# Copyright 2021 The JAX Authors. # # 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 # # https://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. """Array serialization and deserialization.""" from __future__ import annotations import abc import asyncio from collections.abc import Callable, Sequence import functools import itertools import logging import re import threading import time from typing import Any import jax from jax._src import array from jax._src import distributed from jax._src import sharding from jax._src import typing from jax._src import util from jax._src.layout import Format from jax._src.lib import _jax from jax.experimental.array_serialization import tensorstore_impl as ts_impl # ruff: noqa: F401 # pylint: disable=unused-import # import tensorstore-backed methods for backward compatibility. from jax.experimental.array_serialization.tensorstore_impl import ( _run_deserialization as run_deserialization, _run_serialization as run_serialization, async_serialize, async_deserialize, _TS_CONTEXT as TS_CONTEXT, _DEFAULT_BASE_DRIVER as _DEFAULT_DRIVER, _LimitInFlightBytes) import tensorstore as ts # for compatibility with older zarr format _get_metadata = functools.partial(ts_impl._get_tensorstore_metadata, driver='zarr') get_tensorstore_spec = functools.partial(ts_impl.get_tensorstore_spec, driver='zarr', ocdbt=False) # pylint: enable=unused-import _CHECKPOINT_SUCCESS = 'checkpoint_write_success' _module_unique_count = itertools.count() _DISTRIBUTED_SYSTEM_MSG = ( 'Please initialize the distributed system via ' '`jax.distributed.initialize()` at the start of your program.') _REMOTE_URL_PREFIXES = ['gs://', 's3://'] _REMOTE_DRIVER_VALIDATIONS = [ {'driver': 'gcs', 'path_regex': None}, {'driver': 's3', 'path_regex': None}, ] class BarrierTimeoutError(Exception): pass _BARRIER_TIMED_OUT_MSG = ( "Suggestions for possible fixes:\n" "* Check the logs to see if one or more processes failed.\n" "* Make sure the training and checkpointing endpoints are close geographically.\n" "* Try increasing the timeout you pass to GlobalAsyncCheckpointManager.") logger = logging.getLogger(__name__) def is_remote_storage(tspec: dict[str, Any] | str) -> bool: """Detect if user is using cloud storages. This can detect common defines and unable to detect some corner cases such as using gcsfuse. """ if isinstance(tspec, str): # KvStoreUrl if re.match(rf'^({"|".join(_REMOTE_URL_PREFIXES)})', tspec): return True else: return False for key in ('base', 'kvstore'): if key in tspec: return is_remote_storage(tspec[key]) if 'driver' in tspec: for rule in _REMOTE_DRIVER_VALIDATIONS: if tspec['driver'] == rule['driver']: if rule['path_regex'] is None: return True # check if path matches the regex. if re.match(rule['path_regex'], tspec['path']): return True return False def _get_key(key: int): return f'tensorstore_checkpoint_{key}' class GlobalAsyncCheckpointManagerBase(util.StrictABC): """Interface for checkpointing GDAs asynchronously. This class manages the state of an ongoing asynchronous checkpoint. For example, say a checkpoint happens on every step. If you checkpoint on step 1 and after some computation the model is on checkpoint 2. But step 1's checkpoint hasn't finished committing to the storage layer yet. So until that is finished, checkpoint for step 2 will need to be blocked. Maintaining a class allows to maintain that state. Examples: Below is a simplified training loop: ``` # Call this at the start of your program. jax.distributed.initialize() manager = GlobalAsyncCheckpointManager() # Restore checkpoint if available or initialize the train_state from # init_fn(). train_state = manager.deserialize(...) while ...: if step % num_steps_between_checkpoints == 0: manager.serialize(train_state, temp_checkpoint_dir=..., final_checkpoint_dir=...) train_state = train_step(train_state, input) # This is a non-blocking call. manager.check_for_errors() manager.serialize(train_state, temp_checkpoint_dir=..., final_checkpoint_dir=...) # Wait before the end of the program for the checkpoint to finish. This is a # blocking call. manager.wait_until_finished() ``` """ @abc.abstractmethod def check_for_errors(self): """Checks if any errors have been raised in the child thread. This is a non-blocking call that can be called in the main thread. """ @abc.abstractmethod def wait_until_finished(self): """Blocks until serialization has finished.""" @abc.abstractmethod def serialize(self, arrays, tensorstore_specs, *, on_commit_callback: Callable[[], None]): """Serializes GDAs to TensorStore.""" @abc.abstractmethod def deserialize(self, shardings: Sequence[sharding.Sharding], tensorstore_specs: Sequence[dict[str, Any]], global_shapes: Sequence[array.Shape] | None = None, dtypes: Sequence[typing.DTypeLike] | None = None): """Deserializes GDAs from TensorStore.""" class AsyncManager: def __init__(self, timeout_secs=300): self._timeout_secs = timeout_secs self._timeout_in_ms = self._timeout_secs * 1000 self._commit_futures = None self._thread = None self._exception = None if jax.process_count() > 1 and distributed.global_state.client is None: raise ValueError(_DISTRIBUTED_SYSTEM_MSG) self._client = distributed.global_state.client self._count = None def __del__(self): if self._thread is not None and self._thread.is_alive(): logger.warning('Please add `.wait_until_finished()` in the main thread ' 'before your program finishes because there is a ' 'possibility of losing errors raised if the ' 'this class is deleted before writing is completed.') def _thread_func(self): try: current_process = jax.process_index() process_count = jax.process_count() logger.info('Starting commit to storage layer by process: %s', current_process) thread_start_time = time.time() for future in self._commit_futures: future.result() logger.info('Finished committing to storage layer by process: %s', current_process) key_for_barrier = None if process_count > 1: assert self._client is not None # All processes will wait at the barrier. When all processes are at the # barrier, the barrier will be satisfied. If not, then it will timeout. key_for_barrier = _get_key(self._count) logger.info('Key used for barrier is %s for process %s', key_for_barrier, current_process) self._client.wait_at_barrier(key_for_barrier, self._timeout_in_ms) logger.info('Finished waiting at barrier for process %s', current_process) if current_process == 0: if self._on_commit_callback is not None: self._on_commit_callback() logger.info('on_commit_callback successfully ran!') if process_count > 1: assert self._client is not None self._client.key_value_set(key_for_barrier, _CHECKPOINT_SUCCESS) logger.info('Process 0 successfully set key %s in the kv store', key_for_barrier) jax.monitoring.record_event_duration_secs( '/jax/checkpoint/write/async/thread_duration_sec', time.time() - thread_start_time) except Exception as e: # pylint: disable=broad-except self._exception = e def _start_async_commit(self, on_commit_callback): self._count = next(_module_unique_count) self._on_commit_callback = on_commit_callback self._thread = threading.Thread(target=self._thread_func) self._thread.start() def check_for_errors(self): if self._exception is not None: # Clears self._exception so it is only raised once. exception = self._exception self._exception = None if (isinstance(exception, _jax.JaxRuntimeError) and 'DEADLINE_EXCEEDED: Barrier timed out' in str(exception)): raise BarrierTimeoutError( '\n'.join([str(exception), _BARRIER_TIMED_OUT_MSG])) raise exception # pylint: disable=raising-bad-type def wait_until_finished(self): if self._thread is not None: self._thread.join() self._thread = None logger.info('Thread joined successfully') self.check_for_errors() logger.info('Error check finished successfully') if jax.process_count() > 1 and self._count is not None: assert self._client is not None # Block until process 0 writes success value to the key value store. # If it fails to write it, then `blocking_key_value_get` will time out. get_key = _get_key(self._count) self._client.blocking_key_value_get(get_key, self._timeout_in_ms) logger.info('blocking_key_value_get on key %s was successfully ' 'completed.', get_key) def _add_futures(self, futures: Sequence[ts.Future]): self._commit_futures = futures class GlobalAsyncCheckpointManager(AsyncManager, GlobalAsyncCheckpointManagerBase): """Responsible for serializing GDAs via TensorStore.""" def serialize( self, arrays, tensorstore_specs, *, on_commit_callback: Callable[[], None] | None = None, transaction: ts_impl.Transaction | None = None, ): """Serializes Arrays or Arrays via TensorStore asynchronously. TensorStore writes to a storage layer in 2 steps: * Reading/copying from the source after which the source can be modified. * Returns a copy future. * Writing/committing to the storage layer. * Returns a commit future. In asynchronous mode, the serialization waits for the commit future to finish in a separate thread allowing other computation to proceed. Args: arrays: Arrays or Arrays that should be serialized. tensorstore_specs: TensorStore specs that are used to serialize GDAs or Arrays. on_commit_callback: This callback will be executed after all processes have finished writing their checkpoints to disk. Filesystems where atomic rename operations are supported, you can rename from the temporary directory to the final directory. On GCS, you write to the final directory directly and in `on_commit_callback` you write a success file indicating that the serialization was successful because GCS does not support atomic rename operations. transaction: Optional TensorStore transaction to use. """ logger.info('Waiting for previous serialization to finish.') self.wait_until_finished() commit_futures: list[ts_impl.Future] = [] async def _run_serializer(): future_writer = jax.tree_util.tree_map( lambda arr_inp, tensorstore_spec: ts_impl.async_serialize( arr_inp, tensorstore_spec, commit_future=commit_futures, transaction=transaction, ), arrays, tensorstore_specs, ) return await asyncio.gather(*future_writer) asyncio.run(_run_serializer()) self._add_futures(commit_futures) # Used in wait_until_finished to check on process != 0, if the checkpoint # has finished writing. self._start_async_commit(on_commit_callback) def serialize_with_paths( self, arrays: Sequence[jax.Array], paths: Sequence[str], *, on_commit_callback: Callable[[], None] | None = None, transaction: ts_impl.Transaction | None = None, ): tspecs = jax.tree.map(get_tensorstore_spec, paths) return self.serialize( arrays, tspecs, on_commit_callback=on_commit_callback, transaction=transaction, ) def deserialize(self, shardings: Sequence[sharding.Sharding | Format], tensorstore_specs: Sequence[dict[str, Any]], global_shapes: Sequence[array.Shape] | None = None, dtypes: Sequence[typing.DTypeLike] | None = None, concurrent_gb: int = 32): self.wait_until_finished() return ts_impl._run_deserialization( shardings, tensorstore_specs, global_shapes, dtypes, concurrent_gb) def deserialize_with_paths( self, shardings: Sequence[sharding.Sharding], paths: Sequence[str], global_shapes: Sequence[array.Shape] | None = None, dtypes: Sequence[typing.DTypeLike] | None = None, concurrent_gb: int = 32): tspecs = jax.tree.map(get_tensorstore_spec, paths) return self.deserialize(shardings, tspecs, global_shapes, dtypes, concurrent_gb)