def testRemote(self):
gpus = config.list_logical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
context.ensure_initialized()
gpus = config.list_logical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
for gpu in gpus:
self.assertIsNotNone(gpu.name)
context.ensure_initialized()
job_name = 'test'
cluster_def = cluster_pb2.ClusterDef()
job_def = cluster_def.job.add()
job_def.name = job_name
job_def.tasks[0] = 'localhost:0'
server_def = tensorflow_server_pb2.ServerDef(
cluster=cluster_def, job_name=job_name, task_index=0, protocol='grpc')
context.set_server_def(server_def)
gpus = config.list_logical_devices('GPU')
for gpu in gpus:
self.assertIsNotNone(gpu.name)
def testCpuMultiple(self):
cpus = config.list_physical_devices('CPU')
self.assertEqual(len(cpus), 1)
config.set_virtual_device_configuration(cpus[0], [
context.VirtualDeviceConfiguration(),
context.VirtualDeviceConfiguration()
])
context.ensure_initialized()
cpus = config.list_logical_devices('CPU')
self.assertEqual(len(cpus), 2)
with ops.device('/device:CPU:0'):
a = constant_op.constant(1.0)
self.evaluate(a)
with ops.device('/device:CPU:1'):
b = constant_op.constant(1.0)
self.evaluate(b)
with self.assertRaisesRegexp(RuntimeError, 'unknown device'):
with ops.device('/device:CPU:2'):
c = constant_op.constant(1.0)
self.evaluate(c)
# Ensure we can place ops on each of the device names
for cpu in cpus:
with ops.device(cpu.name):
d = constant_op.constant(1.0)
self.evaluate(d)
def testCpuMultiple(self):
cpus = config.list_physical_devices('CPU')
self.assertEqual(len(cpus), 1)
config.set_logical_device_configuration(cpus[0], [
context.LogicalDeviceConfiguration(),
context.LogicalDeviceConfiguration()
])
context.ensure_initialized()
vcpus = config.list_logical_devices('CPU')
self.assertEqual(len(vcpus), 2)
with ops.device('/device:CPU:0'):
a = constant_op.constant(1.0)
self.evaluate(a)
with ops.device('/device:CPU:1'):
b = constant_op.constant(1.0)
self.evaluate(b)
with ops.device('/device:CPU:2'):
c = constant_op.constant(1.0)
self.evaluate(c)
self.assertIn('CPU:0', c.device)
# Ensure we can place ops on each of the device names
for vcpu in vcpus:
with ops.device(vcpu.name):
d = constant_op.constant(1.0)
self.evaluate(d)
# Modifying the CPU configuration is not supported
with self.assertRaisesRegex(RuntimeError, 'cannot be modified'):
config.set_logical_device_configuration(cpus[0], [
context.LogicalDeviceConfiguration(),
context.LogicalDeviceConfiguration(),
context.LogicalDeviceConfiguration()
])
# Setting the same CPU configuration is fine
config.set_logical_device_configuration(cpus[0], [
context.LogicalDeviceConfiguration(),
context.LogicalDeviceConfiguration()
])
def testVirtualGpu(self):
config.set_soft_device_placement(False)
gpus = config.list_physical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
self.assertIsNone(config.get_logical_device_configuration(gpus[-1]))
config.set_logical_device_configuration(gpus[-1], [
context.LogicalDeviceConfiguration(memory_limit=10),
context.LogicalDeviceConfiguration(memory_limit=10)
])
self.assertEqual(len(config.get_logical_device_configuration(gpus[-1])), 2)
logical_gpus = config.list_logical_devices('GPU')
self.assertTrue(len(logical_gpus), len(gpus) + 1)
for i in range(0, len(logical_gpus)):
with ops.device('/device:GPU:' + str(i)):
a = array_ops.identity(1.0)
self.evaluate(a)
with self.assertRaisesRegex(errors.InvalidArgumentError,
'Could not satisfy'):
with ops.device('/device:GPU:' + str(len(logical_gpus))):
a = array_ops.identity(1.0)
self.evaluate(a)
# Modifying the GPU configuration is not supported
with self.assertRaisesRegex(RuntimeError, 'cannot be modified'):
config.set_logical_device_configuration(gpus[-1], [
context.LogicalDeviceConfiguration(memory_limit=20),
context.LogicalDeviceConfiguration(memory_limit=20)
])
with self.assertRaisesRegex(RuntimeError, 'cannot be modified'):
config.set_logical_device_configuration(gpus[-1], [
context.LogicalDeviceConfiguration(memory_limit=10),
context.LogicalDeviceConfiguration(memory_limit=10),
context.LogicalDeviceConfiguration(memory_limit=10)
])
# Setting the same GPU configuration is fine
config.set_logical_device_configuration(gpus[-1], [
context.LogicalDeviceConfiguration(memory_limit=10),
context.LogicalDeviceConfiguration(memory_limit=10)
])
def testVirtualGpu(self):
gpus = config.list_physical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
self.assertIsNone(config.get_logical_device_configuration(gpus[-1]))
config.set_logical_device_configuration(gpus[-1], [
context.LogicalDeviceConfiguration(memory_limit=10),
context.LogicalDeviceConfiguration(memory_limit=10)
])
self.assertEqual(
len(config.get_logical_device_configuration(gpus[-1])), 2)
logical_gpus = config.list_logical_devices('GPU')
self.assertTrue(len(logical_gpus), len(gpus) + 1)
for i in range(0, len(logical_gpus)):
with ops.device('/device:GPU:' + str(i)):
a = constant_op.constant(1.0)
self.evaluate(a)
with self.assertRaisesRegexp(RuntimeError, 'unknown device'):
with ops.device('/device:GPU:' + str(len(logical_gpus))):
a = constant_op.constant(1.0)
self.evaluate(a)
# Modifying the GPU configuration is not supported
with self.assertRaisesRegexp(RuntimeError, 'cannot be modified'):
config.set_logical_device_configuration(gpus[-1], [
context.LogicalDeviceConfiguration(memory_limit=20),
context.LogicalDeviceConfiguration(memory_limit=20)
])
with self.assertRaisesRegexp(RuntimeError, 'cannot be modified'):
config.set_logical_device_configuration(gpus[-1], [
context.LogicalDeviceConfiguration(memory_limit=10),
context.LogicalDeviceConfiguration(memory_limit=10),
context.LogicalDeviceConfiguration(memory_limit=10)
])
# Setting the same GPU configuration is fine
config.set_logical_device_configuration(gpus[-1], [
context.LogicalDeviceConfiguration(memory_limit=10),
context.LogicalDeviceConfiguration(memory_limit=10)
])
def _set_num_gpus(self):
devices = config.list_logical_devices("GPU")
per_worker_gpus = {}
for d in devices:
d_spec = tf_device.DeviceSpec.from_string(d.name)
if d_spec.device_type == "GPU" and d_spec.job == "worker":
# TODO(b/167894802): update if worker name is customizable
job_spec = d_spec.replace(device_type=None, device_index=None)
per_worker_gpus[job_spec] = per_worker_gpus.get(job_spec,
0) + 1
num_gpus = 0
for _, count in per_worker_gpus.items():
if num_gpus > 0 and count != num_gpus:
raise ValueError("Mismatched number of GPUs per worker")
num_gpus = count
self._num_gpus_per_worker = num_gpus
logging.info(f"Number of GPUs on workers: {self._num_gpus_per_worker}")
def testKeepLogicalDevice(self):
# Cannot change logical device after the context initialization.
context._reset_context() # pylint: disable=protected-access
cluster_spec = multi_worker_test_base.create_cluster_spec(
has_chief=False, num_workers=1)
resolver = cluster_resolver_lib.SimpleClusterResolver(
cluster_spec=multi_worker_util.normalize_cluster_spec(cluster_spec),
task_type='worker',
task_id=0)
gpus = tf_config.list_physical_devices('GPU')
tf_config.set_logical_device_configuration(gpus[-1], [
context.LogicalDeviceConfiguration(64),
context.LogicalDeviceConfiguration(64),
])
collective_all_reduce_strategy.CollectiveAllReduceStrategy(
cluster_resolver=resolver)
# Since we create two logical GPUs out of the last GPU, there should be one
# more logical GPUs than physical GPUs.
self.assertLen(tf_config.list_logical_devices('GPU'), len(gpus) + 1)
context._reset_context() # pylint: disable=protected-access
def testForcedCompile(self):
"""Tests whole-function forced-compilation.
This test checks that stateless_random_* can be used in forced-compilation
scenarios (e.g. TPU). The new version of stateless_random_* requires the
intermediate tensor `alg` to be compile-time constant, so we need to check
that this requirement is met. We use xla.compile instead of tf.function's
jit_compile because the latter doesn't throw an error even if the
compile-time-constant constraint is not met.
"""
if config.list_logical_devices('TPU'):
self.skipTest(
'To accommodate OSS, xla.compile support for TPU is not '
'linked in.')
@def_function.function
def f(x):
return xla.compile(
lambda x: stateless.stateless_random_normal([], seed=x), [x])
f([1, 2])
def testVirtualGpu(self):
gpus = config.list_physical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
self.assertIsNone(config.get_virtual_device_configuration(gpus[-1]))
config.set_virtual_device_configuration(gpus[-1], [
context.VirtualDeviceConfiguration(memory_limit=10),
context.VirtualDeviceConfiguration(memory_limit=10)
])
self.assertEqual(len(config.get_virtual_device_configuration(gpus[-1])), 2)
logical_gpus = config.list_logical_devices('GPU')
self.assertTrue(len(logical_gpus), len(gpus) + 1)
for i in range(0, len(logical_gpus)):
with ops.device('/device:GPU:' + str(i)):
a = constant_op.constant(1.0)
self.evaluate(a)
with self.assertRaisesRegexp(RuntimeError, 'unknown device'):
with ops.device('/device:GPU:' + str(len(logical_gpus))):
a = constant_op.constant(1.0)
self.evaluate(a)
def testVirtualGpu(self):
gpus = config.list_physical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
self.assertIsNone(config.get_virtual_device_configuration(gpus[-1]))
config.set_virtual_device_configuration(gpus[-1], [
context.VirtualDeviceConfiguration(memory_limit=10),
context.VirtualDeviceConfiguration(memory_limit=10)
])
self.assertEqual(len(config.get_virtual_device_configuration(gpus[-1])), 2)
logical_gpus = config.list_logical_devices('GPU')
self.assertTrue(len(logical_gpus), len(gpus) + 1)
for i in range(0, len(logical_gpus)):
with ops.device('/device:GPU:' + str(i)):
a = constant_op.constant(1.0)
self.evaluate(a)
with self.assertRaisesRegexp(RuntimeError, 'unknown device'):
with ops.device('/device:GPU:' + str(len(logical_gpus))):
a = constant_op.constant(1.0)
self.evaluate(a)
def testGpuNone(self):
config.set_soft_device_placement(False)
gpus = config.list_physical_devices('GPU')
self.assertGreater(len(gpus), 0)
cpus = config.list_physical_devices('CPU')
self.assertEqual(len(cpus), 1)
self.assertEqual(len(config.get_visible_devices('CPU')), 1)
self.assertGreater(len(config.get_visible_devices('GPU')), 0)
# get_visible_devices filters out XLA_* devices. list_logical_devices does
# not, but we can't call it here because it initializes the devices and
# calling set_visible_devices after that is disallowed.
self.assertEqual(len(config.get_visible_devices('XLA_GPU')), 0)
config.set_visible_devices(cpus[0])
self.assertEqual(len(config.get_visible_devices('CPU')), 1)
self.assertEqual(len(config.get_visible_devices('GPU')), 0)
self.assertEqual(len(config.list_logical_devices('XLA_GPU')), 0)
with self.assertRaisesRegexp(errors.InvalidArgumentError,
'Could not satisfy'):
with ops.device('/device:GPU:0'):
a = array_ops.identity(1.0)
self.evaluate(a)
with self.assertRaisesRegexp(errors.InvalidArgumentError,
'Could not satisfy'):
with ops.device('/device:XLA_GPU:0'):
a = array_ops.identity(1.0)
self.evaluate(a)
# Modifying the visible devices is not supported
with self.assertRaisesRegexp(RuntimeError, 'cannot be modified'):
config.set_visible_devices(gpus)
# Setting the same visible devices is fine
config.set_visible_devices(cpus[0])
def testGpuGrowth(self):
gpus = config.list_physical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
self.assertIsNone(config.get_memory_growth(gpus[-1]))
for gpu in gpus:
config.set_memory_growth(gpu, True)
c = context.context().config
self.assertTrue(c.gpu_options.allow_growth)
logical_gpus = config.list_logical_devices('GPU')
self.assertTrue(len(logical_gpus), len(gpus))
# Modifying the GPU configuration is not supported
with self.assertRaisesRegex(RuntimeError, 'cannot be modified'):
for gpu in gpus:
config.set_memory_growth(gpu, False)
# Setting the same GPU configuration is fine
for gpu in gpus:
config.set_memory_growth(gpu, True)
def _make_device_specs(
devices: Optional[List[str]] = None,
device_type: Optional[str] = None
) -> Tuple[List[tf_device.DeviceSpec], str]:
"""Makes device specs from local devices names or number of global devices."""
if devices is None:
if device_type is None:
device_type = 'CPU'
devices = [
tf_device.DeviceSpec.from_string(d.name)
for d in tf_config.list_logical_devices(device_type)
]
else:
devices = [tf_device.DeviceSpec.from_string(d) for d in devices]
if device_type is None:
device_type = devices[0].device_type
if device_type.upper() != devices[0].device_type.upper():
raise ValueError(
f'Conflicting devices {str(devices)} and device_type {device_type}'
)
return devices, device_type
def build(self, input_shape):
# Note: most sparse optimizers do not have GPU kernels defined. When
# building graphs, the placement algorithm is able to place variables on CPU
# since it knows all kernels using the variable only exist on CPU.
# When eager execution is enabled, the placement decision has to be made
# right now. Checking for the presence of GPUs to avoid complicating the
# TPU codepaths which can handle sparse optimizers.
if context.executing_eagerly() and tf_config.list_logical_devices('GPU'):
with ops.device('cpu:0'):
self.embeddings = self.add_weight(
shape=(self.input_dim, self.output_dim),
initializer=self.embeddings_initializer,
name='embeddings',
regularizer=self.embeddings_regularizer,
constraint=self.embeddings_constraint)
else:
self.embeddings = self.add_weight(
shape=(self.input_dim, self.output_dim),
initializer=self.embeddings_initializer,
name='embeddings',
regularizer=self.embeddings_regularizer,
constraint=self.embeddings_constraint)
self.built = True
def testWrapFuncDatasetDevice(self, device_type, dataset_reduce_fn):
devices = config.list_logical_devices(device_type=device_type)
if not devices:
self.skipTest(
'Skip when {} is not detected by TF'.format(device_type))
@def_function.function
def comp():
return dataset_reduce_fn(dataset_ops.Dataset.range(10))
graph = comp.get_concrete_function().graph
def function_to_wrap():
with ops.device(devices[0].name):
return graph_def_importer.import_graph_def(
graph.as_graph_def())
with ops.device(devices[0].name):
wrapped_noarg_fn = wrap_function.wrap_function(function_to_wrap,
signature=[])
wrapped_noarg_fn()
def testForcedCompile(self):
"""Tests whole-function forced-compilation.
This test checks that stateless_random_* can be used in forced-compilation
scenarios (e.g. TPU). The new version of stateless_random_* requires the
intermediate tensor `alg` to be compile-time constant, so we need to check
that this requirement won't prevent `seed` from depending on variables.
"""
if config.list_logical_devices('TPU'):
self.skipTest('To accommodate OSS, experimental_compile support for TPU '
'is not linked in.')
# GPU doesn't support int32 variables, so we use int64.
v = variables.Variable([1, 2], dtype=dtypes.int64)
@def_function.function(experimental_compile=True)
def f():
key, counter = (
gen_stateless_random_ops_v2.stateless_random_get_key_counter(
seed=math_ops.cast(v.read_value(), dtypes.int32)))
alg = gen_stateless_random_ops_v2.stateless_random_get_alg()
return gen_stateless_random_ops_v2.stateless_random_normal_v2(
shape=[], key=key, counter=counter, alg=alg)
f()
def testGpuNone(self):
gpus = config.list_physical_devices('GPU')
self.assertGreater(len(gpus), 0)
cpus = config.list_physical_devices('CPU')
self.assertEqual(len(cpus), 1)
self.assertEqual(len(config.get_visible_devices('CPU')), 1)
self.assertGreater(len(config.get_visible_devices('GPU')), 0)
# get_visible_devices filters out XLA_* devices. list_logical_devices does
# not, but we can't call it here because it initializes the devices and
# calling set_visible_devices after that is disallowed.
self.assertEqual(len(config.get_visible_devices('XLA_GPU')), 0)
config.set_visible_devices(cpus[0])
self.assertEqual(len(config.get_visible_devices('CPU')), 1)
self.assertEqual(len(config.get_visible_devices('GPU')), 0)
self.assertEqual(len(config.list_logical_devices('XLA_GPU')), 0)
with self.assertRaisesRegexp(RuntimeError, 'unknown device'):
with ops.device('/device:GPU:0'):
a = constant_op.constant(1.0)
self.evaluate(a)
with self.assertRaisesRegexp(RuntimeError, 'unknown device'):
with ops.device('/device:XLA_GPU:0'):
a = constant_op.constant(1.0)
self.evaluate(a)
# Modifying the visible devices is not supported
with self.assertRaisesRegexp(RuntimeError, 'cannot be modified'):
config.set_visible_devices(gpus)
# Setting the same visible devices is fine
config.set_visible_devices(cpus[0])
def all_local_devices(num_gpus=None):
devices = config.list_logical_devices("GPU")
if num_gpus is not None:
devices = devices[:num_gpus]
return devices or config.list_logical_devices("CPU")
def _query_tpu_system_metadata(master_address, cluster_def=None,
query_topology=False):
"""Automatically detects the TPU system metadata in the system."""
tpu_core_count = 0
devices = []
device_dict = collections.defaultdict(list)
if context.executing_eagerly():
logical_devices = config.list_logical_devices()
# We want the output type to match in both eager and session mode
devices = [session_lib._DeviceAttributes(device_util.canonicalize(d.name), # pylint: disable=protected-access
d.device_type, 0, 0)
for d in logical_devices]
else:
# TODO(b/120564445): Replace with standard library for retries.
retry_count = 1
while True:
logging.info('Querying Tensorflow master (%s) for TPU system metadata.',
master_address)
try:
with ops.Graph().as_default():
with session_lib.Session(
master_address,
config=get_session_config_with_timeout(
_PINGING_MASTER_TIMEOUT_IN_MS,
cluster_def)) as sess:
devices = sess.list_devices()
break
except errors.DeadlineExceededError:
msg = ('Failed to connect to the Tensorflow master. The TPU worker may '
'not be ready (still scheduling) or the Tensorflow master '
'address is incorrect: got (%s).' %
(master_address))
# TODO(xiejw): For local or grpc master we might not need retry logic
# here.
if retry_count <= _RETRY_TIMES:
logging.warning('%s', msg)
logging.warning('Retrying (%d/%d).', retry_count, _RETRY_TIMES)
retry_count += 1
else:
raise ValueError(msg)
for device in devices:
spec = tf_device.DeviceSpec.from_string(device.name)
if spec.device_type == 'TPU':
device_dict[spec.task].append(spec.device_index)
tpu_core_count += 1
num_of_cores_per_host = 0
if tpu_core_count:
num_cores_per_host_set = set(
[len(core_ids) for core_ids in device_dict.values()])
if len(num_cores_per_host_set) != 1:
raise RuntimeError(
'TPU cores on each host is not same. This should not happen!. '
'devices: {}'.format(devices))
num_of_cores_per_host = num_cores_per_host_set.pop()
topology = None
if query_topology:
if not tpu_core_count:
raise RuntimeError(
'Cannot find any TPU cores in the system (master address {}). '
'This usually means the master address is incorrect or the '
'TPU worker has some problems. Available devices: {}'.format(
master_address, devices))
topology = _obtain_topology(master_address, cluster_def)
# We sort the metadata devices so that downstream users get a sorted list
# for creating mirrored variables correctly.
def _sort_key(device):
spec = tf_device.DeviceSpec.from_string(device.name)
return (spec.job, spec.replica, spec.task, spec.device_type,
spec.device_index)
devices = tuple(sorted(devices, key=_sort_key))
metadata = _TPUSystemMetadata(
num_cores=tpu_core_count,
num_hosts=len(device_dict),
num_of_cores_per_host=num_of_cores_per_host,
topology=topology,
devices=devices)
if tpu_core_count:
logging.info('Found TPU system:')
logging.info('*** Num TPU Cores: %d', metadata.num_cores)
logging.info('*** Num TPU Workers: %d', metadata.num_hosts)
logging.info('*** Num TPU Cores Per Worker: %d',
metadata.num_of_cores_per_host)
for device in metadata.devices:
logging.info('*** Available Device: %s', device)
else:
logging.info('Failed to find TPU: %s', metadata)
return metadata
def create_mesh(mesh_dims: Optional[List[Tuple[str, int]]] = None,
mesh_name: str = '',
devices: Optional[List[str]] = None,
device_type: Optional[str] = None) -> layout.Mesh:
"""Creates a single-client mesh.
If both `mesh_dims` and `devices` are specified, they must match each otehr.
As a special case, when all arguments are missing, this creates a 1D CPU mesh
with an empty name, assigning all available devices to that dimension.
Args:
mesh_dims: A list of (dim_name, dim_size) tuples. Defaults to a single
batch-parallel dimension called 'x' using all devices. As a special case,
a single-element mesh_dims whose dim_size is -1 also uses all devices.
mesh_name: Name of the created mesh. Defaults to ''.
devices: String representations of devices to use. This is the device part
of tf.DeviceSpec, e.g. 'CPU:0'. Defaults to all available logical devices.
device_type: If `devices` is missing, the type of devices to use. Defaults
to 'CPU'.
Returns:
A single-client mesh created from specified or default arguments.
"""
if devices is None:
if device_type is None:
device_type = 'CPU'
devices = [
tf_device.DeviceSpec.from_string(d.name)
for d in tf_config.list_logical_devices(device_type)
]
else:
devices = [tf_device.DeviceSpec.from_string(d) for d in devices]
if device_type is None:
device_type = devices[0].device_type
if device_type.upper() != devices[0].device_type.upper():
raise ValueError(
f'Conflicting devices {str(devices)} and device_type {device_type}'
)
local_spec = tf_device.DeviceSpec(job='localhost', replica=0, task=0)
devices = [local_spec.make_merged_spec(d) for d in devices]
if mesh_dims is None:
mesh_dims = [('x', len(devices))]
elif len(mesh_dims) == 1 and mesh_dims[0][1] == -1:
# Replace -1 dim_size in a 1D mesh will the number of all devices.
mesh_dims[0] = (mesh_dims[0][0], len(devices))
dim_names = [d[0] for d in mesh_dims]
shape = [d[1] for d in mesh_dims]
global_device_ids = np.arange(len(devices)).reshape(shape)
local_device_ids = np.ravel(global_device_ids).tolist()
mesh = layout.Mesh(dim_names=dim_names,
global_device_ids=global_device_ids,
local_device_ids=local_device_ids,
local_devices=devices,
mesh_name=mesh_name)
_print_context(num_global_devices=len(devices),
num_clients=1,
client_id=0,
device_type=devices[0].device_type,
mesh=mesh)
return mesh
def create_central_storage_strategy():
"""Create a CentralStorageStrategy, using a GPU if it is available."""
compute_devices = ['cpu:0', 'gpu:0'] if (
tf_config.list_logical_devices('GPU')) else ['cpu:0']
return central_storage_strategy.CentralStorageStrategy(
compute_devices, parameter_device='cpu:0')
def create_mirrored_strategy():
"""Create a MirroredStrategy, using a GPU if it is available."""
if tf_config.list_logical_devices('GPU'):
return mirrored_strategy.MirroredStrategy(['cpu:0', 'gpu:0'])
else:
return mirrored_strategy.MirroredStrategy(['cpu:0'])
def create_mirrored_strategy():
if tf_config.list_logical_devices('GPU'):
return mirrored_strategy.MirroredStrategy(['cpu:0', 'gpu:0'])
else:
return mirrored_strategy.MirroredStrategy(['cpu:0'])