def __init__(self,
container_strategy,
tpu_cluster_resolver,
steps_per_run,
num_cores=None):
super(TPUExtended, self).__init__(container_strategy)
self._tpu_cluster_resolver = tpu_cluster_resolver
self._tpu_metadata = get_tpu_system_metadata(
self._tpu_cluster_resolver)
# TODO(sourabhbajaj): Change this from num_cores to metadata_override
self._num_cores_override = num_cores
# TODO(jhseu): Switch to DeviceAssignment to support pods and model
# parallelism.
device_map = {
d.name: i
for i, d in enumerate(self._tpu_metadata.devices)
if "device:TPU:" in d.name
}
self._device_index = values.PerReplica(device_map)
self._host_device = self.get_host_cpu_device(0)
self._tpu_devices = sorted(device_map.keys())
# Only create variables for the number of replicas we're running.
self._tpu_devices = self._tpu_devices[:self._num_replicas_in_sync]
# TODO(sourabhbajaj): Remove this once performance of running one step
# at a time is comparable to multiple steps.
self.steps_per_run = steps_per_run
self._require_static_shapes = True
def _initialize_local(self, num_gpus, devices):
"""Initializes the object for local training."""
self._cluster_spec = None
# Convert `num_gpus` into `devices`, shouldn't specify both.
if devices is None:
if num_gpus is None:
num_gpus = context.num_gpus()
if num_gpus == 0:
devices = ["/device:CPU:0"]
else:
devices = ["/device:GPU:%d" % d for d in range(num_gpus)]
elif num_gpus is not None:
raise ValueError(
"Must only specify one of `devices` and `num_gpus`.")
self._num_gpus = num_gpus
# TODO(yuefengz): consider setting the default device.
assert devices, "Must specify at least one device."
assert len(set(devices)) == len(devices), (
"No duplicates allowed in `devices` argument.")
# TODO(josh11b): Require at least 2 devices?
self._devices = [device_util.resolve(d) for d in devices]
self._canonical_device_set = set(self._devices)
self._device_index = values.PerReplica(
{d: i
for i, d in enumerate(devices)})
def testContainsIndexedSlices_PerReplica(self):
t0 = math_ops._as_indexed_slices(
constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
t1 = math_ops._as_indexed_slices(
constant_op.constant([[0., 0.], [5, 6], [7., 8.]]))
per_replica = value_lib.PerReplica({"/gpu:0": t0, "/cpu:0": t1})
self.assertTrue(cross_tower_utils.contains_indexed_slices(per_replica))
def map(self, map_over, fn, *args, **kwargs):
# TODO(josh11b): In eager mode, use one thread per device.
index = {}
for i, m in enumerate(map_over):
d = self._devices[i % len(self._devices)]
with ops.device(d):
l = index.get(d, [])
l.append(
fn(m, *values.select_device_mirrored(d, args),
**values.select_device_mirrored(d, kwargs)))
index[d] = l
# TODO(josh11b): Need a values.regroup equivalent that handles MapOutput
# in addition to PerReplica data.
return values.PerReplica(
{k: values.MapOutput(v)
for k, v in index.items()})
def _make_tensor_into_per_replica(input_tensor):
"""Converts a single tensor into a PerReplica object."""
if isinstance(input_tensor, (tuple, list)):
raise ValueError("Cannot convert `input_tensor` to a `PerReplica` object, "
"got %r but expected a object that is not a tuple or list."
% (input_tensor,))
if isinstance(input_tensor, value_lib.PerReplica):
return input_tensor
try:
device = input_tensor.device
except AttributeError:
raise ValueError("Cannot convert `input_tensor` to a `PerReplica` object "
"because it doesn't have device set.")
return value_lib.PerReplica({device: input_tensor})
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_replica = value_lib.PerReplica({devices[0]: t0, devices[1]: t1})
result = cross_tower_ops_lib._simple_reduce(
per_replica, devices[0], math_ops.add_n, vs.VariableAggregation.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)
def _make_per_replica(values, devices, regroup=False):
devices = cross_tower_ops_lib.get_devices_from(devices)
assert len(values) == len(devices)
# We simulate the result of regroup called on PerReplica which strips the
# PerReplica wrapper if it has only one value.
if len(values) == 1 and regroup:
with ops.device(devices[0]):
placed_v = array_ops.identity(values[0])
return placed_v
index = {}
for d, v in zip(devices, values):
with ops.device(d):
placed_v = array_ops.identity(v)
index[d] = placed_v
return value_lib.PerReplica(index)
def _initialize_multi_worker(self, num_gpus, cluster_spec):
"""Initializes the object for multi-worker training."""
cluster_spec = multi_worker_util.normalize_cluster_spec(cluster_spec)
self._cluster_spec = cluster_spec
self._workers = []
for job in ["chief", "worker"]:
for task in range(len(cluster_spec.as_dict().get(job, []))):
self._workers.append("/job:%s/task:%d" % (job, task))
if num_gpus is None:
raise ValueError(
"`num_gpus` is required if `cluster_spec` is given.")
if num_gpus > 0:
self._worker_devices = [(worker, [
device_util.canonicalize(worker + "/device:GPU:%d" % gpu)
for gpu in range(num_gpus)
]) for worker in self._workers]
else:
self._worker_devices = [
(worker, [device_util.canonicalize(worker, "/device:CPU:0")])
for worker in self._workers
]
devices = nest.flatten([l for _, l in self._worker_devices])
# Setting `_default_device` will add a device scope in the
# distribution.scope. We set the default device to the first worker. When
# users specify device under distribution.scope by
# with tf.device("/cpu:0"):
# ...
# their ops will end up on the cpu device of its first worker, e.g.
# "/job:worker/task:0/device:CPU:0". Note this is not used in replica mode.
self._default_device = self._workers[0]
assert devices, "Must specify at least one device."
assert len(set(devices)) == len(devices), (
"No duplicates allowed in `devices` argument.")
# TODO(josh11b): Require at least 2 devices?
self._devices = [device_util.resolve(d) for d in devices]
self._canonical_device_set = set(self._devices)
self._device_index = values.PerReplica(
{d: i
for i, d in enumerate(devices)})
def testIndexedSlicesAllReduce(self, cross_tower_ops_instance, reduce_op,
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_replica = value_lib.PerReplica({devices[0]: t0, devices[1]: t1})
if batch_reduce:
result = cross_tower_ops_instance.batch_reduce(
reduce_op, [(per_replica, devices)])
else:
result = cross_tower_ops_instance.reduce(reduce_op, per_replica,
devices)
total_indices_with_dups = [1, 1, 3]
total_indices_without_dups = [1, 3]
if reduce_op == reduce_util.ReduceOp.SUM:
total_values_with_dups = [[1., 2.], [3., 4.], [5., 6.]]
total_values_without_dups = [[4., 6.], [5., 6.]]
else:
assert reduce_op == reduce_util.ReduceOp.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 __init__(self, tpu_cluster_resolver, steps_per_run, num_cores=None):
"""Initializes the TPUStrategy object.
Args:
tpu_cluster_resolver: A tf.contrib.cluster_resolver.TPUClusterResolver,
which provides information about the TPU cluster.
steps_per_run: Number of steps to run on device before returning to the
host. Note that this can have side-effects on performance, hooks,
metrics, summaries etc.
This parameter is only used when Distribution Strategy is used with
estimator or keras.
num_cores: Number of cores to use on the TPU. If None specified, then
auto-detect the cores and topology of the TPU system.
"""
super(TPUStrategy, self).__init__()
self._tpu_cluster_resolver = tpu_cluster_resolver
self._tpu_metadata = get_tpu_system_metadata(
self._tpu_cluster_resolver)
# TODO(sourabhbajaj): Change this from num_cores to metadata_override
self._num_cores_override = num_cores
# TODO(jhseu): Switch to DeviceAssignment to support pods and model
# parallelism.
device_map = {
d.name: i
for i, d in enumerate(self._tpu_metadata.devices)
if "device:TPU:" in d.name
}
self._device_index = values.PerReplica(device_map)
self._host_device = self.get_host_cpu_device(0)
self._tpu_devices = sorted(device_map.keys())
# Only create variables for the number of replicas we're running.
self._tpu_devices = self._tpu_devices[:self.num_replicas_in_sync]
# TODO(sourabhbajaj): Remove this once performance of running one step
# at a time is comparable to multiple steps.
self.steps_per_run = steps_per_run
self._require_static_shapes = True
