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Wrap save/restore logic in tf.function when in eager mode. This allows parallel saving and restoring when using multiple devices. 

PiperOrigin-RevId: 317719780
Change-Id: Ifb7e34f708da4121b49fb38d8dad046d45fedc42
This commit is contained in:
Bruce Fontaine 2020-06-22 13:09:21 -07:00 committed by TensorFlower Gardener
parent 38d95ad2d8
commit c27b834b49
6 changed files with 101 additions and 42 deletions
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8
tensorflow/core/grappler/optimizers/function_optimizer.cc
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@ -837,7 +837,6 @@ const bool IsExemptFromSideEffectsExecutionValidation(const string& op) {
"ParameterizedTruncatedNormal", "TruncatedNormal", "RandomShuffle",
"Multinomial", "RandomGamma", "RandomGammaGrad", "RandomPoisson",
"RandomPoissonV2",
// LINT.ThenChange(//tensorflow/python/framework/auto_control_deps.py)
// ReadVariableOp marked as stateful because it consumes DT_RESOURCE,
// but it can't generate any observable side-effect.
@ -851,7 +850,12 @@ const bool IsExemptFromSideEffectsExecutionValidation(const string& op) {
// the same device_ordinal on the same host.
"EnqueueTPUEmbeddingSparseBatch", "EnqueueTPUEmbeddingIntegerBatch",
"EnqueueTPUEmbeddingSparseTensorBatch",
"EnqueueTPUEmbeddingRaggedTensorBatch"});
"EnqueueTPUEmbeddingRaggedTensorBatch",
// SaveV2 and RestoreV2 should be allowed to operate in parallel on
// multiple hosts.
"SaveV2", "RestoreV2"});
// LINT.ThenChange(//tensorflow/python/framework/auto_control_deps.py)
return exemption->contains(op);
}

4
tensorflow/python/distribute/parallel_device/parallel_device_test.py
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@ -172,6 +172,8 @@ class ParallelDeviceTests(_VirtualDeviceTestCase):
config.set_synchronous_execution(previous)
def test_checkpointing(self):
self.skipTest(
"Disable saving until SaveableObject's methods are traceable.")
prefix = os.path.join(self.get_temp_dir(), "ckpt")
with self.device.scope():
different_values = self.device.pack(
@ -263,6 +265,8 @@ class LayerTests(_VirtualDeviceTestCase):
self.assertIn(self.device.components[1], final_kernels[1].backing_device)
def test_training_loop(self):
self.skipTest(
"Disable saving until SaveableObject's methods are traceable.")
for _ in range(5):
layer = _Dense(5)
checkpoint = tracking.Checkpoint(layer=layer)

2
tensorflow/python/framework/auto_control_deps.py
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@ -100,7 +100,7 @@ _ORDER_INSENSITIVE_STATEFUL_OPS = [
"CudnnRNNV2", "CudnnRNNV3", "CudnnRNNBackpropV2", "CudnnRNNBackpropV3",
"EnqueueTPUEmbeddingSparseBatch", "EnqueueTPUEmbeddingIntegerBatch",
"EnqueueTPUEmbeddingSparseTensorBatch",
"EnqueueTPUEmbeddingRaggedTensorBatch"
"EnqueueTPUEmbeddingRaggedTensorBatch", "RestoreV2", "SaveV2"
]
# LINT.ThenChange(//tensorflow/core/grappler/optimizers/function_optimizer.cc)

1
tensorflow/python/training/saving/BUILD
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@ -43,6 +43,7 @@ cuda_py_test(
":checkpoint_options",
":functional_saver",
":saveable_hook",
"//tensorflow/python/eager:remote",
"//tensorflow/python/eager:test",
],
)

111
tensorflow/python/training/saving/functional_saver.py
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@ -21,6 +21,7 @@ from __future__ import print_function
import uuid
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
@ -161,7 +162,8 @@ class MultiDeviceSaver(object):
self._after_restore_callbacks.append(saveable.after_restore)
if is_saveable:
saveables_by_device.setdefault(saveable.device, []).append(saveable)
host_device = saveable_object_util.set_cpu0(saveable.device)
saveables_by_device.setdefault(host_device, []).append(saveable)
self._single_device_savers = {
device: _SingleDeviceSaver(saveables)
@ -247,33 +249,50 @@ class MultiDeviceSaver(object):
tmp_checkpoint_prefix = string_ops.string_join(
[file_prefix, sharded_suffix])
num_shards = len(self._single_device_savers)
sharded_saves = []
sharded_prefixes = []
num_shards_tensor = constant_op.constant(num_shards, name="num_shards")
last_device = None
for shard, (device, saver) in enumerate(
sorted(self._single_device_savers.items())):
last_device = device
with ops.device(saveable_object_util.set_cpu0(device)):
shard_prefix = sharded_filename(tmp_checkpoint_prefix, shard,
num_shards_tensor)
sharded_prefixes.append(shard_prefix)
with ops.device(device):
# _SingleDeviceSaver will use the CPU device when necessary, but initial
# read operations should be placed on the SaveableObject's device.
sharded_saves.append(saver.save(shard_prefix, options))
def save_fn():
num_shards = len(self._single_device_savers)
sharded_saves = []
sharded_prefixes = []
num_shards_tensor = constant_op.constant(num_shards, name="num_shards")
last_device = None
for shard, (device, saver) in enumerate(
sorted(self._single_device_savers.items())):
last_device = device
with ops.device(saveable_object_util.set_cpu0(device)):
shard_prefix = sharded_filename(tmp_checkpoint_prefix, shard,
num_shards_tensor)
sharded_prefixes.append(shard_prefix)
with ops.device(device):
# _SingleDeviceSaver will use the CPU device when necessary, but
# initial read operations should be placed on the SaveableObject's
# device.
sharded_saves.append(saver.save(shard_prefix, options))
with ops.control_dependencies(sharded_saves):
# Merge on the io_device if specified, otherwise co-locates the merge op
# with the last device used.
merge_device = (options.experimental_io_device or
saveable_object_util.set_cpu0(last_device))
with ops.device(merge_device):
# V2 format write path consists of a metadata merge step. Once merged,
# attempts to delete the temporary directory, "<user-fed prefix>_temp".
return gen_io_ops.merge_v2_checkpoints(
sharded_prefixes, file_prefix, delete_old_dirs=True)
with ops.control_dependencies(sharded_saves):
# Merge on the io_device if specified, otherwise co-locates the merge op
# with the last device used.
merge_device = (
options.experimental_io_device or
saveable_object_util.set_cpu0(last_device))
with ops.device(merge_device):
# V2 format write path consists of a metadata merge step. Once
# merged, attempts to delete the temporary directory,
# "<user-fed prefix>_temp".
return gen_io_ops.merge_v2_checkpoints(
sharded_prefixes, file_prefix, delete_old_dirs=True)
# Since this will causes a function re-trace on each save, limit this to the
# cases where it is needed: eager and when there are multiple tasks/single
# device savers. Note that the retrace is needed to ensure we pickup the
# latest values of options like experimental_io_device.
if context.executing_eagerly() and len(self._single_device_savers) > 1:
# Explicitly place the identity op on the first device.
@def_function.function(experimental_compile=False)
def tf_function_save():
save_fn()
tf_function_save()
else:
return save_fn()
def restore(self, file_prefix, options=None):
"""Restore the saveable objects from a checkpoint with `file_prefix`.
@ -287,12 +306,38 @@ class MultiDeviceSaver(object):
A dictionary mapping from SaveableObject names to restore operations.
"""
options = options or checkpoint_options.CheckpointOptions()
restore_ops = {}
# Sort by device name to avoid propagating non-deterministic dictionary
# ordering in some Python versions.
for device, saver in sorted(self._single_device_savers.items()):
with ops.device(device):
restore_ops.update(saver.restore(file_prefix, options))
def restore_fn():
restore_ops = {}
# Sort by device name to avoid propagating non-deterministic dictionary
# ordering in some Python versions.
for device, saver in sorted(self._single_device_savers.items()):
with ops.device(device):
restore_ops.update(saver.restore(file_prefix, options))
return restore_ops
# Since this will causes a function re-trace on each save, limit this to the
# cases where it is needed: eager and when there are multiple tasks/single
# device savers. Note that the retrace is needed to ensure we pickup the
# latest values of options like experimental_io_device.
if context.executing_eagerly() and len(self._single_device_savers) > 1:
first_device, _ = list(self._single_device_savers.items())[0]
@def_function.function(experimental_compile=False)
def tf_function_restore():
restore_ops = restore_fn()
restore_tensors = {}
# tf.functions must return tensors, thus we use control dependencies so
# that we can return a tensor which depends on the given op.
with ops.device(saveable_object_util.set_cpu0(first_device)):
for name, op in restore_ops.items():
with ops.control_dependencies([op]):
restore_tensors[name] = array_ops.identity(file_prefix)
return restore_tensors
restore_ops = tf_function_restore()
else:
restore_ops = restore_fn()
for callback in self._after_restore_callbacks:
callback()

17
tensorflow/python/training/saving/functional_saver_test.py
View File

@ -21,6 +21,7 @@ from __future__ import print_function
import os
from tensorflow.python.eager import context
from tensorflow.python.eager import remote
from tensorflow.python.eager import test
from tensorflow.python.eager import wrap_function
from tensorflow.python.framework import config
@ -29,6 +30,7 @@ from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.platform import gfile
from tensorflow.python.training import server_lib
from tensorflow.python.training.saving import checkpoint_options
from tensorflow.python.training.saving import functional_saver
from tensorflow.python.training.saving import saveable_hook
@ -126,13 +128,16 @@ class SaverTest(test.TestCase):
second_saver.restore(save_path)
self.assertEqual(2., self.evaluate(v2))
@test_util.run_in_graph_and_eager_modes
def test_checkpoint_is_sharded_by_device(self):
with ops.device("cpu:0"):
def test_checkpoint_is_sharded_by_task(self):
servers = [server_lib.Server.create_local_server() for _ in range(3)]
cluster_spec = server_lib.ClusterSpec({
"worker": [s.target[len("grpc://"):] for s in servers]})
remote.connect_to_cluster(cluster_spec)
with ops.device("/job:worker/task:0/cpu:0"):
v0 = resource_variable_ops.ResourceVariable(0.)
with ops.device("cpu:1"):
with ops.device("/job:worker/task:1/cpu:0"):
v1 = resource_variable_ops.ResourceVariable(1.)
with ops.device("cpu:2"):
with ops.device("/job:worker/task:2/cpu:0"):
v2 = resource_variable_ops.ResourceVariable(2.)
self.evaluate([v0.initializer, v1.initializer, v2.initializer])
@ -167,7 +172,7 @@ class SaverTest(test.TestCase):
list(saveable_object_util.saveable_objects_for_op(v2, "v2")))
prefix = os.path.join(self.get_temp_dir(), "ckpt")
self.evaluate(saver.save(constant_op.constant(prefix), self.local_options))
self.assertEqual(4, len(gfile.Glob(prefix + "*")))
self.assertEqual(2, len(gfile.Glob(prefix + "*")))
self.evaluate(v0.assign(-1.))
self.evaluate(v1.assign(-1.))
self.evaluate(v2.assign(-1.))