import asyncio
import io
import logging
import time
from collections import deque
from dataclasses import dataclass
from functools import wraps
from inspect import isasyncgenfunction, iscoroutinefunction
from typing import (
Any,
AsyncGenerator,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
TypeVar,
overload,
)
from ray import serve
from ray._private.signature import extract_signature, flatten_args, recover_args
from ray._private.utils import get_or_create_event_loop
from ray.serve._private.constants import SERVE_LOGGER_NAME
from ray.serve._private.utils import extract_self_if_method_call
from ray.serve.exceptions import RayServeException
from ray.util.annotations import PublicAPI
logger = logging.getLogger(SERVE_LOGGER_NAME)
# The user can return these values in their streaming batch handler function to
# indicate that a request is finished, so Serve can terminate the request.
USER_CODE_STREAMING_SENTINELS = [StopIteration, StopAsyncIteration]
@dataclass
class _SingleRequest:
self_arg: Any
flattened_args: List[Any]
future: asyncio.Future
@dataclass
class _GeneratorResult:
result: Any
next_future: asyncio.Future
def _batch_args_kwargs(
list_of_flattened_args: List[List[Any]],
) -> Tuple[Tuple[Any], Dict[Any, Any]]:
"""Batch a list of flatten args and returns regular args and kwargs"""
# Ray's flatten arg format is a list with alternating key and values
# e.g. args=(1, 2), kwargs={"key": "val"} got turned into
# [None, 1, None, 2, "key", "val"]
arg_lengths = {len(args) for args in list_of_flattened_args}
assert (
len(arg_lengths) == 1
), "All batch requests should have the same number of parameters."
arg_length = arg_lengths.pop()
batched_flattened_args = []
for idx in range(arg_length):
if idx % 2 == 0:
batched_flattened_args.append(list_of_flattened_args[0][idx])
else:
batched_flattened_args.append(
[item[idx] for item in list_of_flattened_args]
)
return recover_args(batched_flattened_args)
class _BatchQueue:
def __init__(
self,
max_batch_size: int,
batch_wait_timeout_s: float,
handle_batch_func: Optional[Callable] = None,
) -> None:
"""Async queue that accepts individual items and returns batches.
Respects max_batch_size and timeout_s; a batch will be returned when
max_batch_size elements are available or the timeout has passed since
the previous get.
If handle_batch_func is passed in, a background coroutine will run to
poll from the queue and call handle_batch_func on the results.
Cannot be pickled.
Arguments:
max_batch_size: max number of elements to return in a batch.
timeout_s: time to wait before returning an incomplete
batch.
handle_batch_func(Optional[Callable]): callback to run in the
background to handle batches if provided.
"""
self.queue: asyncio.Queue[_SingleRequest] = asyncio.Queue()
self.max_batch_size = max_batch_size
self.batch_wait_timeout_s = batch_wait_timeout_s
self.requests_available_event = asyncio.Event()
# Used for observability.
self.curr_iteration_start_time = time.time()
self._handle_batch_task = None
self._loop = get_or_create_event_loop()
if handle_batch_func is not None:
self._handle_batch_task = self._loop.create_task(
self._process_batches(handle_batch_func)
)
self._warn_if_max_batch_size_exceeds_max_ongoing_requests()
def _warn_if_max_batch_size_exceeds_max_ongoing_requests(self):
"""Helper to check whether the max_batch_size is bounded.
Log a warning to configure `max_ongoing_requests` if it's bounded.
"""
max_ongoing_requests = (
serve.get_replica_context()._deployment_config.max_ongoing_requests
)
if max_ongoing_requests < self.max_batch_size:
logger.warning(
f"`max_batch_size` ({self.max_batch_size}) is larger than "
f"`max_ongoing_requests` ({max_ongoing_requests}). This means "
"the replica will never receive a full batch. Please update "
"`max_ongoing_requests` to be >= `max_batch_size`."
)
def set_max_batch_size(self, new_max_batch_size: int) -> None:
"""Updates queue's max_batch_size."""
self.max_batch_size = new_max_batch_size
self._warn_if_max_batch_size_exceeds_max_ongoing_requests()
def put(self, request: Tuple[_SingleRequest, asyncio.Future]) -> None:
self.queue.put_nowait(request)
self.requests_available_event.set()
async def wait_for_batch(self) -> List[Any]:
"""Wait for batch respecting self.max_batch_size and self.timeout_s.
Returns a batch of up to self.max_batch_size items. Waits for up to
to self.timeout_s after receiving the first request that will be in
the next batch. After the timeout, returns as many items as are ready.
Always returns a batch with at least one item - will block
indefinitely until an item comes in.
"""
batch = []
batch.append(await self.queue.get())
# Cache current max_batch_size and batch_wait_timeout_s for this batch.
max_batch_size = self.max_batch_size
batch_wait_timeout_s = self.batch_wait_timeout_s
# Wait self.timeout_s seconds for new queue arrivals.
batch_start_time = time.time()
while True:
remaining_batch_time_s = max(
batch_wait_timeout_s - (time.time() - batch_start_time), 0
)
try:
# Wait for new arrivals.
await asyncio.wait_for(
self.requests_available_event.wait(), remaining_batch_time_s
)
except asyncio.TimeoutError:
pass
# Add all new arrivals to the batch.
while len(batch) < max_batch_size and not self.queue.empty():
batch.append(self.queue.get_nowait())
# Only clear the put event if the queue is empty. If it's not empty
# we can start constructing a new batch immediately in the next loop.
# The code that puts items into the queue runs on the same event loop
# as this code, so there's no race condition between the time we
# get objects in the queue (and clear the event) and when objects
# get added to the queue.
if self.queue.empty():
self.requests_available_event.clear()
if (
time.time() - batch_start_time >= batch_wait_timeout_s
or len(batch) >= max_batch_size
):
break
return batch
def _validate_results(
self, results: Iterable[Any], input_batch_length: int
) -> None:
if len(results) != input_batch_length:
raise RayServeException(
"Batched function doesn't preserve batch size. "
f"The input list has length {input_batch_length} but the "
f"returned list has length {len(results)}."
)
async def _consume_func_generator(
self,
func_generator: AsyncGenerator,
initial_futures: List[asyncio.Future],
input_batch_length: int,
) -> None:
"""Consumes batch function generator.
This function only runs if the function decorated with @serve.batch
is a generator.
"""
FINISHED_TOKEN = None
try:
futures = deque(initial_futures)
assert len(futures) == input_batch_length
async for results in func_generator:
self._validate_results(results, input_batch_length)
for idx in range(input_batch_length):
result, future = results[idx], futures[0]
if future is FINISHED_TOKEN:
# This caller has already terminated.
futures.append(FINISHED_TOKEN)
elif result in USER_CODE_STREAMING_SENTINELS:
# User's code returned sentinel. No values left
# for caller. Terminate iteration for caller.
_set_exception_if_not_done(future, StopAsyncIteration)
futures.append(FINISHED_TOKEN)
else:
next_future = get_or_create_event_loop().create_future()
_set_result_if_not_done(
future, _GeneratorResult(result, next_future)
)
futures.append(next_future)
# Remove processed future. We remove the future at the very
# end of the loop to ensure that if an exception occurs,
# all pending futures will get set in the `except` block.
futures.popleft()
for future in futures:
if future is not FINISHED_TOKEN:
_set_exception_if_not_done(future, StopAsyncIteration)
except Exception as e:
for future in futures:
if future is not FINISHED_TOKEN:
_set_exception_if_not_done(future, e)
async def _assign_func_results(
self,
func_future: asyncio.Future,
futures: List[asyncio.Future],
input_batch_length: int,
):
"""Assigns func's results to the list of futures."""
try:
results = await func_future
self._validate_results(results, input_batch_length)
for result, future in zip(results, futures):
_set_result_if_not_done(future, result)
except Exception as e:
for future in futures:
_set_exception_if_not_done(future, e)
async def _process_batches(self, func: Callable) -> None:
"""Loops infinitely and processes queued request batches."""
while not self._loop.is_closed():
try:
self.curr_iteration_start_time = time.time()
await self._process_batch(func)
except Exception:
logger.exception(
"_process_batches asyncio task ran into an unexpected exception."
)
async def _process_batch(self, func: Callable) -> None:
"""Processes queued request batch."""
batch: List[_SingleRequest] = await self.wait_for_batch()
assert len(batch) > 0
futures = [item.future for item in batch]
# Most of the logic in the function should be wrapped in this try-
# except block, so the futures' exceptions can be set if an exception
# occurs. Otherwise, the futures' requests may hang indefinitely.
try:
self_arg = batch[0].self_arg
args, kwargs = _batch_args_kwargs([item.flattened_args for item in batch])
# Method call.
if self_arg is not None:
func_future_or_generator = func(self_arg, *args, **kwargs)
# Normal function call.
else:
func_future_or_generator = func(*args, **kwargs)
if isasyncgenfunction(func):
func_generator = func_future_or_generator
await self._consume_func_generator(func_generator, futures, len(batch))
else:
func_future = func_future_or_generator
await self._assign_func_results(func_future, futures, len(batch))
except Exception as e:
logger.exception("_process_batch ran into an unexpected exception.")
for future in futures:
_set_exception_if_not_done(future, e)
def __del__(self):
if (
self._handle_batch_task is None
or not get_or_create_event_loop().is_running()
):
return
# TODO(edoakes): although we try to gracefully shutdown here, it still
# causes some errors when the process exits due to the asyncio loop
# already being destroyed.
self._handle_batch_task.cancel()
class _LazyBatchQueueWrapper:
"""Stores a _BatchQueue and updates its settings.
_BatchQueue cannot be pickled, you must construct it lazily
at runtime inside a replica. This class initializes a queue only upon
first access.
"""
def __init__(
self,
max_batch_size: int = 10,
batch_wait_timeout_s: float = 0.0,
handle_batch_func: Optional[Callable] = None,
):
self._queue: Optional[_BatchQueue] = None
self.max_batch_size = max_batch_size
self.batch_wait_timeout_s = batch_wait_timeout_s
self.handle_batch_func = handle_batch_func
@property
def queue(self) -> _BatchQueue:
"""Returns _BatchQueue.
Initializes queue when called for the first time.
"""
if self._queue is None:
self._queue = _BatchQueue(
self.max_batch_size,
self.batch_wait_timeout_s,
self.handle_batch_func,
)
return self._queue
def set_max_batch_size(self, new_max_batch_size: int) -> None:
"""Updates queue's max_batch_size."""
self.max_batch_size = new_max_batch_size
if self._queue is not None:
self._queue.set_max_batch_size(new_max_batch_size)
def set_batch_wait_timeout_s(self, new_batch_wait_timeout_s: float) -> None:
self.batch_wait_timeout_s = new_batch_wait_timeout_s
if self._queue is not None:
self._queue.batch_wait_timeout_s = new_batch_wait_timeout_s
def get_max_batch_size(self) -> int:
return self.max_batch_size
def get_batch_wait_timeout_s(self) -> float:
return self.batch_wait_timeout_s
def _get_curr_iteration_start_time(self) -> Optional[float]:
"""Gets current iteration's start time on default _BatchQueue implementation.
Returns None if the batch handler doesn't use a default _BatchQueue.
"""
if hasattr(self.queue, "curr_iteration_start_time"):
return self.queue.curr_iteration_start_time
else:
return None
async def _is_batching_task_alive(self) -> bool:
"""Gets whether default _BatchQueue's background task is alive.
Returns False if the batch handler doesn't use a default _BatchQueue.
"""
if hasattr(self.queue, "_handle_batch_task"):
return not self.queue._handle_batch_task.done()
else:
return False
async def _get_handling_task_stack(self) -> Optional[str]:
"""Gets the stack for the default _BatchQueue's background task.
Returns empty string if the batch handler doesn't use a default _BatchQueue.
"""
if hasattr(self.queue, "_handle_batch_task"):
str_buffer = io.StringIO()
self.queue._handle_batch_task.print_stack(file=str_buffer)
return str_buffer.getvalue()
else:
return None
def _validate_max_batch_size(max_batch_size):
if not isinstance(max_batch_size, int):
if isinstance(max_batch_size, float) and max_batch_size.is_integer():
max_batch_size = int(max_batch_size)
else:
raise TypeError(
f"max_batch_size must be integer >= 1, got {max_batch_size}"
)
if max_batch_size < 1:
raise ValueError(
f"max_batch_size must be an integer >= 1, got {max_batch_size}"
)
def _validate_batch_wait_timeout_s(batch_wait_timeout_s):
if not isinstance(batch_wait_timeout_s, (float, int)):
raise TypeError(
"batch_wait_timeout_s must be a float >= 0, " f"got {batch_wait_timeout_s}"
)
if batch_wait_timeout_s < 0:
raise ValueError(
"batch_wait_timeout_s must be a float >= 0, " f"got {batch_wait_timeout_s}"
)
T = TypeVar("T")
R = TypeVar("R")
F = TypeVar("F", bound=Callable[[List[T]], List[R]])
G = TypeVar("G", bound=Callable[[T], R])
# Normal decorator use case (called with no arguments).
@overload
def batch(func: F) -> G:
pass
# "Decorator factory" use case (called with arguments).
@overload
def batch(
max_batch_size: int = 10,
batch_wait_timeout_s: float = 0.0,
) -> Callable[[F], G]:
pass
[docs]@PublicAPI(stability="stable")
def batch(
_func: Optional[Callable] = None,
max_batch_size: int = 10,
batch_wait_timeout_s: float = 0.0,
):
"""Converts a function to asynchronously handle batches.
The function can be a standalone function or a class method. In both
cases, the function must be `async def` and take a list of objects as
its sole argument and return a list of the same length as a result.
When invoked, the caller passes a single object. These will be batched
and executed asynchronously once there is a batch of `max_batch_size`
or `batch_wait_timeout_s` has elapsed, whichever occurs first.
`max_batch_size` and `batch_wait_timeout_s` can be updated using setter
methods from the batch_handler (`set_max_batch_size` and
`set_batch_wait_timeout_s`).
Example:
.. code-block:: python
from ray import serve
from starlette.requests import Request
@serve.deployment
class BatchedDeployment:
@serve.batch(max_batch_size=10, batch_wait_timeout_s=0.1)
async def batch_handler(self, requests: List[Request]) -> List[str]:
response_batch = []
for r in requests:
name = (await requests.json())["name"]
response_batch.append(f"Hello {name}!")
return response_batch
def update_batch_params(self, max_batch_size, batch_wait_timeout_s):
self.batch_handler.set_max_batch_size(max_batch_size)
self.batch_handler.set_batch_wait_timeout_s(batch_wait_timeout_s)
async def __call__(self, request: Request):
return await self.batch_handler(request)
app = BatchedDeployment.bind()
Arguments:
max_batch_size: the maximum batch size that will be executed in
one call to the underlying function.
batch_wait_timeout_s: the maximum duration to wait for
`max_batch_size` elements before running the current batch.
"""
# `_func` will be None in the case when the decorator is parametrized.
# See the comment at the end of this function for a detailed explanation.
if _func is not None:
if not callable(_func):
raise TypeError(
"@serve.batch can only be used to decorate functions or methods."
)
if not iscoroutinefunction(_func):
raise TypeError("Functions decorated with @serve.batch must be 'async def'")
_validate_max_batch_size(max_batch_size)
_validate_batch_wait_timeout_s(batch_wait_timeout_s)
def _batch_decorator(_func):
lazy_batch_queue_wrapper = _LazyBatchQueueWrapper(
max_batch_size,
batch_wait_timeout_s,
_func,
)
async def batch_handler_generator(
first_future: asyncio.Future,
) -> AsyncGenerator:
"""Generator that handles generator batch functions."""
future = first_future
while True:
try:
async_response: _GeneratorResult = await future
future = async_response.next_future
yield async_response.result
except StopAsyncIteration:
break
def enqueue_request(args, kwargs) -> asyncio.Future:
flattened_args: List = flatten_args(extract_signature(_func), args, kwargs)
# If the function is a method, remove self as an argument.
self = extract_self_if_method_call(args, _func)
if self is not None:
flattened_args = flattened_args[2:]
batch_queue = lazy_batch_queue_wrapper.queue
future = get_or_create_event_loop().create_future()
batch_queue.put(_SingleRequest(self, flattened_args, future))
return future
@wraps(_func)
def generator_batch_wrapper(*args, **kwargs):
first_future = enqueue_request(args, kwargs)
return batch_handler_generator(first_future)
@wraps(_func)
async def batch_wrapper(*args, **kwargs):
# This will raise if the underlying call raised an exception.
return await enqueue_request(args, kwargs)
if isasyncgenfunction(_func):
wrapper = generator_batch_wrapper
else:
wrapper = batch_wrapper
# We store the lazy_batch_queue_wrapper's getters and setters as
# batch_wrapper attributes, so they can be accessed in user code.
wrapper._get_max_batch_size = lazy_batch_queue_wrapper.get_max_batch_size
wrapper._get_batch_wait_timeout_s = (
lazy_batch_queue_wrapper.get_batch_wait_timeout_s
)
wrapper.set_max_batch_size = lazy_batch_queue_wrapper.set_max_batch_size
wrapper.set_batch_wait_timeout_s = (
lazy_batch_queue_wrapper.set_batch_wait_timeout_s
)
# Store debugging methods in the lazy_batch_queue wrapper
wrapper._get_curr_iteration_start_time = (
lazy_batch_queue_wrapper._get_curr_iteration_start_time
)
wrapper._is_batching_task_alive = (
lazy_batch_queue_wrapper._is_batching_task_alive
)
wrapper._get_handling_task_stack = (
lazy_batch_queue_wrapper._get_handling_task_stack
)
return wrapper
# Unfortunately, this is required to handle both non-parametrized
# (@serve.batch) and parametrized (@serve.batch(**kwargs)) usage.
# In the former case, `serve.batch` will be called with the underlying
# function as the sole argument. In the latter case, it will first be
# called with **kwargs, then the result of that call will be called
# with the underlying function as the sole argument (i.e., it must be a
# "decorator factory.").
return _batch_decorator(_func) if callable(_func) else _batch_decorator
def _set_result_if_not_done(future: asyncio.Future, result: Any):
"""Sets the future's result if the future is not done."""
if not future.done():
future.set_result(result)
def _set_exception_if_not_done(future: asyncio.Future, exception: Any):
"""Sets the future's exception if the future is not done."""
if not future.done():
future.set_exception(exception)