562 lines
22 KiB
Python
562 lines
22 KiB
Python
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# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Utilities for saving/loading Trackable objects asynchronously."""
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import atexit
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import collections
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import copy
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import threading
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import time
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import weakref
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from absl import logging
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from tensorflow.python.distribute import device_util
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from tensorflow.python.distribute.sharded_variable import ShardedVariable
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from tensorflow.python.eager import context
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from tensorflow.python.eager import def_function
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from tensorflow.python.eager import executor
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from tensorflow.python.framework import device as pydev
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from tensorflow.python.framework import ops
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from tensorflow.python.ops.resource_variable_ops import UninitializedVariable
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from tensorflow.python.ops.variables import Variable
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from tensorflow.python.saved_model.pywrap_saved_model import metrics
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from tensorflow.python.tpu.tpu_embedding_v2 import TPUEmbedding
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from tensorflow.python.training import optimizer as optimizer_v1
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from tensorflow.python.util import object_identity
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# Captures the timestamp of the first Checkpoint instantiation or end of a write
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# operation. Can be accessed by multiple Checkpoint instances.
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_END_TIME_OF_LAST_ASYNC_WRITE = None
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_END_TIME_OF_LAST_ASYNC_WRITE_LOCK = threading.Lock()
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# API label for cell names used in async checkpoint metrics.
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_ASYNC_CHECKPOINT = "async_checkpoint"
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def _get_duration_microseconds(start_time_seconds, end_time_seconds):
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"""Calculate the duration between start and end time.
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Args:
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start_time_seconds: The start time in seconds.
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end_time_seconds: The end time in seconds.
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Returns:
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The duration between the start and the end time. Return 0 if
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end_time_seconds < start_time_seconds.
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"""
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if end_time_seconds < start_time_seconds:
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# Avoid returning negative value in case of clock skew.
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return 0
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return round((end_time_seconds - start_time_seconds) * 1000000)
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class AsyncCheckpointHelper:
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"""Helper class for async checkpoint."""
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def __init__(self, checkpointer_impl, root=None, **kwargs):
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"""Initialize AsyncCheckpoint.
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Args:
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checkpointer_impl: The Checkpoint class to power the AsyncCheckpoint.
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root: The root object to checkpoint. `root` may be a trackable object or
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`WeakRef` of a trackable object.
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**kwargs: The keyword arguments representing the checkpointed variables.
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"""
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# TODO(chienchunh): Make sure the processing for the root object is
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# consistent when integrating with the public API, e.g., adding all kwarg
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# items as the child of the root object.
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if root:
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trackable_root = root() if isinstance(root, weakref.ref) else root
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kwargs["root"] = trackable_root
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trackable_root._maybe_initialize_trackable()
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self._checkpointer_impl = checkpointer_impl
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self._checkpoint_items = kwargs
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# The underlying Checkpoint instance and its items.
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self._checkpoint = None
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self._checkpoint_options = None
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# The callback function that needs to be executed after checkpoint write.
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# Currently this is only applied to the scenario where CheckpointManager is
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# used, which triggers the _write() method.
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self._async_write_done_callback = None
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# The list of all nodes from the original checkpoint items.
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# TODO(chienchunh): Consider changing this to local variable.
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self._original_nodes = None
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# The mapping between the original and the copied resource variables.
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# The copied variables are used for the underlying checkpointing.
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self._object_map = None
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# A list of TPUEmbedding objects included in the checkpoint items.
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self._tpu_embedding_objects = None
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self._default_device = device_util.current() or "CPU:0"
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self._default_device = device_util.canonicalize(self._default_device)
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self._save_file_prefix = None
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self._use_checkpoint_save = False
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self._async_save_thread = None
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self._async_save_thread_shutdown = False
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# Semaphores for writing/reading the cpu-copied variables (self._var_pairs)
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# TODO(chienchunh): Consider Queue/Condition instead of Semaphore.
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self._writer_sem = threading.Semaphore(1)
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self._reader_sem = threading.Semaphore(0)
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# Register to join the async save thread upon exit.
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atexit.register(self._join_async_save_thread)
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global _END_TIME_OF_LAST_ASYNC_WRITE
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with _END_TIME_OF_LAST_ASYNC_WRITE_LOCK:
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if _END_TIME_OF_LAST_ASYNC_WRITE is None:
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_END_TIME_OF_LAST_ASYNC_WRITE = time.time()
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@def_function.function
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def _copy_from_cpu(self):
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"""Copy the checkpointed variables from the host CPU to the accelerator.
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TODO(chienchunh): Get the concrete function before firstly called to avoid
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hangining the accelerators idle during function tracing.
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"""
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for accelerator_var, cpu_var in self._object_map.items():
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if isinstance(accelerator_var, (ShardedVariable, TPUEmbedding)):
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# Skip for SharededVariable and TPUEmbedding as their sub-variables will
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# be copied over separately through other entries in the object map.
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continue
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with ops.device(accelerator_var.device):
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accelerator_var.assign(cpu_var.read_value())
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@def_function.function
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def _copy_to_cpu(self):
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"""Copy the checkpointed variables from the accelerator to the host CPU.
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TODO(chienchunh): Get the concrete function before firstly called to avoid
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hangining the accelerators idle during function tracing.
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"""
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for accelerator_var, cpu_var in self._object_map.items():
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if isinstance(accelerator_var, (ShardedVariable, TPUEmbedding)):
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# Skip for SharededVariable and TPUEmbedding as their sub-variables will
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# be copied over separately through other entries in the object map.
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continue
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with ops.device(cpu_var.device):
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cpu_var.assign(accelerator_var.read_value())
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for tpu_embedding in self._tpu_embedding_objects:
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tpu_embedding._retrieve_variables() # pylint: disable=protected-access
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def _traverse_variables(self, to_traverse, visited):
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"""Create the copied nodes and variables while traversing the nodes.
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This method performs a BFS to traverse the nodes while avoiding duplicated
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visits. Throughout the process, self._mapping, self._original_nodes, and
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self._var_pairs are populated.
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Args:
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to_traverse: A deque that stores the nodes to be traversed.
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visited: A list of nodes that have been visited.
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"""
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# pylint: disable=protected-access
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while to_traverse:
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current_trackable = to_traverse.popleft()
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self._original_nodes.append(current_trackable)
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if isinstance(current_trackable, (Variable, ShardedVariable)):
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self._copy_trackable(current_trackable)
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if isinstance(current_trackable, TPUEmbedding):
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self._handle_tpu_embedding(current_trackable)
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for child in current_trackable._trackable_children().values():
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if child in visited:
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continue
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visited.add(child)
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to_traverse.append(child)
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# pylint: enable=protected-access
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def _ensure_initialized(self):
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"""Initialize the async checkpoint internal state."""
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if self._checkpoint is not None:
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return
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self._original_nodes = []
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self._object_map = object_identity.ObjectIdentityDictionary()
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self._tpu_embedding_objects = []
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# Add the top-level checkpoint items to be traversed,
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to_traverse = collections.deque([])
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visited = object_identity.ObjectIdentitySet()
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for v in self._checkpoint_items.values():
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if isinstance(v, (Variable, ShardedVariable)):
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self._copy_trackable(v)
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elif isinstance(v, TPUEmbedding):
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self._handle_tpu_embedding(v)
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to_traverse.append(v)
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visited.add(v)
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self._traverse_variables(to_traverse, visited)
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# Copy for the slot variables.
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for current_trackable in self._original_nodes:
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if (isinstance(current_trackable, optimizer_v1.Optimizer)
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# Note: dir() is used rather than hasattr() here to avoid triggering
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# custom __getattr__ code, see b/152031870 for context.
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or "get_slot_names" in dir(current_trackable)):
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slot_names = current_trackable.get_slot_names()
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for slot_name in slot_names:
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for original_variable in self._original_nodes:
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if not isinstance(original_variable, Variable):
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continue
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try:
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original_slot_variable = current_trackable.get_slot(
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original_variable, slot_name)
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except (AttributeError, KeyError):
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continue
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if isinstance(original_slot_variable, (Variable, ShardedVariable)):
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self._copy_trackable(original_slot_variable)
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# Initiate the underlying Checkpoint instance with the copied items.
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self._checkpoint = self._checkpointer_impl(**self._checkpoint_items)
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# Pass the object map of the copied variables to the underlying Checkpoint.
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self._checkpoint._saver._object_map = self._object_map # pylint: disable=protected-access
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# Initiate the async thread for checkpoint saving.
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self._async_save_thread = threading.Thread(
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target=self._async_save, daemon=True)
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self._async_save_thread.start()
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def _join_async_save_thread(self):
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"""Join the async save thread.
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The steps for terminating the async save thread:
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1). Wait until the last async save event is done.
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2). Set _async_save_thread_shutdown flag to false to indicate termination.
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3). Trigger the async save thread to check and fail the while-predicate.
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4). Join the async save thread. (The thread may finish before joining.)
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"""
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if self._writer_sem.acquire(timeout=3600): # Step-1.
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self._async_save_thread_shutdown = True # Step-2.
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self._reader_sem.release() # Step-3.
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logging.info("Joining the async save thread.")
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if self._async_save_thread is not None:
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self._async_save_thread.join() # Step-4.
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else:
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logging.error("Timeout waiting for the async save thread; terminating the"
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" thread instead. The last checkpoint may be incomeplete.")
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def _async_save(self):
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"""The thread function for the async checkpoint save."""
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with context.executor_scope(
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executor.new_executor(
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enable_async=False, enable_streaming_enqueue=False)):
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while self._reader_sem.acquire() and not self._async_save_thread_shutdown:
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logging.info("Starting async checkpoint save on the device: %s",
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self._default_device)
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async_save_start_time = time.time()
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# Specify the ops placement on the worker if running with
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# coordinator-worker mode. This is required as launching a new thread
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# would clear the placement policy and make localhost the default
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# placement, while the main thread's default placement would be the
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# master worker's CPU:0.
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with ops.device(self._default_device):
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if self._use_checkpoint_save:
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self._checkpoint.save(self._save_file_prefix,
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self._checkpoint_options)
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else:
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self._checkpoint._write( # pylint: disable=protected-access
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self._save_file_prefix,
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options=self._checkpoint_options,
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write_done_callback=self._async_write_done_callback)
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# Allow the next checkpoint event to overwrite the cpu-copied variables.
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self._writer_sem.release()
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async_save_end_time = time.time()
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metrics.AddAsyncCheckpointWriteDuration(
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api_label=_ASYNC_CHECKPOINT,
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microseconds=_get_duration_microseconds(async_save_start_time,
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async_save_end_time))
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# Measure the elapsed time since the last checkpoint.
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# Due to the nature of async checkpoint, here it actually captures the
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# duration between the start_time of the previous checkpoint and the
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# start time of this checkpoint. As a result, the duration of the final
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# async checkpoint is excluded, which is fine since it does not take
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# much time.
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global _END_TIME_OF_LAST_ASYNC_WRITE
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with _END_TIME_OF_LAST_ASYNC_WRITE_LOCK:
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metrics.AddTrainingTimeSaved(
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api_label=_ASYNC_CHECKPOINT,
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microseconds=_get_duration_microseconds(
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_END_TIME_OF_LAST_ASYNC_WRITE, async_save_start_time))
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_END_TIME_OF_LAST_ASYNC_WRITE = async_save_start_time
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logging.info("Async save thread reached the end of the execution.")
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def _copy_for_variable(self, original_var):
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"""Create a new instance for the input trackable.
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Args:
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original_var: Input Variable object to be copied.
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"""
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op_device = pydev.DeviceSpec.from_string(original_var.device).replace(
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device_type="CPU", device_index=0).to_string()
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with ops.device(op_device):
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new_var = UninitializedVariable(
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trainable=original_var.trainable,
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shape=original_var.shape,
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dtype=original_var.dtype,
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name=original_var._shared_name) # pylint: disable=protected-access
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self._object_map[original_var] = new_var
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def _copy_for_sharded_variable(self, original_var):
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"""Create a new instance for the input ShardedVariable.
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Args:
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original_var: Input ShardedVariable object to be copied.
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"""
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copied_vars = []
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for v in original_var._variables: # pylint: disable=protected-access
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self._copy_for_variable(v)
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copied_vars.append(self._object_map[v])
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self._object_map[original_var] = ShardedVariable(
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copied_vars, name=original_var.name)
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def _copy_trackable(self, original_trackable):
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"""Create a new instance for the input trackable.
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Args:
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original_trackable: The trackable instance to be copied.
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Raises:
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AttributeError: if the input trackable is not Variable or ShardedVariable.
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"""
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if isinstance(original_trackable, ShardedVariable):
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self._copy_for_sharded_variable(original_trackable)
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elif isinstance(original_trackable, Variable):
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self._copy_for_variable(original_trackable)
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else:
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raise AttributeError("Only Variable or ShardedVariable can be copied.")
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def _handle_tpu_embedding(self, tpu_embedding):
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"""Handle TPUEmbedding.
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Args:
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tpu_embedding: TPUEmbedding object to be handled.
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Raises:
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AttributeError: if the input trackable is not TPUEmbedding type.
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"""
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if not isinstance(tpu_embedding, TPUEmbedding):
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raise AttributeError("Expecting TPUEmbedding type; got %s" %
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type(tpu_embedding))
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# Create a dummy TPUEmbedding object and add it to the object_map. This is
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# to prevent the TPUEmbedding's save_callback from being triggered because
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# the embedding values have already being retrieved by AsyncCheckpoint.
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# pylint: disable=protected-access
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new_embedding = TPUEmbedding(
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feature_config=tpu_embedding._feature_config,
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optimizer=tpu_embedding._table_config[0].optimizer,
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pipeline_execution_with_tensor_core=tpu_embedding
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._pipeline_execution_with_tensor_core)
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self._object_map[tpu_embedding] = new_embedding
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# pylint: enable=protected-access
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if tpu_embedding not in self._tpu_embedding_objects:
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self._tpu_embedding_objects.append(tpu_embedding)
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@property
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def save_counter(self):
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"""An integer variable numbering the checkpoint events.
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This is maintained by the underlying tf.train.Checkpoing object employed by
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AsyncCheckpoint class. The number starts at 0 and gets incremented for each
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checkpoint event.
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Returns:
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The save counter variable.
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"""
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self._ensure_initialized()
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return self._checkpoint.save_counter
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def write(self, save_path, options=None):
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"""Save the checkpointed variables.
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Args:
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save_path: The file prefix of the checkpoint file.
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options: Optional CheckpointOption instance.
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Returns:
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The full path of the checkpoint file.
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"""
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self._write(save_path, options)
|
||
|
|
||
|
def _write(self, save_path, options=None, write_done_callback=None):
|
||
|
"""Save the checkpointed variables.
|
||
|
|
||
|
This method has exactly the same logic as save(), except it does not
|
||
|
increment the underlying save_counter, which is done by the caller, e.g.,
|
||
|
CheckpointManager.
|
||
|
|
||
|
Args:
|
||
|
save_path: The file prefix of the checkpoint file.
|
||
|
options: Optional CheckpointOption instance.
|
||
|
write_done_callback: Optional callback function executed after the async
|
||
|
write is done.
|
||
|
|
||
|
Returns:
|
||
|
The full path of the checkpoint file.
|
||
|
"""
|
||
|
self._ensure_initialized()
|
||
|
|
||
|
write_start_time = time.time()
|
||
|
|
||
|
# Copy the variable values to the host CPU.
|
||
|
if self._writer_sem.acquire():
|
||
|
self._copy_to_cpu()
|
||
|
|
||
|
# Trigger the async thread to checkpoint the cpu-copied variables.
|
||
|
# Need to wait until the weight copying finishes before checkpoint save.
|
||
|
context.async_wait()
|
||
|
self._save_file_prefix = save_path
|
||
|
self._use_checkpoint_save = False
|
||
|
|
||
|
# Ensure that we do not request async checkpointing to the underlying
|
||
|
# checkpointer as this could lead to an infinite loop.
|
||
|
self._checkpoint_options = copy.copy(options) if options else None
|
||
|
if self._checkpoint_options:
|
||
|
self._checkpoint_options.experimental_enable_async_checkpoint = False
|
||
|
|
||
|
self._async_write_done_callback = write_done_callback
|
||
|
self._reader_sem.release()
|
||
|
|
||
|
write_end_time = time.time()
|
||
|
metrics.AddCheckpointWriteDuration(
|
||
|
api_label=_ASYNC_CHECKPOINT,
|
||
|
microseconds=_get_duration_microseconds(write_start_time,
|
||
|
write_end_time))
|
||
|
|
||
|
return save_path
|
||
|
|
||
|
def save(self, save_path, options=None):
|
||
|
"""Save the checkpointed variables.
|
||
|
|
||
|
Args:
|
||
|
save_path: The file prefix of the checkpoint file.
|
||
|
options: Optional CheckpointOption instance.
|
||
|
|
||
|
Returns:
|
||
|
The full path of the checkpoint file.
|
||
|
"""
|
||
|
# If this is the first time that AsyncCheckpoint.save() is called,
|
||
|
# initialize the cpu-copied variables and create the pair-wise mapping
|
||
|
# between the original model variables and the cpu-copied variables.
|
||
|
#
|
||
|
# This is not performed in the initializer because some variables, e.g.,
|
||
|
# slot variables of the optimizer, were not created until actually running
|
||
|
# the train function, so we could only get the complete list of the
|
||
|
# variables after some train steps were run.
|
||
|
self._ensure_initialized()
|
||
|
|
||
|
save_start_time = time.time()
|
||
|
|
||
|
# Copy the variable values to the host CPU.
|
||
|
if self._writer_sem.acquire():
|
||
|
self._copy_to_cpu()
|
||
|
|
||
|
# Retrieve the save counter from the underlying checkpoint object to
|
||
|
# re-construct the full path of the checkpoint file.
|
||
|
# This step has to happen before triggerting the underlying checkpoint;
|
||
|
# otherwise, the save_counter value may or may not have been updated.
|
||
|
save_counter = self._checkpoint.save_counter.numpy() + 1
|
||
|
full_path = "{}-{}".format(save_path, save_counter)
|
||
|
|
||
|
# Trigger the async thread to checkpoint the cpu-copied variables.
|
||
|
# Need to wait until the weight copying finishes before checkpoint save.
|
||
|
context.async_wait()
|
||
|
self._save_file_prefix = save_path
|
||
|
self._use_checkpoint_save = True
|
||
|
|
||
|
# Ensure that we do not request async checkpointing to the underlying
|
||
|
# checkpointer as this could lead to an infinite loop.
|
||
|
self._checkpoint_options = copy.copy(options) if options else None
|
||
|
if self._checkpoint_options:
|
||
|
self._checkpoint_options.experimental_enable_async_checkpoint = False
|
||
|
|
||
|
self._reader_sem.release()
|
||
|
|
||
|
save_end_time = time.time()
|
||
|
metrics.AddCheckpointWriteDuration(
|
||
|
api_label=_ASYNC_CHECKPOINT,
|
||
|
microseconds=_get_duration_microseconds(save_start_time, save_end_time))
|
||
|
|
||
|
return full_path
|
||
|
|
||
|
def read(self, save_path, options=None):
|
||
|
"""Restore the checkpointed variables.
|
||
|
|
||
|
This method has exactly the same logic as restore(). This method is
|
||
|
implemented only to fulfill the duty of subclassing tf.train.Checkpoint.
|
||
|
|
||
|
Args:
|
||
|
save_path: The full name of the checkpoint file to be restored.
|
||
|
options: CheckpointOption instance.
|
||
|
|
||
|
Returns:
|
||
|
A load status object, which can be used to make assertions about the
|
||
|
status of a checkpoint restoration. See tf.train.Checkpoint.restore()
|
||
|
for more details.
|
||
|
"""
|
||
|
return self.restore(save_path, options)
|
||
|
|
||
|
def restore(self, save_path, options=None):
|
||
|
"""Restore the checkpointed variables.
|
||
|
|
||
|
Args:
|
||
|
save_path: The full name of the checkpoint file to be restored.
|
||
|
options: CheckpointOption instance.
|
||
|
|
||
|
Returns:
|
||
|
A load status object, which can be used to make assertions about the
|
||
|
status of a checkpoint restoration. See tf.train.Checkpoint.restore()
|
||
|
for more details.
|
||
|
"""
|
||
|
# Ensure that we do not request async checkpointing to the underlying
|
||
|
# checkpointer as this could lead to an infinite loop.
|
||
|
self._checkpoint_options = (
|
||
|
copy.copy(options) if options else self._checkpoint_options)
|
||
|
if self._checkpoint_options:
|
||
|
self._checkpoint_options.experimental_enable_async_checkpoint = False
|
||
|
|
||
|
# Wait for any ongoing checkpoint event to finish.
|
||
|
with self._writer_sem:
|
||
|
# If _checkpoint has not been initialized yet, it means the restore() is
|
||
|
# called right after the coordinator is restarted. We directly restore
|
||
|
# the checkpointed items through tf.train.Checkpoint.restore().
|
||
|
if self._checkpoint is None:
|
||
|
tmp_checkpoint = self._checkpointer_impl(**self._checkpoint_items)
|
||
|
return tmp_checkpoint.restore(save_path, self._checkpoint_options)
|
||
|
|
||
|
# Restore the values of the cpu-copied variables.
|
||
|
status = self._checkpoint.restore(save_path, self._checkpoint_options)
|
||
|
|
||
|
# Restore the values of the original model.
|
||
|
self._copy_from_cpu()
|
||
|
return status
|
||
|
|
||
|
def sync(self):
|
||
|
"""Sync on any ongoing save or restore events."""
|
||
|
with self._writer_sem:
|
||
|
logging.info("Sync on ongoing save/restore.")
|