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# Copyright 2016 The TensorFlow 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.
# ==============================================================================
"""Tests for SparseAdd."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import timeit
import numpy as np
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import sparse_ops
import tensorflow.python.ops.sparse_grad # pylint: disable=unused-import
from tensorflow.python.platform import test
def _sparsify(x, thresh=0.5, index_dtype=np.int64):
x[x < thresh] = 0
non_zero = np.where(x)
x_indices = np.vstack(non_zero).astype(index_dtype).T
x_values = x[non_zero]
x_shape = x.shape
return sparse_tensor.SparseTensor(
indices=x_indices, values=x_values, dense_shape=x_shape), len(x_values)
class SparseAddTest(test.TestCase):
def _randomTensor(self, size, np_dtype, sparse=True):
n, m = size
x = np.random.randn(n, m).astype(np_dtype)
return _sparsify(x) if sparse else x
def _SparseTensorValue_3x3(self, negate=False):
# [ 1]
# [2 ]
# [3 4]
# ...or its cwise negation, if `negate`
ind = np.array([[0, 1], [1, 0], [2, 0], [2, 1]])
val = np.array([1, 2, 3, 4])
if negate:
val = -np.array([1, 2, 3, 4])
shape = np.array([3, 3])
return sparse_tensor.SparseTensorValue(
np.array(ind, np.int64),
np.array(val, np.float32), np.array(shape, np.int64))
def _SparseTensor_3x3(self, negate=False):
return sparse_tensor.SparseTensor.from_value(
self._SparseTensorValue_3x3(negate))
def _SparseTensor_3x3_v2(self):
# [ 1]
# [-1.9 ]
# [ 3 -4.2]
ind = np.array([[0, 1], [1, 0], [2, 0], [2, 1]])
val = np.array([1, -1.9, 3, -4.2])
shape = np.array([3, 3])
return sparse_tensor.SparseTensor(
constant_op.constant(ind, dtypes.int64),
constant_op.constant(val, dtypes.float32),
constant_op.constant(shape, dtypes.int64))
def testAddSelf(self):
with self.test_session(use_gpu=False) as sess:
for sp_a in (self._SparseTensorValue_3x3(), self._SparseTensor_3x3()):
for sp_b in (self._SparseTensorValue_3x3(), self._SparseTensor_3x3()):
sp_sum = sparse_ops.sparse_add(sp_a, sp_b)
self.assertAllEqual((3, 3), sp_sum.get_shape())
sum_out = sess.run(sp_sum)
self.assertEqual(sp_sum.dense_shape.get_shape(), [2])
self.assertAllEqual(sum_out.indices, [[0, 1], [1, 0], [2, 0], [2, 1]])
self.assertAllEqual(sum_out.values, [2, 4, 6, 8])
self.assertAllEqual(sum_out.dense_shape, [3, 3])
def testAddSelfAndNegation(self):
with self.test_session(use_gpu=False) as sess:
sp_a = self._SparseTensor_3x3()
sp_b = self._SparseTensor_3x3(negate=True)
sp_sum = sparse_ops.sparse_add(sp_a, sp_b, 0.1)
sum_out = sess.run(sp_sum)
self.assertEqual(sp_sum.dense_shape.get_shape(), [2])
self.assertAllEqual(sum_out.indices, np.empty([0, 2]))
self.assertAllEqual(sum_out.values, [])
self.assertAllEqual(sum_out.dense_shape, [3, 3])
def testSmallValuesShouldVanish(self):
with self.test_session(use_gpu=False) as sess:
sp_a = self._SparseTensor_3x3()
sp_b = self._SparseTensor_3x3_v2()
# sum:
# [ 2]
# [.1 ]
# [ 6 -.2]
# two values should vanish: |.1| < .21, and |-.2| < .21
sp_sum = sparse_ops.sparse_add(sp_a, sp_b, thresh=0.21)
sum_out = sess.run(sp_sum)
self.assertEqual(sp_sum.dense_shape.get_shape(), [2])
self.assertAllEqual(sum_out.indices, [[0, 1], [2, 0]])
self.assertAllEqual(sum_out.values, [2, 6])
self.assertAllEqual(sum_out.dense_shape, [3, 3])
# only .1 vanishes
sp_sum = sparse_ops.sparse_add(sp_a, sp_b, thresh=0.11)
sum_out = sess.run(sp_sum)
self.assertEqual(sp_sum.dense_shape.get_shape(), [2])
self.assertAllEqual(sum_out.indices, [[0, 1], [2, 0], [2, 1]])
self.assertAllClose(sum_out.values, [2, 6, -.2])
self.assertAllEqual(sum_out.dense_shape, [3, 3])
def testGradients(self):
np.random.seed(1618) # Make it reproducible.
with self.test_session(use_gpu=False):
for n in [10, 31]:
for m in [4, 17]:
sp_a, nnz_a = self._randomTensor([n, m], np.float32)
sp_b, nnz_b = self._randomTensor([n, m], np.float32)
sp_sum = sparse_ops.sparse_add(sp_a, sp_b)
nnz_sum = len(sp_sum.values.eval())
err = gradient_checker.compute_gradient_error(
[sp_a.values, sp_b.values], [(nnz_a,), (nnz_b,)], sp_sum.values,
(nnz_sum,))
self.assertLess(err, 1e-3)
def testAddSparseDense(self):
np.random.seed(1618) # Make it reproducible.
n, m = np.random.randint(30, size=2)
for dtype in [np.float32, np.float64, np.int64, np.complex64]:
for index_dtype in [np.int32, np.int64]:
rand_vals_np = np.random.randn(n, m).astype(dtype)
dense_np = np.random.randn(n, m).astype(dtype)
with self.test_session(use_gpu=False):
sparse, unused_nnz = _sparsify(rand_vals_np, index_dtype=index_dtype)
s = sparse_ops.sparse_add(sparse,
constant_op.constant(dense_np)).eval()
self.assertAllEqual(dense_np + rand_vals_np, s)
self.assertTrue(s.dtype == dtype)
# check commutativity
s = sparse_ops.sparse_add(constant_op.constant(dense_np),
sparse).eval()
self.assertAllEqual(dense_np + rand_vals_np, s)
self.assertTrue(s.dtype == dtype)
def testSparseTensorDenseAddGradients(self):
np.random.seed(1618) # Make it reproducible.
n, m = np.random.randint(30, size=2)
rand_vals_np = np.random.randn(n, m).astype(np.float32)
dense_np = np.random.randn(n, m).astype(np.float32)
with self.test_session(use_gpu=False):
sparse, nnz = _sparsify(rand_vals_np)
dense = constant_op.constant(dense_np, dtype=dtypes.float32)
s = sparse_ops.sparse_add(sparse, dense)
err = gradient_checker.compute_gradient_error([sparse.values, dense],
[(nnz,), (n, m)], s, (n, m))
self.assertLess(err, 1e-3)
def testInvalidSparseTensor(self):
with self.test_session(use_gpu=False) as sess:
shape = [2, 2]
val = [0]
dense = constant_op.constant(np.zeros(shape, dtype=np.int32))
for bad_idx in [
[[-1, 0]], # -1 is invalid.
[[1, 3]], # ...so is 3.
]:
sparse = sparse_tensor.SparseTensorValue(bad_idx, val, shape)
s = sparse_ops.sparse_add(sparse, dense)
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
"invalid index"):
sess.run(s)
######################## Benchmarking code
def _s2d_add_vs_sparse_add(sparsity, n, m, num_iters=50):
np.random.seed(1618)
with session.Session(graph=ops.Graph()) as sess:
sp_vals = np.random.rand(n, m).astype(np.float32)
sp_t, unused_nnz = _sparsify(sp_vals, thresh=sparsity, index_dtype=np.int32)
vals = np.random.rand(n, m).astype(np.float32)
s2d = math_ops.add(
sparse_ops.sparse_tensor_to_dense(sp_t), constant_op.constant(vals))
sa = sparse_ops.sparse_add(sp_t, constant_op.constant(vals))
timeit.timeit(lambda: sess.run(s2d), number=3)
timeit.timeit(lambda: sess.run(sa), number=3)
s2d_total = timeit.timeit(lambda: sess.run(s2d), number=num_iters)
sa_total = timeit.timeit(lambda: sess.run(sa), number=num_iters)
# per-iter latency; secs to millis
return s2d_total * 1e3 / num_iters, sa_total * 1e3 / num_iters
class SparseAddBenchmark(test.Benchmark):
def benchmarkSparseAddDense(self):
print("SparseAddDense: add with sparse_to_dense vs. sparse_add")
print("%nnz \t n \t m \t millis(s2d) \t millis(sparse_add) \t speedup")
for sparsity in [0.99, 0.5, 0.01]:
for n in [1, 256, 50000]:
for m in [100, 1000]:
s2d_dt, sa_dt = _s2d_add_vs_sparse_add(sparsity, n, m)
print("%.2f \t %d \t %d \t %.4f \t %.4f \t %.2f" % (sparsity, n, m,
s2d_dt, sa_dt,
s2d_dt / sa_dt))
if __name__ == "__main__":
test.main()
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