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# Copyright 2015 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 inplace_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import inplace_ops
from tensorflow.python.platform import test as test_lib
class InplaceOpsTest(test_util.TensorFlowTestCase):
def testBasicUpdate(self):
for dtype in [dtypes.float32, dtypes.int32, dtypes.int64]:
with self.test_session(use_gpu=True):
x = array_ops.ones([7, 3], dtype)
y = np.ones([7, 3], dtype.as_numpy_dtype)
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_update(x, [3], array_ops.ones([1, 3], dtype))
y[3, :] = 1
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_update(x, [-1],
array_ops.ones([1, 3], dtype) * 2)
y[-1, :] = 2
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_update(x, 5, array_ops.ones([3], dtype) * 7)
y[5, :] = 7
self.assertAllClose(x.eval(), y)
def testBasicUpdateBool(self):
with self.test_session(use_gpu=True):
x = array_ops.ones([7, 3], dtypes.bool)
y = np.ones([7, 3], dtypes.bool.as_numpy_dtype)
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_update(x, [3], array_ops.ones([1, 3],
dtypes.bool))
y[3, :] = True
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_update(x, [-1],
array_ops.zeros([1, 3], dtypes.bool))
y[-1, :] = False
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_update(x, 5, array_ops.zeros([3], dtypes.bool))
y[5, :] = False
self.assertAllClose(x.eval(), y)
def testBasicAdd(self):
for dtype in [dtypes.float32, dtypes.int32, dtypes.int64]:
with self.test_session(use_gpu=True):
x = array_ops.ones([7, 3], dtype)
y = np.ones([7, 3], dtype.as_numpy_dtype)
self.assertAllClose(x.eval(), y)
x = array_ops.inplace_add(x, [3], array_ops.ones([1, 3], dtype))
y[3, :] += 1
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_add(x, [-1], array_ops.ones([1, 3], dtype) * 2)
y[-1, :] += 2
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_add(x, 5, array_ops.ones([3], dtype) * 7)
y[5, :] += 7
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_add(x, None, array_ops.ones([7, 3], dtype) * 99)
y[:, :] += 99
self.assertAllClose(x.eval(), y)
def testBasicSub(self):
for dtype in [dtypes.float32, dtypes.int32, dtypes.int64]:
with self.test_session(use_gpu=True):
x = array_ops.ones([7, 3], dtype)
y = np.ones([7, 3], dtype.as_numpy_dtype)
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_sub(x, [3], array_ops.ones([1, 3], dtype))
y[3, :] -= 1
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_sub(x, [-1], array_ops.ones([1, 3], dtype) * 2)
y[-1, :] -= 2
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_sub(x, 5, array_ops.ones([3], dtype) * 7)
y[5, :] -= 7
self.assertAllClose(x.eval(), y)
x = inplace_ops.inplace_sub(x, None, array_ops.ones([7, 3], dtype) * 99)
y[:, :] -= 99
self.assertAllClose(x.eval(), y)
def testRandom(self):
with self.test_session(use_gpu=True):
d0, d1, d2 = 100, 3, 5
x = array_ops.zeros([d0, d1, d2])
y = np.zeros([d0, d1, d2])
for _ in xrange(20):
idx = np.random.choice(d0, d0 // 10, replace=False)
val = np.random.randint(10, size=(d0 // 10, d1, d2))
op = np.random.randint(3)
if op == 0:
x = inplace_ops.inplace_update(x, idx, val)
y[idx, :] = val
elif op == 1:
x = inplace_ops.inplace_add(x, idx, val)
y[idx, :] += val
elif op == 2:
x = inplace_ops.inplace_sub(x, idx, val)
y[idx, :] -= val
self.assertAllClose(x.eval(), y)
def testRandom1D(self):
with self.test_session(use_gpu=True):
d0 = 100
x = array_ops.zeros([d0])
y = np.zeros([d0])
for _ in xrange(20):
idx = np.random.choice(d0, d0 // 10, replace=False)
val = np.random.randint(10, size=(d0 // 10))
op = np.random.randint(3)
if op == 0:
x = inplace_ops.inplace_update(x, idx, val)
y[idx] = val
elif op == 1:
x = inplace_ops.inplace_add(x, idx, val)
y[idx] += val
elif op == 2:
x = inplace_ops.inplace_sub(x, idx, val)
y[idx] -= val
self.assertAllClose(x.eval(), y)
def testAlias(self):
with self.test_session(use_gpu=True) as sess:
x = array_ops.ones([2, 3])
y = inplace_ops.alias_inplace_add(x, [0], [[1, 2, 3]])
with ops.control_dependencies([y]):
z = array_ops.identity(x)
_, vy, vz = sess.run([x, y, z])
self.assertAllClose(vy, vz)
def testError(self):
with self.cached_session():
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"must be a vector"):
_ = inplace_ops.inplace_update([[1.]], [[0]], [[10]]).eval()
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"x and v shape doesn't match"):
_ = inplace_ops.inplace_update([[1.]], [0], [10]).eval()
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"i and x shape doesn't match"):
_ = inplace_ops.inplace_update([[1.]], [0, 1], [[10]]).eval()
def testEmpty(self):
for dtype in [
dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64, dtypes.bool,
dtypes.uint8
]:
with self.test_session(use_gpu=True):
test_shapes = [(), (1,), (2, 3), (0, 2), (2, 3, 5), (2, 0, 5)]
for shape in test_shapes:
val = inplace_ops.empty(shape, dtype).eval()
self.assertEqual(val.shape, shape)
self.assertEqual(val.dtype, dtype.as_numpy_dtype)
val = inplace_ops.empty(shape, dtype, init=True).eval()
self.assertEqual(val.shape, shape)
self.assertEqual(val.dtype, dtype.as_numpy_dtype)
self.assertAllEqual(val, np.zeros(shape, dtype.as_numpy_dtype))
val = inplace_ops.empty_like(array_ops.zeros(shape, dtype)).eval()
self.assertEqual(val.shape, shape)
self.assertEqual(val.dtype, dtype.as_numpy_dtype)
val = inplace_ops.empty_like(
array_ops.zeros(shape, dtype), init=True).eval()
self.assertEqual(val.shape, shape)
self.assertEqual(val.dtype, dtype.as_numpy_dtype)
self.assertAllEqual(val, np.zeros(shape, dtype.as_numpy_dtype))
with self.test_session(use_gpu=True):
val = inplace_ops.empty((1, 2), dtypes.string, init=True).eval()
self.assertEqual(val.tolist(), [[b"", b""]])
val = inplace_ops.empty((1, 2), dtypes.string, init=False).eval()
self.assertEqual(val.tolist(), [[b"", b""]])
if __name__ == "__main__":
test_lib.main()
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