You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
135 lines
5.3 KiB
135 lines
5.3 KiB
import os
|
|
import threading
|
|
from queue import Empty as EmptyQueue, Queue
|
|
|
|
from torch._lazy.device_context import get_device_context
|
|
|
|
|
|
class ClosureHandler:
|
|
def __init__(self):
|
|
pass
|
|
|
|
def run(self, closure):
|
|
"""Run closure function
|
|
|
|
Args:
|
|
closure: callable function to run
|
|
"""
|
|
closure()
|
|
|
|
def __call__(self, closures):
|
|
for closure in closures:
|
|
self.run(closure)
|
|
|
|
|
|
class AsyncClosureHandler(ClosureHandler):
|
|
"""Handler for Asynchronous Step Closures
|
|
Args:
|
|
max_queue_size: The maximum length of the closure queue after which
|
|
the training loop will block until closures are evaluated.
|
|
By default, a reasonable limit of a maximum of 100 on the queue.
|
|
This value can be set using the `XLA_MAX_ASYNC_QUEUE` environment
|
|
variable.
|
|
"""
|
|
|
|
def __init__(self, max_queue_size=100):
|
|
super().__init__()
|
|
self._closure_queue: Queue = Queue(
|
|
int(os.environ.get("LTC_MAX_ASYNC_QUEUE", max_queue_size))
|
|
)
|
|
self._closure_exception: Queue = Queue()
|
|
self._closure_lock = threading.Lock()
|
|
self._closure_event_loop_finished = threading.Event()
|
|
self._closure_event_loop = None
|
|
|
|
def start_event_loop(self):
|
|
"""Start closure event loop if not started"""
|
|
if self._closure_event_loop is None:
|
|
|
|
def event_loop():
|
|
# Run loop until closure event is set and closure queue is empty
|
|
while True:
|
|
try:
|
|
closure = self._closure_queue.get(block=True, timeout=3)
|
|
closure()
|
|
self._closure_queue.task_done()
|
|
except EmptyQueue:
|
|
with self._closure_lock:
|
|
if self._closure_queue.empty():
|
|
self._closure_event_loop_finished.set()
|
|
return
|
|
except Exception as e:
|
|
self._closure_exception.put(e)
|
|
return
|
|
|
|
self._closure_event_loop = threading.Thread(target=event_loop)
|
|
self._closure_event_loop.start()
|
|
|
|
def run(self, closure):
|
|
with self._closure_lock:
|
|
self._closure_queue.put(closure, block=True)
|
|
if (
|
|
self._closure_event_loop is None
|
|
or not self._closure_event_loop.is_alive()
|
|
):
|
|
try:
|
|
e = self._closure_exception.get(block=False)
|
|
raise RuntimeError(
|
|
"Cannot run asynchronous closure due to previously raised exception"
|
|
) from e
|
|
except EmptyQueue:
|
|
self._closure_event_loop = None
|
|
self.start_event_loop()
|
|
|
|
|
|
def add_step_closure(closure, args=(), run_async=False):
|
|
"""Adds a closure to the list of the ones to be run at the end of the step.
|
|
Many times during model training there is the need to print/report (print to
|
|
console, post to tensorboard, etc...) information which require the content of
|
|
intermediary tensors to be inspected.
|
|
Inspecting different tensors content in different points of the model code
|
|
requires many executions and typically causes performance issues.
|
|
Adding a step closure will ensure that it will be run after the barrier, when
|
|
all the live tensors will be already materialized to device data.
|
|
Live tensors which will include the ones captured by the closure arguments.
|
|
So using `add_step_closure()` will ensure a single execution will be
|
|
performed, even when multiple closures are queued, requiring multiple tensors
|
|
to be inspected.
|
|
Step closures will be run sequentially in the order they have been queued.
|
|
Note that even though using this API the execution will be optimized, it is
|
|
advised to throttle the printing/reporting events once every N steps.
|
|
Args:
|
|
closure (callable): The function to be called.
|
|
args (tuple): The arguments to be passed to the closure.
|
|
run_async: If True, run the closure asynchronously.
|
|
"""
|
|
devctx = get_device_context()
|
|
closures_type = "async_step_closures" if run_async else "step_closures"
|
|
step_closures = getattr(devctx, closures_type, None)
|
|
if step_closures is None:
|
|
step_closures = []
|
|
setattr(devctx, closures_type, step_closures)
|
|
step_closures.append(lambda a=args: closure(*a))
|
|
|
|
|
|
def run_step_closures():
|
|
devctx = get_device_context()
|
|
async_step_closures = getattr(devctx, "async_step_closures", None)
|
|
if async_step_closures is not None:
|
|
devctx.async_step_closures = []
|
|
async_closure_handler = getattr(devctx, "async_closure_handler", None)
|
|
if async_closure_handler is None:
|
|
async_closure_handler = AsyncClosureHandler()
|
|
devctx.async_closure_handler = async_closure_handler
|
|
async_closure_handler(async_step_closures)
|
|
|
|
step_closures = getattr(devctx, "step_closures", None)
|
|
if step_closures is not None:
|
|
devctx.step_closures = []
|
|
closure_handler = getattr(devctx, "closure_handler", None)
|
|
if closure_handler is None:
|
|
closure_handler = ClosureHandler()
|
|
devctx.closure_handler = closure_handler
|
|
closure_handler(step_closures)
|
|
return devctx
|