def __init__(self,
container_strategy,
tpu_cluster_resolver=None,
steps_per_run=None,
device_assignment=None):
super(TPUExtended, self).__init__(container_strategy)
if tpu_cluster_resolver is None:
tpu_cluster_resolver = resolver_lib.TPUClusterResolver("")
if steps_per_run is None:
# TODO(frankchn): Warn when we are being used by DS/Keras and this is
# not specified.
steps_per_run = 1
self._tpu_cluster_resolver = tpu_cluster_resolver
self._tpu_metadata = get_tpu_system_metadata(self._tpu_cluster_resolver)
self._device_assignment = device_assignment
# Device assignment is currently only supported for 1 core case.
if self._device_assignment:
assert isinstance(self._device_assignment,
device_assignment_lib.DeviceAssignment)
if self._device_assignment.num_replicas != 1:
raise ValueError("Device assignment is only supported for a single "
"core single replica case currently.")
if self._device_assignment.num_cores_per_replica != 1:
raise ValueError("Device assignment is only supported for a single "
"core single replica case currently.")
if not all(self._device_assignment.core_assignment[0][0] == [0, 0, 0]):
raise ValueError("Device assignment is only supported for a single "
"core single replica case currently.")
# TODO(jhseu): Switch to DeviceAssignment to support pods and model
# parallelism.
self._device_index = {
d.name: i for i, d in enumerate(self._tpu_metadata.devices)
if "device:TPU:" in d.name
}
self._host_device = self.get_host_cpu_device(0)
self._tpu_devices = tuple(sorted(self._device_index.keys()))
# Only create variables for the number of replicas we're running.
self._tpu_devices = self._tpu_devices[:self._num_replicas_in_sync]
self._device_map = values.ReplicaDeviceMap(self._tpu_devices)
# For input:
input_device_map = values.ReplicaDeviceMap(tuple(
self.get_host_cpu_device(hid) for hid in range(self.num_hosts)))
worker_devices = [
(self.get_host(hid), [self.get_host_cpu_device(hid)])
for hid in range(self.num_hosts)
]
self._input_workers = input_lib.InputWorkers(
input_device_map, worker_devices)
# 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 __init__(self,
container_strategy,
tpu_cluster_resolver=None,
steps_per_run=None,
num_cores=None):
super(TPUExtended, self).__init__(container_strategy)
if tpu_cluster_resolver is None:
tpu_cluster_resolver = resolver_lib.TPUClusterResolver("")
if steps_per_run is None:
# TODO(frankchn): Warn when we are being used by DS/Keras and this is
# not specified.
steps_per_run = 1
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.
self._device_index = {
d.name: i
for i, d in enumerate(self._tpu_metadata.devices)
if "device:TPU:" in d.name
}
self._host_device = self.get_host_cpu_device(0)
self._tpu_devices = tuple(sorted(self._device_index.keys()))
# Only create variables for the number of replicas we're running.
self._tpu_devices = self._tpu_devices[:self._num_replicas_in_sync]
self._device_map = values.ReplicaDeviceMap(self._tpu_devices)
# For input:
input_device_map = values.ReplicaDeviceMap(
tuple(
self.get_host_cpu_device(hid)
for hid in range(self.num_hosts)))
worker_devices = [(self.get_host(hid), [self.get_host_cpu_device(hid)])
for hid in range(self.num_hosts)]
self._input_workers = values.InputWorkers(input_device_map,
worker_devices)
# 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
# Initialize the TPU devices.
self._initialize_tpu()
def testInitializableIterator(self):
with context.graph_mode():
devices = ["/device:CPU:0"]
# Using random input since that is only allowed with initializable
# iterator.
dataset = dataset_ops.Dataset.from_tensor_slices(
random_ops.random_uniform((10,)))
device_map = values.ReplicaDeviceMap(devices)
input_workers = values.InputWorkers(device_map)
per_replica_dataset = values.PerReplicaDataset(dataset, input_workers, 0)
iterator = per_replica_dataset.make_initializable_iterator()
self.evaluate(iterator.initializer)
next_element = iterator.get_next_as_list()
for _ in range(10):
self.evaluate(next_element)
# Should fail after the input is finished.
with self.assertRaises(errors.OutOfRangeError):
self.evaluate(next_element)
# After re-initializing the iterator, should be able to iterate again.
self.evaluate(iterator.initializer)
for _ in range(10):
self.evaluate(next_element)
def testMirroredStratParaAsync(self):
"""Tests RNG/MirrorStrategy interaction #3.
The user can create n independent RNGs outside strategy.scope(), where n
is the number of replicas, and give one to each replica. The replicas can
thus get different random-number streams.
"""
shape = [3, 4]
dtype = dtypes.int32
gens = random.get_global_generator().split(count=2)
devices = ["/cpu:0", test_util.gpu_device_name()]
strat = MirroredStrategy(devices=devices)
# Use `PerReplica` to specify which `gen` is sent to which replica
gens = dist_values.PerReplica(
device_map=dist_values.ReplicaDeviceMap(devices),
values=[[g] for g in gens])
with strat.scope():
def f(gen):
t1 = gen.uniform_full_int(shape=shape, dtype=dtype)
t2 = gen.uniform_full_int(shape=shape, dtype=dtype)
t = array_ops.stack([t1, t2])
return t
results = strat.extended.call_for_each_replica(
fn=f, args=gens)
values = results.values
self.assertAllEqual(2, len(values))
self.assertAllDifferent(values)
def __init__(self,
container_strategy,
tpu_cluster_resolver=None,
steps_per_run=None,
device_assignment=None):
super(TPUExtended, self).__init__(container_strategy)
if tpu_cluster_resolver is None:
tpu_cluster_resolver = TPUClusterResolver("")
if steps_per_run is None:
# TODO(frankchn): Warn when we are being used by DS/Keras and this is
# not specified.
steps_per_run = 1
self._tpu_function_cache = weakref.WeakKeyDictionary()
self._tpu_cluster_resolver = tpu_cluster_resolver
self._tpu_metadata = get_tpu_system_metadata(self._tpu_cluster_resolver)
self._device_assignment = device_assignment
# Device assignment is currently only supported for 1 core case.
if self._device_assignment:
assert isinstance(self._device_assignment,
device_assignment_lib.DeviceAssignment)
if self._device_assignment.num_replicas != 1:
raise ValueError("Device assignment is only supported for a single "
"core single replica case currently.")
if self._device_assignment.num_cores_per_replica != 1:
raise ValueError("Device assignment is only supported for a single "
"core single replica case currently.")
if not all(self._device_assignment.core_assignment[0][0] == [0, 0, 0]):
raise ValueError("Device assignment is only supported for a single "
"core single replica case currently.")
# TODO(jhseu): Switch to DeviceAssignment to support pods and model
# parallelism.
self._tpu_devices = [d.name for d in self._tpu_metadata.devices
if "device:TPU:" in d.name]
self._host_device = device_util.get_host_for_device(self._tpu_devices[0])
# Only create variables for the number of replicas we're running.
self._tpu_devices = self._tpu_devices[:self._num_replicas_in_sync]
self._device_map = values.ReplicaDeviceMap(self._tpu_devices)
# Preload the data onto the TPUs.
input_worker_devices = collections.OrderedDict()
for tpu_device in self._tpu_devices:
host_device = device_util.get_host_for_device(tpu_device)
input_worker_devices.setdefault(host_device, [])
input_worker_devices[host_device].append(tpu_device)
self._input_workers = input_lib.InputWorkers(
self._device_map, tuple(input_worker_devices.items()))
# 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
self.experimental_enable_get_next_as_optional = True
def _create_iterator(self, input_type, dataset_fn, worker_device_pairs,
devices, split_batch_by, enable_get_next_as_optional):
device_map = values.ReplicaDeviceMap(devices)
input_workers = input_lib.InputWorkers(device_map, worker_device_pairs)
if input_type == "input_fn":
input_contexts = []
for i in range(input_workers.num_workers):
input_contexts.append(
distribute_lib.InputContext(
num_input_pipelines=input_workers.num_workers,
input_pipeline_id=i,
num_replicas_in_sync=len(devices)))
iterator = input_lib.InputFunctionIterator(
dataset_fn,
input_workers,
input_contexts,
_enable_get_next_as_optional=enable_get_next_as_optional)
else:
iterator = input_lib.DatasetIterator(
dataset_fn(distribute_lib.InputContext()),
input_workers,
split_batch_by,
_enable_get_next_as_optional=enable_get_next_as_optional)
return iterator
def predict(actions, state):
state = state.copy()
# break down the inputs along the batch dimension to form equal sized
# tensors in each replica.
num_replicas = strategy.num_replicas_in_sync
actions = tf.split(actions, num_replicas)
state = {
key: tf.split(value, num_replicas)
for key, value in state.items()
}
devices = values.ReplicaDeviceMap(strategy.extended.worker_devices)
dist_actions = values.PerReplica(devices, tuple(actions))
dist_state = []
for i in range(num_replicas):
dist_state.append({key: value[i] for key, value in state.items()})
dist_state = values.PerReplica(devices, tuple(dist_state))
dist_predictions = strategy.experimental_run_v2(model.predict,
args=(dist_actions,
dist_state))
dist_predictions = {
key: strategy.experimental_local_results(value)
for key, value in dist_predictions.items()
}
predictions = {
key: tf.concat(value, axis=0)
for key, value in dist_predictions.items()
}
return predictions
def __init__(self,
container_strategy,
tpu_cluster_resolver=None,
steps_per_run=None,
device_assignment=None):
super(TPUExtended, self).__init__(container_strategy)
if tpu_cluster_resolver is None:
tpu_cluster_resolver = TPUClusterResolver("")
if steps_per_run is None:
# TODO(frankchn): Warn when we are being used by DS/Keras and this is
# not specified.
steps_per_run = 1
self._tpu_function_cache = weakref.WeakKeyDictionary()
self._tpu_cluster_resolver = tpu_cluster_resolver
self._tpu_metadata = get_tpu_system_metadata(
self._tpu_cluster_resolver)
self._device_assignment = device_assignment
self._tpu_devices = [
d.name for d in self._tpu_metadata.devices
if "device:TPU:" in d.name
]
# Only create variables for the number of replicas we're running.
if device_assignment is not None:
job_name = device_spec.DeviceSpecV2.from_string(
self._tpu_devices[0]).job
self._tpu_devices = []
for replica_id in range(device_assignment.num_replicas):
tpu_device = device_assignment.tpu_device(replica=replica_id,
logical_core=0,
job=job_name)
tpu_device = device_util.canonicalize(tpu_device)
self._tpu_devices.append(tpu_device)
self._host_device = device_util.get_host_for_device(
self._tpu_devices[0])
self._device_map = values.ReplicaDeviceMap(self._tpu_devices)
# Preload the data onto the TPUs.
input_worker_devices = collections.OrderedDict()
for tpu_device in self._tpu_devices:
host_device = device_util.get_host_for_device(tpu_device)
input_worker_devices.setdefault(host_device, [])
input_worker_devices[host_device].append(tpu_device)
self._input_workers = input_lib.InputWorkers(
self._device_map, tuple(input_worker_devices.items()))
# 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
self.experimental_enable_get_next_as_optional = True
self.experimental_enable_dynamic_batch_size = True
def testNamedTupleEstimatorSpec(self):
with context.graph_mode(), ops.Graph().as_default():
devices = []
created_estimator_specs = []
for device_id in range(3):
spec = model_fn_lib.EstimatorSpec(
mode=model_fn_lib.ModeKeys.TRAIN,
loss=constant_op.constant(device_id / 2),
train_op=array_ops.identity(constant_op.constant(device_id)))
devices.append(_device_str(device_id))
created_estimator_specs.append(spec)
device_map = values.ReplicaDeviceMap(devices)
merged_estimator_spec = values.regroup(
device_map, created_estimator_specs)
self.assertTrue(
isinstance(merged_estimator_spec, model_fn_lib.EstimatorSpec))
self.assertEqual(model_fn_lib.ModeKeys.TRAIN, merged_estimator_spec.mode)
for device_id in range(3):
d = _device_str(device_id)
self.assertEqual(created_estimator_specs[device_id].loss,
merged_estimator_spec.loss.get(d))
self.assertEqual(created_estimator_specs[device_id].train_op,
merged_estimator_spec.train_op.get(d))
# Scaffold is populated by `EstimatorSpec.__new__`.
self.assertEqual(created_estimator_specs[device_id].scaffold,
merged_estimator_spec.scaffold.get(d))
# Also test that we can undo the merge using select_replica()
self.assertEqual(created_estimator_specs[device_id],
values.select_replica(device_id,
merged_estimator_spec))
def testNested(self):
device_map = values.ReplicaDeviceMap((_device_str(0), _device_str(1)))
result = values.regroup(device_map,
(_nested_value("1"), _nested_value("2")))
self.assertIsInstance(result, tuple)
self.assertEqual(3, len(result))
self._is_per_replica(result[0], ["a1", "a2"])
self._is_per_replica(result[2], ["h1", "h2"])
self.assertIsInstance(result[1], list)
self.assertEqual(3, len(result[1]))
self._is_per_replica(result[1][0], ["b1", "b2"])
self._is_per_replica(result[1][2], ["g1", "g2"])
self.assertIsInstance(result[1][1], dict)
self.assertEqual(set(["c", "e"]), set(result[1][1].keys()))
self._is_per_replica(result[1][1]["c"], ["d1", "d2"])
self._is_per_replica(result[1][1]["e"], ["f1", "f2"])
# Also test that we can undo the merge using select_replica()
self.assertEqual(_nested_value("1"),
values.select_replica(0, result))
self.assertEqual(_nested_value("2"),
values.select_replica(1, result))
# select_device_mirrored() should fail due to non-mirrored values
with self.assertRaises(TypeError):
values.select_device_mirrored(_device_str(0), result)
with self.assertRaises(TypeError):
values.select_device_mirrored(_device_str(1), result)
def testWrapClass(self):
# Normally a mirrored value would be the same across devices, but
# for a test it is convenient to be able to tell the values apart.
device_map = values.ReplicaDeviceMap((_device_str(0), _device_str(1)))
result = values.regroup(device_map,
(_nested_value("1"), _nested_value("2")),
values.Mirrored)
self.assertIsInstance(result, tuple)
self.assertEqual(3, len(result))
self._is_per_replica(result[0], ["a1", "a2"], values.Mirrored)
self._is_per_replica(result[2], ["h1", "h2"], values.Mirrored)
self.assertIsInstance(result[1], list)
self.assertEqual(3, len(result[1]))
self._is_per_replica(result[1][0], ["b1", "b2"], values.Mirrored)
self._is_per_replica(result[1][2], ["g1", "g2"], values.Mirrored)
self.assertIsInstance(result[1][1], dict)
self.assertEqual(set(["c", "e"]), set(result[1][1].keys()))
self._is_per_replica(result[1][1]["c"], ["d1", "d2"], values.Mirrored)
self._is_per_replica(result[1][1]["e"], ["f1", "f2"], values.Mirrored)
# Also test that we can undo the merge using select_replica()
self.assertEqual(_nested_value("1"),
values.select_replica(0, result))
self.assertEqual(_nested_value("2"),
values.select_replica(1, result))
# Values are marked as mirrored, so select_device_mirrored() is allowed.
self.assertEqual(_nested_value("1"),
values.select_device_mirrored(_device_str(0), result))
self.assertEqual(_nested_value("2"),
values.select_device_mirrored(_device_str(1), result))
def _make_mirrored_indexed_slices(devices, values, indices, dense_shape):
values = [
_make_indexed_slices(values, indices, dense_shape, d) for d in devices
]
return value_lib.regroup(value_lib.ReplicaDeviceMap(devices),
values,
wrap_class=value_lib.Mirrored)
def distributed_function(x, y, sample_weights, learning_phase=None):
"""A single step of the distributed execution across replicas."""
del learning_phase
# TODO(b/129653859): Simplify after PerReplica can be the input of
# `def_function.function`. `regroup` calls and re-wrapping in
# PerReplica won't be needed then.
if isinstance(strategy, one_device_strategy.OneDeviceStrategy):
device_map = values.SingleDeviceMap(devices[0])
wrap_class = lambda d, x: x
else:
device_map = values.ReplicaDeviceMap(devices)
wrap_class = values.PerReplica
# Transform each lists of lists of values into per replica objects
# in the case of mirrored strategy. For example, for 2 replicas:
# [[x0, y0], [x1, y1]] > [PerReplica(d0:x0, d1:x1),
# PerReplica(d0:y0, d1:y1)]
x = values.regroup(device_map, x, wrap_class)
y = values.regroup(device_map, y, wrap_class) if y else None
sample_weights = values.regroup(device_map, sample_weights,
wrap_class) if sample_weights else None
# Call `Model.{train,test,predict}_on_batch` on every replica passing
# PerReplicas as arguments. On every replica inside this call, each
# PerReplica object will return the value for that replica. The outputs
# are PerReplicas too.
outputs = strategy.experimental_run_v2(
per_replica_function, args=(x, y, sample_weights))
# Out of PerReplica outputs reduce or pick values to return.
all_outputs = unwrap_outputs(
strategy, outputs, with_loss_tensor=(mode != ModeKeys.PREDICT))
return all_outputs
def _initialize_multi_worker(self, devices):
"""Initializes the object for multi-worker training."""
self._local_mode = False
assert devices, "Must specify at least one device."
devices = tuple(device_util.resolve(d) for d in devices)
assert len(set(devices)) == len(devices), (
"No duplicates allowed in `devices` argument: %s" % devices)
# TODO(josh11b): Require at least 2 devices?
device_dict = _group_device_list(devices)
workers = []
worker_devices = []
for job in ("chief", "worker"):
for task in range(len(device_dict.get(job, []))):
worker = "/job:%s/task:%d" % (job, task)
workers.append(worker)
worker_devices.append((worker, device_dict[job][task]))
# 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 = workers[0]
self._host_input_device = numpy_dataset.SingleDevice(workers[0])
self._device_map = values.ReplicaDeviceMap(devices)
self._input_workers = input_lib.InputWorkers(self._device_map,
worker_devices)
self._inferred_cross_device_ops = cross_device_ops_lib.MultiWorkerAllReduce(
workers, _infer_num_gpus_per_worker(devices))
def testWrapAListOfTwoTuples(self):
device_map = values.ReplicaDeviceMap((_device_str(0), _device_str(1)))
result = values.regroup(device_map, [("1", "2"), ("3", "4")])
self.assertIsInstance(result, tuple)
self.assertEqual(2, len(result))
self._is_per_replica(result[0], ("1", "3"), values.PerReplica)
self._is_per_replica(result[1], ("2", "4"), values.PerReplica)
def _initialize_local(self, cluster_resolver):
"""Initialize internal devices for local training."""
worker_device = device_util.canonicalize("/device:CPU:0")
num_gpus = cluster_resolver.num_accelerators()
# Define compute devices which is a list of device strings and one for each
# replica. When there are GPUs, replicate operations on these GPUs.
# Otherwise, place operations on CPU.
if num_gpus > 0:
compute_devices = tuple(
map("/device:GPU:{}".format, range(num_gpus)))
else:
compute_devices = (_LOCAL_CPU, )
self._device_map = values.ReplicaDeviceMap(compute_devices)
self._input_workers = input_lib.InputWorkers(
self._device_map, [(worker_device, compute_devices)])
# If there is only one GPU, put everything on that GPU. Otherwise, place
# variables on CPU.
if num_gpus == 1:
assert len(compute_devices) == 1
self._variable_device = _LOCAL_GPU_0
self._parameter_devices = (_LOCAL_GPU_0, )
else:
self._variable_device = _LOCAL_CPU
self._parameter_devices = (_LOCAL_CPU, )
self._is_chief = True
self._cluster_spec = None
self._task_type = None
self._task_id = None
logging.info(
"ParameterServerStrategy with compute_devices = %r, "
"variable_device = %r", compute_devices, self._variable_device)
def observe(images, actions, rewards, state):
images = tf.to_float(images) / 255.0 - 0.5
# break down the inputs along the batch dimension to form equal sized
# tensors in each replica.
num_replicas = strategy.num_replicas_in_sync
images = tf.split(images, num_replicas)
actions = tf.split(actions, num_replicas)
state = {
key: tf.split(value, num_replicas)
for key, value in state.items()
}
devices = values.ReplicaDeviceMap(strategy.extended.worker_devices)
dist_images = values.PerReplica(devices, tuple(images))
dist_actions = values.PerReplica(devices, tuple(actions))
dist_state = []
for i in range(num_replicas):
dist_state.append({key: value[i] for key, value in state.items()})
dist_state = values.PerReplica(devices, tuple(dist_state))
_, dist_posteriors = strategy.experimental_run_v2(model.observe,
args=(dist_actions,
dist_images,
dist_state))
dist_posteriors = {
key: strategy.experimental_local_results(value)
for key, value in dist_posteriors.items()
}
posteriors = {
key: tf.concat(value, axis=0)
for key, value in dist_posteriors.items()
}
posteriors = {key: value[:, -1] for key, value in posteriors.items()}
posteriors['rewards'] = rewards[:, -1]
return posteriors
def _initialize_local(self, devices):
"""Initializes the object for local training."""
self._local_mode = True
self._device_map = values.ReplicaDeviceMap(devices)
self._input_workers = input_lib.InputWorkers(self._device_map)
self._inferred_cross_device_ops = None if self._cross_device_ops else (
cross_device_ops_lib.choose_the_best(devices))
self._host_input_device = numpy_dataset.SingleDevice("/cpu:0")
def testValueErrorForIterator(self):
# Incompatiable arguments.
d1 = "/device:GPU:0"
d2 = "/device:GPU:1"
device_map = values.ReplicaDeviceMap([d1, d2])
input_workers = values.InputWorkers(
device_map, (("w1", (d1,)), ("w2", (d2,))))
with self.assertRaises(ValueError):
values.MultiWorkerDataIterator([("w1", None)], input_workers)
def _fake_mirrored(value, devices):
"""Create a faked Mirrored object for testing.
All components of the returned Mirrored have the same objects, which is not
true in reality.
"""
devices = _get_devices(devices)
return value_lib.Mirrored(value_lib.ReplicaDeviceMap(devices),
[value] * len(devices))
def _make_replica_local(method, strategy=None):
device_map = values.ReplicaDeviceMap(_devices)
v = []
for d, n, init in zip(_devices, ["v", "v/replica"], [1., 2.]):
with ops.device(d):
v.append(variable_scope.get_variable(
name=n, initializer=init, use_resource=True))
replica_local = values.ReplicaLocalVariable(strategy, device_map, v, method)
return v, replica_local
def testOneDevice(self):
device_map = values.ReplicaDeviceMap((_device_str(0),))
result = values.regroup(device_map, (_nested_value("1"),))
# On one device regroup() and select_replica() are basically identity.
self.assertEqual(_nested_value("1"), result)
self.assertEqual(_nested_value("1"),
values.select_replica(0, result))
# The one exception has to do with MirroredVariables.
d = "/device:CPU:0"
with ops.device(d):
v = variable_scope.get_variable(
name="v", initializer=1., use_resource=True)
device_map = values.ReplicaDeviceMap((d,))
mirrored = values.MirroredVariable(None, device_map, (v,),
variable_scope.VariableAggregation.SUM)
result = values.regroup(device_map, (v,))
self.assertIs(mirrored, result)
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.]]))
device_map = value_lib.ReplicaDeviceMap(("/gpu:0", "/cpu:0"))
per_replica = value_lib.PerReplica(device_map, (t0, t1))
self.assertTrue(
cross_device_utils.contains_indexed_slices(per_replica))
def testPassPerReplica(self, distribution):
@function.defun
def fn1(mock_model, factor):
return mock_model(factor)
device_map = values.ReplicaDeviceMap(("/device:CPU:0", "/device:GPU:0"))
factors = values.PerReplica(device_map, (5.0, 3.0))
expected_result = values.PerReplica(device_map, (5.0 * 1.25, 3.0 * 1.25))
self._call_and_check(distribution, fn1, [factors], expected_result, [fn1])
def _create_variable(self, next_creator, *args, **kwargs):
colocate_with = kwargs.pop("colocate_with", None)
if colocate_with is None:
device_map = values.ReplicaDeviceMap([self._variable_device])
logical_device = 0
elif isinstance(colocate_with, numpy_dataset.SingleDevice):
with ops.device(colocate_with.device):
return next_creator(*args, **kwargs)
else:
device_map = colocate_with.device_map
logical_device = colocate_with.logical_device
def _real_creator(devices, *args, **kwargs):
assert len(devices) == 1
assert devices[0] == self._variable_device
# The chief worker will initialize and broadcast the value to
# the other workers. Always done on the host.
kwargs["initial_value"] = self._get_variable_creator_initial_value(
replica_id=0, # First (and only) replica on each worker.
device=self._host_device,
primary_var=None,
**kwargs)
# We always place sync-on-read variables on the IPU. They will
# be transfered and reduced on the hosts only when read.
synchronization = kwargs.get("synchronization")
if (not self._variables_on_host or synchronization
== variable_scope.VariableSynchronization.ON_READ):
with ops.device(self._ipu_device):
return [next_creator(*args, **kwargs)]
# Cache a snapshot of the variable on the IPU device,
# otherwise the XLA cluster containing the ops consuming the
# variable might be moved to the host to be colocated with it.
kwargs["caching_device"] = self._ipu_device
# In case we are inside an ipu_jit_scope, we need to override it
# to disable XLA for variable initialization on the host.
disable_xla = {
"_XlaCompile": attr_value_pb2.AttrValue(b=False),
"_XlaScope": attr_value_pb2.AttrValue(s=b''),
}
graph = ops.get_default_graph()
with ops.device(self._host_device), \
graph._attr_scope(disable_xla): # pylint: disable=protected-access
return [next_creator(*args, **kwargs)]
# For tf1: use distribute_lib.create_mirrored_variable
return values.create_mirrored_variable(self._container_strategy(),
device_map, logical_device,
_real_creator,
IPUMirroredVariable,
IPUSyncOnReadVariable, *args,
**kwargs)
def testGetEager(self):
with ops.device("/device:CPU:0"):
one = constant_op.constant(1)
two = constant_op.constant(2)
device_map = values.ReplicaDeviceMap(("/device:CPU:0", "/device:GPU:0"))
v = values.DistributedValues(device_map, (one, two))
self.assertEqual(two, v.get("/device:GPU:0"))
self.assertEqual(one, v.get())
with self.assertRaises(ValueError):
self.assertIsNone(v.get("/device:GPU:2"))
def __init__(self, container_strategy, ipu_device, cpu_device):
super().__init__(container_strategy)
self._ipu_device = ipu_device
self._cpu_device = cpu_device
device_map = values.ReplicaDeviceMap([self._cpu_device])
worker_device_pairs = [("", [self._cpu_device])]
self._input_workers = input_lib.InputWorkers(device_map,
worker_device_pairs)
def _test_dataset(self, dataset_fn, worker_devices, devices,
expected_values):
device_map = values.ReplicaDeviceMap(devices)
input_workers = values.InputWorkers(device_map, worker_devices)
multi_worker_dataset = values.MultiWorkerDataset(
dataset_fn, input_workers)
multi_worker_iterator = multi_worker_dataset.make_initializable_iterator()
with self.cached_session() as sess:
sess.run(multi_worker_iterator.initializer)
self._test_iterator(sess, multi_worker_iterator, devices, expected_values)
def testVariableOnAnotherDevice(self):
v = variable_scope.get_variable(
name="v", initializer=[1.], use_resource=True)
device_map = values.ReplicaDeviceMap(("/job:foo/device:CPU:0",))
mirrored = values.MirroredVariable(None, device_map, (v,),
variable_scope.VariableAggregation.MEAN)
self.assertEqual(v.name, mirrored.name)
self.assertEqual(v.dtype, mirrored.dtype)
self.assertEqual(v.shape, mirrored.shape)
def testFetchAMirroredVariable(self, distribution):
with self.session(graph=ops.Graph()) as sess, distribution.scope():
with ops.device("/device:GPU:0"):
v = variable_scope.get_variable(
name="v", initializer=1., use_resource=True)
mirrored = values.MirroredVariable(
distribution, values.ReplicaDeviceMap(("/device:GPU:0",)), (v,),
variable_scope.VariableAggregation.MEAN)
sess.run(variables_lib.global_variables_initializer())
sess.run({"complicated": mirrored})
