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# Copyright 2015 Google Inc. 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 tensorflow.ops.tf.Assign*."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
class AssignOpTest(tf.test.TestCase):
def _initAssignFetch(self, x, y, use_gpu=False):
"""Initialize a param to init and update it with y."""
super(AssignOpTest, self).setUp()
with self.test_session(use_gpu=use_gpu):
p = tf.Variable(x)
assign = tf.assign(p, y)
p.initializer.run()
new_value = assign.eval()
return p.eval(), new_value
def _initAssignAddFetch(self, x, y, use_gpu=False):
"""Initialize a param to init, and compute param += y."""
with self.test_session(use_gpu=use_gpu):
p = tf.Variable(x)
add = tf.assign_add(p, y)
p.initializer.run()
new_value = add.eval()
return p.eval(), new_value
def _initAssignSubFetch(self, x, y, use_gpu=False):
"""Initialize a param to init, and compute param -= y."""
with self.test_session(use_gpu=use_gpu):
p = tf.Variable(x)
sub = tf.assign_sub(p, y)
p.initializer.run()
new_value = sub.eval()
return p.eval(), new_value
def _testTypes(self, vals):
for dtype in [np.float32, np.float64, np.int32, np.int64]:
x = np.zeros(vals.shape).astype(dtype)
y = vals.astype(dtype)
var_value, op_value = self._initAssignFetch(x, y, use_gpu=False)
self.assertAllEqual(y, var_value)
self.assertAllEqual(y, op_value)
var_value, op_value = self._initAssignAddFetch(x, y, use_gpu=False)
self.assertAllEqual(x + y, var_value)
self.assertAllEqual(x + y, op_value)
var_value, op_value = self._initAssignSubFetch(x, y, use_gpu=False)
self.assertAllEqual(x - y, var_value)
self.assertAllEqual(x - y, op_value)
if tf.test.IsBuiltWithCuda() and dtype in [np.float32, np.float64]:
var_value, op_value = self._initAssignFetch(x, y, use_gpu=True)
self.assertAllEqual(y, var_value)
self.assertAllEqual(y, op_value)
var_value, op_value = self._initAssignAddFetch(x, y, use_gpu=True)
self.assertAllEqual(x + y, var_value)
self.assertAllEqual(x + y, op_value)
var_value, op_value = self._initAssignSubFetch(x, y, use_gpu=False)
self.assertAllEqual(x - y, var_value)
self.assertAllEqual(x - y, op_value)
def testBasic(self):
self._testTypes(np.arange(0, 20).reshape([4, 5]))
def testAssignNonStrictShapeChecking(self):
with self.test_session():
data = tf.fill([1024, 1024], 0)
p = tf.Variable([1])
a = tf.assign(p, data, validate_shape=False)
a.op.run()
self.assertAllEqual(p.eval(), data.eval())
# Assign to yet another shape
data2 = tf.fill([10, 10], 1)
a2 = tf.assign(p, data2, validate_shape=False)
a2.op.run()
self.assertAllEqual(p.eval(), data2.eval())
def testInitRequiredAssignAdd(self):
with self.test_session():
p = tf.Variable(tf.fill([1024, 1024], 1),
tf.int32)
a = tf.assign_add(p, tf.fill([1024, 1024], 0))
with self.assertRaisesOpError("use uninitialized"):
a.op.run()
def testInitRequiredAssignSub(self):
with self.test_session():
p = tf.Variable(tf.fill([1024, 1024], 1),
tf.int32)
a = tf.assign_sub(p, tf.fill([1024, 1024], 0))
with self.assertRaisesOpError("use uninitialized"):
a.op.run()
# NOTE(mrry): See also
# dense_update_ops_no_tsan_test.AssignOpTest, which contains a benign
# data race and must run without TSAN.
def testParallelUpdateWithLocking(self):
with self.test_session() as sess:
zeros_t = tf.fill([1024, 1024], 0.0)
ones_t = tf.fill([1024, 1024], 1.0)
p = tf.Variable(zeros_t)
adds = [tf.assign_add(p, ones_t, use_locking=True)
for _ in range(20)]
p.initializer.run()
def run_add(add_op):
sess.run(add_op)
threads = [
self.checkedThread(target=run_add, args=(add_op,)) for add_op in adds]
for t in threads:
t.start()
for t in threads:
t.join()
vals = p.eval()
ones = np.ones((1024, 1024)).astype(np.float32)
self.assertAllEqual(vals, ones * 20)
# NOTE(mrry): See also
# dense_update_ops_no_tsan_test.[...].testParallelAssignWithoutLocking,
# which contains a benign data race and must run without TSAN.
def testParallelAssignWithLocking(self):
with self.test_session() as sess:
zeros_t = tf.fill([1024, 1024], 0.0)
ones_t = tf.fill([1024, 1024], 1.0)
p = tf.Variable(zeros_t)
assigns = [tf.assign(p, tf.mul(ones_t, float(i)),
use_locking=True)
for i in range(1, 21)]
p.initializer.run()
def run_assign(assign_op):
sess.run(assign_op)
threads = [self.checkedThread(target=run_assign, args=(assign_op,))
for assign_op in assigns]
for t in threads:
t.start()
for t in threads:
t.join()
vals = p.eval()
# Assert every element is the same, and taken from one of the assignments.
self.assertTrue(vals[0, 0] > 0)
self.assertTrue(vals[0, 0] <= 20)
self.assertAllEqual(vals, np.ones([1024, 1024]) * vals[0, 0])
if __name__ == "__main__":
tf.test.main()
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