 1, 2]
values = [1, 2, 3]
shape = [3, 3, 3, 3]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
def test_same_shape1(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [0, 1, 2, 3]
cols = [0, 1, 2, 3]
values = [1, 2, 3]
shape = [3, 4]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
def test_same_shape2(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [0, 1, 2, 3]
cols = [0, 1, 2, 3]
values = [1, 2, 3, 4]
shape = [3, 4]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
def test_same_shape3(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [0, 1, 2, 3, 0, 1, 2]
cols = [0, 1, 2, 3, 0, 1, 2]
values = [1, 2, 3, 4, 0, 1, 2]
shape = [2, 3, 4]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
def test_crows_first_value(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [1, 1, 2, 3]
cols = [0, 1, 2]
values = [1, 2, 3]
shape = [3, 4]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
def test_dtype(self):
with _test_eager_guard():
with self.assertRaises(TypeError):
crows = [0, 1, 2, 3.0]
cols = [0, 1, 2]
values = [1, 2, 3]
shape = [3]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
if __name__ == "__main__":
unittest.main()