def __init__(self,
num_gpus_per_worker=0,
cluster_spec=None,
task_type=None,
task_id=None):
"""Initiailizes this strategy.
Args:
num_gpus_per_worker: number of local GPUs or GPUs per worker.
cluster_spec: a dict, ClusterDef or ClusterSpec object specifying the
cluster configurations.
task_type: the current task type.
task_id: the current task id.
"""
super(ParameterServerStrategy, self).__init__()
self._num_gpus_per_worker = num_gpus_per_worker
if cluster_spec:
cluster_spec = _normalize_cluster_spec(cluster_spec)
self._cluster_spec = cluster_spec
# We typically don't need to do all-reduce in this strategy.
self._cross_tower_ops = (
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps(
reduce_to_device=_LOCAL_CPU))
self._initialize_devices(num_gpus_per_worker, cluster_spec, task_type,
task_id)
def __init__(self,
num_gpus_per_worker=0,
cluster_spec=None,
task_type=None,
task_id=None):
"""Initializes this strategy.
Args:
num_gpus_per_worker: number of local GPUs or GPUs per worker.
cluster_spec: a dict, ClusterDef or ClusterSpec object specifying the
cluster configurations.
task_type: the current task type.
task_id: the current task id.
Raises:
ValueError: if `cluster_spec` is given but `task_type` or `task_id` is
not.
"""
super(ParameterServerStrategy, self).__init__()
self._num_gpus_per_worker = num_gpus_per_worker
if cluster_spec:
self._initialize_multi_worker(num_gpus_per_worker, cluster_spec,
task_type, task_id)
else:
self._initialize_local(num_gpus_per_worker)
# We typically don't need to do all-reduce in this strategy.
self._cross_tower_ops = (
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps(
reduce_to_device=_LOCAL_CPU))
def __init__(self, num_gpus_per_worker=0):
"""Initializes this strategy.
Args:
num_gpus_per_worker: number of local GPUs or GPUs per worker, the default
is 0 meaning CPU only.
Raises:
ValueError: if `cluster_spec` is given but `task_type` or `task_id` is
not.
"""
super(ParameterServerStrategy, self).__init__()
self._num_gpus_per_worker = num_gpus_per_worker
self._initialize_local(num_gpus_per_worker)
# We typically don't need to do all-reduce in this strategy.
self._cross_tower_ops = (
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps(
reduce_to_device=_LOCAL_CPU))
class SingleWorkerCrossTowerOpsTest(CrossTowerOpsTestBase):
# TODO(yuefengz): decouple the num_gpus check from distribution in
# combinations module so that we can pass in devices instead of a distribution
# strategy.
reduction_to_one_combinations = combinations.combine(
cross_tower_ops=[
combinations.NamedObject(
"DefaultReductionToOneDeviceCrossTowerOps",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps()),
combinations.NamedObject(
"ReductionToCPUDeviceCrossTowerOps",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps(
reduce_to_device=_cpu_device)),
combinations.NamedObject(
"AccumulateNCrossTowerOp",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps(
accumulation_fn=math_ops.accumulate_n)),
],
distribution=[
combinations.one_device_strategy,
combinations.mirrored_strategy_with_gpu_and_cpu,
combinations.mirrored_strategy_with_two_gpus
],
mode=["graph", "eager"])
allreduce_combinations = combinations.combine(
cross_tower_ops=[
combinations.NamedObject(
"AllReduce",
cross_tower_ops_lib.AllReduceCrossTowerOps("nccl", 1, 0, 0)),
combinations.NamedObject(
"HierarchicalCopy",
cross_tower_ops_lib.AllReduceCrossTowerOps(
"hierarchical_copy", 8, 0, 0)),
combinations.NamedObject(
"AllReduceNoGradientRepacking",
cross_tower_ops_lib.AllReduceCrossTowerOps("nccl", 0, 0, 0)),
combinations.NamedObject(
"HierarchicalCopyAggregateSmallTensors",
cross_tower_ops_lib.AllReduceCrossTowerOps(
"hierarchical_copy", 0, 100, 10))
],
distribution=[combinations.mirrored_strategy_with_two_gpus],
mode=["graph", "eager"])
@combinations.generate(reduction_to_one_combinations + allreduce_combinations)
def testReductionAndBroadcast(self, cross_tower_ops, distribution):
with distribution.scope():
self._testReductionAndBroadcast(cross_tower_ops, distribution)
def testChooseAlgorithm(self):
device_links = [[1, 2, 3, 4], [0, 2, 3, 5], [0, 1, 3, 6], [0, 1, 2, 7],
[0, 5, 6, 7], [1, 4, 6, 7], [2, 4, 5, 7], [3, 4, 5, 6]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertIsInstance(result, cross_tower_ops_lib.AllReduceCrossTowerOps)
self.assertEqual(result._all_reduce_alg, "hierarchical_copy")
self.assertEqual(result._num_packs, 8)
# if there are only 4 devices
device_links = [[1, 2, 3, 4], [0, 2, 3, 5], [0, 1, 3, 6], [0, 1, 2, 7]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertIsInstance(result, cross_tower_ops_lib.AllReduceCrossTowerOps)
self.assertEqual(result._all_reduce_alg, "nccl")
self.assertEqual(result._num_packs, 1)
# if devices links contain each device itself
device_links = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 5], [0, 1, 2, 3, 6],
[0, 1, 2, 3, 7], [0, 4, 5, 6, 7], [1, 4, 5, 6, 7],
[2, 4, 5, 6, 7], [3, 4, 5, 6, 7]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertIsInstance(result, cross_tower_ops_lib.AllReduceCrossTowerOps)
self.assertEqual(result._all_reduce_alg, "hierarchical_copy")
self.assertEqual(result._num_packs, 8)
# if not dgx1-like links
device_links = [[0, 2, 3, 5], [0, 1, 3, 6], [0, 1, 2, 7], [0, 5, 6, 7],
[1, 4, 6, 7], [2, 4, 5, 7], [3, 4, 5, 6], [1, 2, 3, 4]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertIsInstance(result, cross_tower_ops_lib.AllReduceCrossTowerOps)
self.assertEqual(result._all_reduce_alg, "nccl")
self.assertEqual(result._num_packs, 1)
@combinations.generate(combinations.combine(
mode=["graph", "eager"],
required_gpus=1))
def testSimpleReduceWithIndexedSlices(self):
devices = ["/cpu:0", "/gpu:0"]
t0 = _make_indexed_slices([[1., 2.]], [1], [5, 2], devices[0])
t1 = _make_indexed_slices([[3., 4.], [5., 6.]], [1, 3], [5, 2], devices[1])
per_device = value_lib.PerDevice({devices[0]: t0, devices[1]: t1})
result = cross_tower_ops_lib._simple_reduce(per_device, devices[0],
math_ops.add_n, "sum")
# Test that the result is semantically equal to both the concatenated
# IndexedSlices with and without duplicate indices.
total_with_dups = _make_indexed_slices(
[[1., 2.], [3., 4.], [5., 6.]], [1, 1, 3], [5, 2], devices[0])
total_without_dups = _make_indexed_slices(
[[4., 6.], [5., 6.]], [1, 3], [5, 2], devices[0])
self._assert_indexed_slices_equal(total_with_dups, result)
self._assert_indexed_slices_equal(total_without_dups, result)
@combinations.generate(combinations.combine(
cross_tower_ops_instance=[
combinations.NamedObject(
"ReductionToOneDeviceCrossTowerOps",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps()),
combinations.NamedObject(
"AllReduceCrossTowerOps",
cross_tower_ops_lib.AllReduceCrossTowerOps())
],
method_string=["sum", "mean"],
batch_reduce=[True, False],
mode=["graph", "eager"],
required_gpus=1))
def testIndexedSlicesAllReduce(self, cross_tower_ops_instance,
method_string, batch_reduce):
devices = ["/cpu:0", "/gpu:0"]
dense_shape = [5, 2]
t0 = _make_indexed_slices([[1., 2.]], [1], dense_shape, devices[0])
t1 = _make_indexed_slices(
[[3., 4.], [5., 6.]], [1, 3], dense_shape, devices[1])
per_device = value_lib.PerDevice({devices[0]: t0, devices[1]: t1})
if batch_reduce:
result = cross_tower_ops_instance.batch_reduce(method_string,
[(per_device, devices)])
else:
result = cross_tower_ops_instance.reduce(method_string, per_device,
devices)
total_indices_with_dups = [1, 1, 3]
total_indices_without_dups = [1, 3]
if method_string == "sum":
total_values_with_dups = [[1., 2.], [3., 4.], [5., 6.]]
total_values_without_dups = [[4., 6.], [5., 6.]]
else:
assert method_string == "mean"
total_values_with_dups = [[0.5, 1.], [1.5, 2.], [2.5, 3.]]
total_values_without_dups = [[2., 3.], [2.5, 3.]]
total_mirrored_with_dups = _make_mirrored_indexed_slices(
devices, total_values_with_dups, total_indices_with_dups, dense_shape)
total_mirrored_without_dups = _make_mirrored_indexed_slices(
devices, total_values_without_dups, total_indices_without_dups,
dense_shape)
# Test that the result is semantically equal to both the concatenated
# IndexedSlices, as well as when the duplicate indices are summed up.
if batch_reduce:
total_mirrored_with_dups = [total_mirrored_with_dups]
total_mirrored_without_dups = [total_mirrored_without_dups]
self._assert_values_equal(total_mirrored_with_dups, result)
self._assert_values_equal(total_mirrored_without_dups, result)
def _get_cross_tower_ops(self):
if self._cross_tower_ops is None:
self._cross_tower_ops = (
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps())
return self._cross_tower_ops
class CrossTowerOpsTest(test.TestCase, parameterized.TestCase):
def _assert_indexed_slices_equal(self, left, right):
self.assertIsInstance(left, ops.IndexedSlices)
self.assertIsInstance(right, ops.IndexedSlices)
self.assertEqual(device_util.resolve(left.device),
device_util.resolve(right.device))
self.assertAllEqual(self.evaluate(ops.convert_to_tensor(left)),
self.evaluate(ops.convert_to_tensor(right)))
def _assert_values_equal(self, left, right):
if isinstance(left, list):
for l, r in zip(left, right):
self._assert_values_equal(l, r)
else:
self.assertEqual(type(left), type(right))
self.assertEqual(left.devices, right.devices)
if isinstance(list(left._index.values())[0], ops.IndexedSlices):
for (d, v) in left._index.iteritems():
self._assert_indexed_slices_equal(v, right._index[d])
elif context.executing_eagerly():
self.assertEqual([v.numpy() for v in left._index.values()],
list(right._index.values()))
else:
with self.test_session() as sess:
self.assertEqual(sess.run(list(left._index.values())),
list(right._index.values()))
# TODO(yuefengz): decouple the num_gpus check from distribution in
# combinations module so that we can pass in devices instead of a distribution
# strategy.
reduction_to_one_combinations = combinations.combine(
cross_tower_ops=[
combinations.NamedObject(
"DefaultReductionToOneDeviceCrossTowerOps",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps()),
combinations.NamedObject(
"ReductionToCPUDeviceCrossTowerOps",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps(
reduce_to_device=_cpu_device)),
combinations.NamedObject(
"AccumulateNCrossTowerOp",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps(
accumulation_fn=math_ops.accumulate_n)),
],
distribution=[
combinations.one_device_strategy,
combinations.mirrored_strategy_with_gpu_and_cpu,
combinations.mirrored_strategy_with_two_gpus
],
mode=["graph", "eager"])
allreduce_combinations = combinations.combine(
cross_tower_ops=[
combinations.NamedObject(
"AllReduce",
cross_tower_ops_lib.AllReduceCrossTowerOps("nccl", 1, 0, 0)),
combinations.NamedObject(
"HierarchicalCopy",
cross_tower_ops_lib.AllReduceCrossTowerOps(
"hierarchical_copy", 8, 0, 0)),
combinations.NamedObject(
"AllReduceNoGradientRepacking",
cross_tower_ops_lib.AllReduceCrossTowerOps("nccl", 0, 0, 0)),
combinations.NamedObject(
"HierarchicalCopyAggregateSmallTensors",
cross_tower_ops_lib.AllReduceCrossTowerOps(
"hierarchical_copy", 0, 100, 10))
],
distribution=[combinations.mirrored_strategy_with_two_gpus],
mode=["graph", "eager"])
@combinations.generate(reduction_to_one_combinations +
allreduce_combinations)
def testReductionAndBroadcast(self, cross_tower_ops, distribution):
devices = distribution.worker_devices
values = [constant_op.constant(float(d)) for d in range(len(devices))]
per_device = _make_per_device(values, devices)
mean = (len(devices) - 1.) / 2.
values_2 = [constant_op.constant(d + 1.0) for d in range(len(devices))]
per_device_2 = _make_per_device(values_2, devices)
mean_2 = mean + 1.
destination_mirrored = _fake_mirrored(1., devices)
destination_different = _fake_mirrored(1., _cpu_device)
destination_str = _cpu_device
destination_list = devices
all_destinations = [
None, destination_mirrored, destination_different, destination_str,
destination_list
]
# test reduce()
for destinations in all_destinations:
self._assert_values_equal(
cross_tower_ops.reduce("mean",
per_device,
destinations=destinations),
_fake_mirrored(mean, destinations or per_device))
self._assert_values_equal(
cross_tower_ops.reduce("mean",
per_device_2,
destinations=destinations),
_fake_mirrored(mean_2, destinations or per_device))
self._assert_values_equal(
cross_tower_ops.reduce("sum",
per_device,
destinations=destinations),
_fake_mirrored(mean * len(devices), destinations
or per_device))
self._assert_values_equal(
cross_tower_ops.reduce("sum",
per_device_2,
destinations=destinations),
_fake_mirrored(mean_2 * len(devices), destinations
or per_device))
# test batch_reduce()
for d1, d2 in itertools.product(all_destinations, all_destinations):
self._assert_values_equal(
cross_tower_ops.batch_reduce("mean", [(per_device, d1),
(per_device_2, d2)]),
[
_fake_mirrored(mean, d1 or per_device),
_fake_mirrored(mean_2, d2 or per_device_2)
])
self._assert_values_equal(
cross_tower_ops.batch_reduce("sum", [(per_device, d1),
(per_device_2, d2)]),
[
_fake_mirrored(mean * len(devices), d1 or per_device),
_fake_mirrored(mean_2 * len(devices), d2 or per_device_2)
])
# test broadcast()
for destinations in all_destinations:
if destinations is None:
continue
else:
self._assert_values_equal(
cross_tower_ops.broadcast(constant_op.constant(1.),
destinations),
_fake_mirrored(1., destinations))
def testChooseAlgorithm(self):
device_links = [[1, 2, 3, 4], [0, 2, 3, 5], [0, 1, 3, 6], [0, 1, 2, 7],
[0, 5, 6, 7], [1, 4, 6, 7], [2, 4, 5, 7], [3, 4, 5, 6]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertIsInstance(result,
cross_tower_ops_lib.AllReduceCrossTowerOps)
self.assertEqual(result.all_reduce_alg, "hierarchical_copy")
self.assertEqual(result.num_packs, 8)
# if there are only 4 devices
device_links = [[1, 2, 3, 4], [0, 2, 3, 5], [0, 1, 3, 6], [0, 1, 2, 7]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertIsInstance(result,
cross_tower_ops_lib.AllReduceCrossTowerOps)
self.assertEqual(result.all_reduce_alg, "nccl")
self.assertEqual(result.num_packs, 1)
# if devices links contain each device itself
device_links = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 5], [0, 1, 2, 3, 6],
[0, 1, 2, 3, 7], [0, 4, 5, 6, 7], [1, 4, 5, 6, 7],
[2, 4, 5, 6, 7], [3, 4, 5, 6, 7]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertIsInstance(result,
cross_tower_ops_lib.AllReduceCrossTowerOps)
self.assertEqual(result.all_reduce_alg, "hierarchical_copy")
self.assertEqual(result.num_packs, 8)
# if not dgx1-like links
device_links = [[0, 2, 3, 5], [0, 1, 3, 6], [0, 1, 2, 7], [0, 5, 6, 7],
[1, 4, 6, 7], [2, 4, 5, 7], [3, 4, 5, 6], [1, 2, 3, 4]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertIsInstance(result,
cross_tower_ops_lib.AllReduceCrossTowerOps)
self.assertEqual(result.all_reduce_alg, "nccl")
self.assertEqual(result.num_packs, 1)
@combinations.generate(
combinations.combine(mode=["graph", "eager"], required_gpus=1))
def testSimpleReduceWithIndexedSlices(self):
devices = ["/cpu:0", "/gpu:0"]
t0 = _make_indexed_slices([[1., 2.]], [1], [5, 2], devices[0])
t1 = _make_indexed_slices([[3., 4.], [5., 6.]], [1, 3], [5, 2],
devices[1])
per_device = value_lib.PerDevice({devices[0]: t0, devices[1]: t1})
result = cross_tower_ops_lib._simple_reduce(per_device, devices[0],
math_ops.add_n, "sum")
# Test that the result is semantically equal to both the concatenated
# IndexedSlices with and without duplicate indices.
total_with_dups = _make_indexed_slices([[1., 2.], [3., 4.], [5., 6.]],
[1, 1, 3], [5, 2], devices[0])
total_without_dups = _make_indexed_slices([[4., 6.], [5., 6.]], [1, 3],
[5, 2], devices[0])
self._assert_indexed_slices_equal(total_with_dups, result)
self._assert_indexed_slices_equal(total_without_dups, result)
@combinations.generate(
combinations.combine(cross_tower_ops_instance=[
combinations.NamedObject(
"ReductionToOneDeviceCrossTowerOps",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps()),
combinations.NamedObject(
"AllReduceCrossTowerOps",
cross_tower_ops_lib.AllReduceCrossTowerOps())
],
method_string=["sum", "mean"],
batch_reduce=[True, False],
mode=["graph", "eager"],
required_gpus=1))
def testIndexedSlicesAllReduce(self, cross_tower_ops_instance,
method_string, batch_reduce):
devices = ["/cpu:0", "/gpu:0"]
dense_shape = [5, 2]
t0 = _make_indexed_slices([[1., 2.]], [1], dense_shape, devices[0])
t1 = _make_indexed_slices([[3., 4.], [5., 6.]], [1, 3], dense_shape,
devices[1])
per_device = value_lib.PerDevice({devices[0]: t0, devices[1]: t1})
if batch_reduce:
result = cross_tower_ops_instance.batch_reduce(
method_string, [(per_device, devices)])
else:
result = cross_tower_ops_instance.reduce(method_string, per_device,
devices)
total_indices_with_dups = [1, 1, 3]
total_indices_without_dups = [1, 3]
if method_string == "sum":
total_values_with_dups = [[1., 2.], [3., 4.], [5., 6.]]
total_values_without_dups = [[4., 6.], [5., 6.]]
else:
assert method_string == "mean"
total_values_with_dups = [[0.5, 1.], [1.5, 2.], [2.5, 3.]]
total_values_without_dups = [[2., 3.], [2.5, 3.]]
total_mirrored_with_dups = _make_mirrored_indexed_slices(
devices, total_values_with_dups, total_indices_with_dups,
dense_shape)
total_mirrored_without_dups = _make_mirrored_indexed_slices(
devices, total_values_without_dups, total_indices_without_dups,
dense_shape)
# Test that the result is semantically equal to both the concatenated
# IndexedSlices, as well as when the duplicate indices are summed up.
if batch_reduce:
total_mirrored_with_dups = [total_mirrored_with_dups]
total_mirrored_without_dups = [total_mirrored_without_dups]
self._assert_values_equal(total_mirrored_with_dups, result)
self._assert_values_equal(total_mirrored_without_dups, result)
class CrossTowerOpsTest(test.TestCase, parameterized.TestCase):
def _assert_value_equal(self, left, right):
if isinstance(left, list):
for l, r in zip(left, right):
self._assert_value_equal(l, r)
else:
self.assertEqual(type(left), type(right))
self.assertEqual(left.devices, right.devices)
if context.executing_eagerly():
self.assertEqual([v.numpy() for v in left._index.values()],
list(right._index.values()))
else:
with self.test_session() as sess:
self.assertEqual(sess.run(list(left._index.values())),
list(right._index.values()))
# TODO (yuefengz): decouple the num_gpus check from distribution in id:1093
# https://github.com/imdone/tensorflow/issues/1094
# combinations module so that we can pass in devices instead of a distribution
# strategy.
reduction_to_one_combinations = combinations.combine(
cross_tower_ops=[
combinations.NamedObject(
"DefaultReductionToOneDeviceCrossTowerOps",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps()),
combinations.NamedObject(
"ReductionToCPUDeviceCrossTowerOps",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps(
reduce_to_device=_cpu_device)),
combinations.NamedObject(
"AccumulateNCrossTowerOp",
cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps(
accumulation_fn=math_ops.accumulate_n)),
],
distribution=[
combinations.one_device_strategy,
combinations.mirrored_strategy_with_gpu_and_cpu,
combinations.mirrored_strategy_with_two_gpus
],
mode=["graph", "eager"])
allreduce_combinations = combinations.combine(
cross_tower_ops=[
combinations.NamedObject(
"AllReduce",
cross_tower_ops_lib.AllReduceCrossTowerOps("nccl", 1, 0, 0)),
combinations.NamedObject(
"HierarchicalCopy",
cross_tower_ops_lib.AllReduceCrossTowerOps(
"hierarchical_copy", 8, 0, 0)),
combinations.NamedObject(
"AllReduceNoGradientRepacking",
cross_tower_ops_lib.AllReduceCrossTowerOps("nccl", 0, 0, 0)),
combinations.NamedObject(
"HierarchicalCopyAggregateSmallTensors",
cross_tower_ops_lib.AllReduceCrossTowerOps(
"hierarchical_copy", 0, 100, 10))
],
distribution=[combinations.mirrored_strategy_with_two_gpus],
mode=["graph", "eager"])
@combinations.generate(reduction_to_one_combinations +
allreduce_combinations)
def testReductionAndBroadcast(self, cross_tower_ops, distribution):
devices = distribution.worker_devices
values = [constant_op.constant(float(d)) for d in range(len(devices))]
per_device = _make_per_device(values, devices)
mean = (len(devices) - 1.) / 2.
values_2 = [constant_op.constant(d + 1.0) for d in range(len(devices))]
per_device_2 = _make_per_device(values_2, devices)
mean_2 = mean + 1.
destination_mirrored = _fake_mirrored(1., devices)
destination_different = _fake_mirrored(1., _cpu_device)
destination_str = _cpu_device
destination_list = devices
all_destinations = [
None, destination_mirrored, destination_different, destination_str,
destination_list
]
# test reduce()
for destinations in all_destinations:
self._assert_value_equal(
cross_tower_ops.reduce("mean",
per_device,
destinations=destinations),
_fake_mirrored(mean, destinations or per_device))
self._assert_value_equal(
cross_tower_ops.reduce("mean",
per_device_2,
destinations=destinations),
_fake_mirrored(mean_2, destinations or per_device))
self._assert_value_equal(
cross_tower_ops.reduce("sum",
per_device,
destinations=destinations),
_fake_mirrored(mean * len(devices), destinations
or per_device))
self._assert_value_equal(
cross_tower_ops.reduce("sum",
per_device_2,
destinations=destinations),
_fake_mirrored(mean_2 * len(devices), destinations
or per_device))
# test batch_reduce()
for d1, d2 in itertools.product(all_destinations, all_destinations):
self._assert_value_equal(
cross_tower_ops.batch_reduce("mean", [(per_device, d1),
(per_device_2, d2)]),
[
_fake_mirrored(mean, d1 or per_device),
_fake_mirrored(mean_2, d2 or per_device_2)
])
self._assert_value_equal(
cross_tower_ops.batch_reduce("sum", [(per_device, d1),
(per_device_2, d2)]),
[
_fake_mirrored(mean * len(devices), d1 or per_device),
_fake_mirrored(mean_2 * len(devices), d2 or per_device_2)
])
# test broadcast()
for destinations in all_destinations:
if destinations is None:
continue
else:
self._assert_value_equal(
cross_tower_ops.broadcast(constant_op.constant(1.),
destinations),
_fake_mirrored(1., destinations))
def testChooseAlgorithm(self):
device_links = [[1, 2, 3, 4], [0, 2, 3, 5], [0, 1, 3, 6], [0, 1, 2, 7],
[0, 5, 6, 7], [1, 4, 6, 7], [2, 4, 5, 7], [3, 4, 5, 6]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertTrue(
isinstance(result, cross_tower_ops_lib.AllReduceCrossTowerOps))
self.assertEqual(result.all_reduce_alg, "hierarchical_copy")
self.assertEqual(result.num_packs, 8)
# if there are only 4 devices
device_links = [[1, 2, 3, 4], [0, 2, 3, 5], [0, 1, 3, 6], [0, 1, 2, 7]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertTrue(
isinstance(result, cross_tower_ops_lib.AllReduceCrossTowerOps))
self.assertEqual(result.all_reduce_alg, "nccl")
self.assertEqual(result.num_packs, 1)
# if devices links contain each device itself
device_links = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 5], [0, 1, 2, 3, 6],
[0, 1, 2, 3, 7], [0, 4, 5, 6, 7], [1, 4, 5, 6, 7],
[2, 4, 5, 6, 7], [3, 4, 5, 6, 7]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertTrue(
isinstance(result, cross_tower_ops_lib.AllReduceCrossTowerOps))
self.assertEqual(result.all_reduce_alg, "hierarchical_copy")
self.assertEqual(result.num_packs, 8)
# if not dgx1-like links
device_links = [[0, 2, 3, 5], [0, 1, 3, 6], [0, 1, 2, 7], [0, 5, 6, 7],
[1, 4, 6, 7], [2, 4, 5, 7], [3, 4, 5, 6], [1, 2, 3, 4]]
result = cross_tower_ops_lib._choose_all_reduce_algorithm(device_links)
self.assertTrue(
isinstance(result, cross_tower_ops_lib.AllReduceCrossTowerOps))
self.assertEqual(result.all_reduce_alg, "nccl")
self.assertEqual(result.num_packs, 1)