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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 nccl ops. See also the cc test for nccl_communicator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib import nccl
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class AllReduceTest(test.TestCase):
def testAllReduce(self):
if not test.is_gpu_available():
return # Test requires access to a GPU
for dtype in [np.float32, np.int32, np.int64, np.float64]:
# Create session inside outer loop to test use of
# same communicator across multiple sessions.
with self.test_session(use_gpu=True) as sess:
self._testSingleAllReduce(sess, dtype, nccl.all_sum, lambda x, y: x + y)
self._testSingleAllReduce(sess, dtype, nccl.all_prod,
lambda x, y: x * y)
self._testSingleAllReduce(sess, dtype, nccl.all_min, np.minimum)
self._testSingleAllReduce(sess, dtype, nccl.all_max, np.maximum)
def _testSingleAllReduce(self, sess, np_type, nccl_fn, numpy_accumulation_fn):
for devices in [['/gpu:0', '/gpu:0', '/gpu:0'], ['/gpu:0', '/gpu:0']]:
shape = (3, 4)
np_ans = None
tensors = []
for d in devices:
with ops.device(d):
t = ((np.random.random_sample(shape) - .5) * 1024).astype(np_type)
if np_ans is None:
np_ans = t
else:
np_ans = numpy_accumulation_fn(np_ans, t)
tensors.append(array_ops.identity(t))
all_reduce_tensors = nccl_fn(tensors)
# Test shape inference.
for r in all_reduce_tensors:
self.assertEqual(shape, r.get_shape())
# Test execution and results.
nccl_results = sess.run(all_reduce_tensors)
for r in nccl_results:
self.assertAllClose(r, np_ans)
def testErrors(self):
with self.assertRaisesRegexp(ValueError, 'Device assignment required'):
nccl.all_sum([array_ops.identity(np.random.random_sample((3, 4)))])
with self.assertRaisesRegexp(ValueError, 'Must pass >0 tensors'):
nccl.all_sum([])
class BroadcastTest(test.TestCase):
def testBroadcast(self):
if not test.is_gpu_available():
return # Test requires access to a GPU
for dtype in [np.float32, np.int32, np.int64, np.float64]:
# Create session inside outer loop to test use of
# same communicator across multiple sessions.
with self.test_session(use_gpu=True) as sess:
for devices in [['/gpu:0', '/gpu:0', '/gpu:0'], ['/gpu:0', '/gpu:0']]:
shape = (3, 4)
sender = np.random.randint(0, len(devices) - 1)
with ops.device(devices[sender]):
np_ans = ((
(np.random.random_sample(shape) - .5) * 1024).astype(dtype))
t = array_ops.identity(np_ans)
other_devices = devices[:sender] + devices[sender + 1:]
send_op, received_tensors = nccl.broadcast(t, other_devices)
# Verify shape inference.
for r in received_tensors:
self.assertEqual(shape, r.get_shape())
# Run and verify results.
nccl_results = sess.run(received_tensors + [send_op])
for r in nccl_results[:-1]:
self.assertAllClose(r, np_ans)
class CombinedTest(test.TestCase):
"""Tests using a mix of all-reduce ops in one session.run call."""
def testCombined(self):
if not test.is_gpu_available():
return # Test requires access to a GPU
for dtype in [np.float32, np.int32, np.int64, np.float64]:
# Create session inside outer loop to test use of
# same communicator across multiple sessions.
with self.test_session(use_gpu=True) as sess:
for devices in [['/gpu:0', '/gpu:0', '/gpu:0'], ['/gpu:0', '/gpu:0']]:
shape = (3, 4)
# all-reduce
np_ans = np.zeros(shape=shape, dtype=dtype)
tensors = []
for d in devices:
with ops.device(d):
t = ((np.random.random_sample(shape) - .5) * 1024).astype(dtype)
np_ans += t
tensors.append(array_ops.identity(t))
all_reduce_tensors = nccl.all_sum(tensors)
sender = np.random.randint(0, len(devices) - 1)
other_devices = devices[:sender] + devices[sender + 1:]
send_op, received_tensors = nccl.broadcast(all_reduce_tensors[sender],
other_devices)
# sender doesn't need to be fetched as part of outputs of session.run.
del all_reduce_tensors[sender]
# Verify shape inference.
for r in received_tensors:
self.assertEqual(shape, r.get_shape())
# Run and verify results.
nccl_results = sess.run(
received_tensors + [send_op] + all_reduce_tensors)
for r in nccl_results[:len(received_tensors)]:
self.assertAllClose(r, np_ans)
if __name__ == '__main__':
test.main()
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