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tpu_strategy_util.py
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tpu_strategy_util.py
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# Copyright 2019 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.
# ==============================================================================
"""TPU specific APIs to be used in conjunction with TPU Strategy."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.client import session as session_lib
from tensorflow.python.distribute.cluster_resolver.tpu_cluster_resolver import TPUClusterResolver
from tensorflow.python.eager import context
from tensorflow.python.eager import function
from tensorflow.python.framework import device
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.tpu import topology
from tensorflow.python.tpu import tpu
from tensorflow.python.util import compat
from tensorflow.python.util.tf_export import tf_export
_INITIALIZED_TPU_SYSTEMS = {}
_LOCAL_MASTERS = ("", "local")
@tf_export("tpu.experimental.initialize_tpu_system")
def initialize_tpu_system(cluster_resolver=None):
"""Initialize the TPU devices.
Args:
cluster_resolver: A tf.distribute.cluster_resolver.TPUClusterResolver,
which provides information about the TPU cluster.
Returns:
The tf.tpu.Topology object for the topology of the TPU cluster. If called
inside tf.function, it returns the serialized topology object instead.
Raises:
RuntimeError: If running inside a tf.function.
NotFoundError: If no TPU devices found in eager mode.
"""
job = None
if cluster_resolver is None:
# If no cluster resolver is specified, and running eagerly, execute the init
# ops in the current device scope.
if context.executing_eagerly():
curr_device = device.DeviceSpec.from_string(context.context().device_name)
if curr_device.job is not None:
job = "{}/replica:0/task:0".format(curr_device.job)
cluster_resolver = TPUClusterResolver("")
assert isinstance(cluster_resolver, TPUClusterResolver)
tpu_name = compat.as_text(cluster_resolver._tpu) # pylint: disable=protected-access
if tpu_name in _INITIALIZED_TPU_SYSTEMS:
logging.warning(
"TPU system %s has already been initialized. "
"Reinitializing the TPU can cause previously created "
"variables on TPU to be lost.", tpu_name)
logging.info("Initializing the TPU system: %s", tpu_name)
# This function looks as it is for the following non-intuitive reasons.
# tpu.initialize_system creates a dummy op whose sole purpose is to trigger
# DistributedTPURewritePass. This pass actually adds real ops that
# initialize the TPU system. Thus, we can't simply run tpu.initialize_system
# eagerly. We need to wrap it in defun and trigger the rewrite passes on it.
if tpu_name not in _LOCAL_MASTERS:
# Explicitly place the tpu.initialize_system in the first worker to
# avoid the output node match multiple devices error.
job = "{}/replica:0/task:0".format(cluster_resolver.get_job_name())
if context.executing_eagerly():
@function.defun
def _tpu_init_fn():
# In TF1, we usually close chips when compilation fails to clear the data
# in infeed. In TF2, we don't need to do this because infeed is no longer
# used, so user can recover from TPU compilation failures more smoothly.
return tpu.initialize_system(
job=job, compilation_failure_closes_chips=False)
# The TPU_SYSTEM device must match the device used in tpu.initialize_system
# exactly, otherwise you can get errors if there are multiple TPU_SYSTEM
# devices available.
try:
with ops.device(tpu._tpu_system_device_name(job)): # pylint: disable=protected-access
output = _tpu_init_fn()
context.async_wait()
except errors.InvalidArgumentError as e:
raise errors.NotFoundError(
None, None,
"TPUs not found in the cluster. Failed in initialization: "
+ str(e))
# Clear out the eager context caches since the memory is invalid now.
logging.info("Clearing out eager caches")
context.context()._clear_caches() # pylint: disable=protected-access
serialized_topology = output.numpy()
elif not ops.executing_eagerly_outside_functions():
master = cluster_resolver.master()
cluster_spec = cluster_resolver.cluster_spec()
session_config = config_pb2.ConfigProto(allow_soft_placement=True)
if cluster_spec:
session_config.cluster_def.CopyFrom(cluster_spec.as_cluster_def())
with ops.Graph().as_default():
with session_lib.Session(config=session_config, target=master) as sess:
serialized_topology = sess.run(tpu.initialize_system())
else:
with ops.device(tpu._tpu_system_device_name(job)): # pylint: disable=protected-access
serialized_topology = tpu.initialize_system(
job=job, compilation_failure_closes_chips=False)
# If initialize_tpu_system is called inside tf.function, we only return
# the serialized topology object as the tf.tpu.Topology object has to be
# constructed in eager mode.
return serialized_topology
logging.info("Finished initializing TPU system.")
tpu_topology = topology.Topology(serialized=serialized_topology)
_INITIALIZED_TPU_SYSTEMS[tpu_name] = tpu_topology
return tpu_topology
@tf_export("tpu.experimental.shutdown_tpu_system")
def shutdown_tpu_system(cluster_resolver=None):
"""Shuts down the TPU devices.
This will clear all caches, even those that are maintained through sequential
calls to tf.tpu.experimental.initialize_tpu_system, such as the compilation
cache.
Args:
cluster_resolver: A tf.distribute.cluster_resolver.TPUClusterResolver,
which provides information about the TPU cluster.
Raises:
RuntimeError: If no TPU devices found for eager execution or if run in a
tf.function.
"""
job = None
if cluster_resolver is None:
# If no cluster resolver is specified, and running eagerly, execute the init
# ops in the current device scope.
if context.executing_eagerly():
curr_device = device.DeviceSpec.from_string(context.context().device_name)
if curr_device.job is not None:
job = "{}/replica:0/task:0".format(curr_device.job)
cluster_resolver = TPUClusterResolver("")
assert isinstance(cluster_resolver, TPUClusterResolver)
tpu_name = compat.as_text(cluster_resolver._tpu) # pylint: disable=protected-access
if tpu_name not in _INITIALIZED_TPU_SYSTEMS:
logging.warning("You are shutting down a TPU system %s that has not been "
"initialized." % tpu_name)
logging.info("Shutting down the TPU system: %s", tpu_name)
if context.executing_eagerly():
# This function looks as it is for the following non-intuitive reasons.
# tpu.shutdown_system creates a dummy op whose sole purpose is to trigger
# DistributedTPURewritePass. This pass actually adds real ops that
# shutdown the TPU system. Thus, we can't simply run tpu.shutdown_system
# eagerly. We need to wrap it in defun and trigger the rewrite passes on it.
if tpu_name not in _LOCAL_MASTERS:
# Explicitly place the tpu.shutdown_system in the first worker to
# avoid the output node match multiple devices error.
job = "{}/replica:0/task:0".format(cluster_resolver.get_job_name())
@function.defun
def _tpu_shutdown_fn():
tpu.shutdown_system(job=job)
# The TPU_SYSTEM device must match the device used in tpu.shutdown_system
# exactly, otherwise you can get errors if there are multiple TPU_SYSTEM
# devices available.
with ops.device(tpu._tpu_system_device_name(job)): # pylint: disable=protected-access
_tpu_shutdown_fn()
# Clear out the eager context caches since the memory is invalid now.
logging.info("Clearing out eager caches")
context.context()._clear_caches() # pylint: disable=protected-access
elif not ops.executing_eagerly_outside_functions():
master = cluster_resolver.master()
cluster_spec = cluster_resolver.cluster_spec()
session_config = config_pb2.ConfigProto(allow_soft_placement=True)
if cluster_spec:
session_config.cluster_def.CopyFrom(cluster_spec.as_cluster_def())
with ops.Graph().as_default():
with session_lib.Session(config=session_config, target=master) as sess:
sess.run(tpu.shutdown_system())
else:
raise RuntimeError("initialize_tpu_system is not supported within "
"tf.functions.")
logging.info("Finished shutting down TPU system.")
if tpu_name in _INITIALIZED_TPU_SYSTEMS:
del _INITIALIZED_TPU_SYSTEMS[tpu_name]