Source code for covalent.executor.base

# Copyright 2021 Agnostiq Inc.
#
# This file is part of Covalent.
#
# Licensed under the GNU Affero General Public License 3.0 (the "License").
# A copy of the License may be obtained with this software package or at
#
#      https://www.gnu.org/licenses/agpl-3.0.en.html
#
# Use of this file is prohibited except in compliance with the License. Any
# modifications or derivative works of this file must retain this copyright
# notice, and modified files must contain a notice indicating that they have
# been altered from the originals.
#
# Covalent is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the License for more details.
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# Relief from the License may be granted by purchasing a commercial license.

"""
Class that defines the base executor template.
"""

import asyncio
import copy
import io
import json
import os
import queue
from abc import ABC, abstractmethod
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import (
    Any,
    Callable,
    ContextManager,
    Dict,
    Iterable,
    List,
    Literal,
    Optional,
    Tuple,
    Union,
)

import aiofiles

from covalent._shared_files.exceptions import TaskCancelledError
from covalent._workflow.depscall import RESERVED_RETVAL_KEY__FILES
from covalent.executor.utils import Signals

from .._shared_files import TaskRuntimeError, logger
from .._shared_files.context_managers import active_dispatch_info_manager
from .._shared_files.util_classes import RESULT_STATUS, DispatchInfo
from .._workflow.transport import TransportableObject

app_log = logger.app_log
log_stack_info = logger.log_stack_info
TypeJSON = Union[str, int, float, bool, None, Dict[str, Any], List[Any]]


def wrapper_fn(
    function: TransportableObject,
    call_before: List[Tuple[TransportableObject, TransportableObject, TransportableObject]],
    call_after: List[Tuple[TransportableObject, TransportableObject, TransportableObject]],
    *args,
    **kwargs,
):
    """Wrapper for serialized callable.

    Execute preparatory shell commands before deserializing and
    running the callable. This is the actual function to be sent to
    the various executors.

    """

    cb_retvals = {}
    for tup in call_before:
        serialized_fn, serialized_args, serialized_kwargs, retval_key = tup
        cb_fn = serialized_fn.get_deserialized()
        cb_args = serialized_args.get_deserialized()
        cb_kwargs = serialized_kwargs.get_deserialized()
        retval = cb_fn(*cb_args, **cb_kwargs)

        # we always store cb_kwargs dict values as arrays to factor in non-unique values
        if retval_key and retval_key in cb_retvals:
            cb_retvals[retval_key].append(retval)
        elif retval_key:
            cb_retvals[retval_key] = [retval]

    # if cb_retvals key only contains one item this means it is a unique (non-repeated) retval key
    # so we only return the first element however if it is a 'files' kwarg we always return as a list
    cb_retvals = {
        key: value[0] if len(value) == 1 and key != RESERVED_RETVAL_KEY__FILES else value
        for key, value in cb_retvals.items()
    }

    fn = function.get_deserialized()

    new_args = [arg.get_deserialized() for arg in args]

    new_kwargs = {k: v.get_deserialized() for k, v in kwargs.items()}

    # Inject return values into kwargs
    for key, val in cb_retvals.items():
        new_kwargs[key] = val

    output = fn(*new_args, **new_kwargs)

    for tup in call_after:
        serialized_fn, serialized_args, serialized_kwargs, retval_key = tup
        ca_fn = serialized_fn.get_deserialized()
        ca_args = serialized_args.get_deserialized()
        ca_kwargs = serialized_kwargs.get_deserialized()
        ca_fn(*ca_args, **ca_kwargs)

    return TransportableObject(output)


class _AbstractBaseExecutor(ABC):
    """
    Private parent class for BaseExecutor and AsyncBaseExecutor

    Attributes:
        log_stdout: The path to the file to be used for redirecting stdout.
        log_stderr: The path to the file to be used for redirecting stderr.
        cache_dir: The location used for cached files in the executor.
        time_limit: time limit for the task
        retries: Number of times to retry execution upon failure

    """

    def __init__(
        self,
        log_stdout: str = "",
        log_stderr: str = "",
        cache_dir: str = "",
        time_limit: int = -1,
        retries: int = 0,
        *args,
        **kwargs,
    ):
        self.log_stdout = log_stdout
        self.log_stderr = log_stderr
        self.cache_dir = cache_dir
        self.time_limit = time_limit
        self.retries = retries

    def get_dispatch_context(self, dispatch_info: DispatchInfo) -> ContextManager[DispatchInfo]:
        """
        Start a context manager that will be used to
        access the dispatch info for the executor.

        Args:
            dispatch_info: The dispatch info to be used inside current context.

        Returns:
            A context manager object that handles the dispatch info.
        """

        return active_dispatch_info_manager.claim(dispatch_info)

    def short_name(self):
        module = self.__module__
        return self.__module__.split("/")[-1].split(".")[-1]

    def to_dict(self) -> dict:
        """Return a JSON-serializable dictionary representation of self"""
        return {
            "type": str(self.__class__),
            "short_name": self.short_name(),
            "attributes": self.__dict__.copy(),
        }

    def from_dict(self, object_dict: dict) -> "BaseExecutor":
        """Rehydrate a dictionary representation

        Args:
            object_dict: a dictionary representation returned by `to_dict`

        Returns:
            self

        Instance attributes will be overwritten.
        """
        if object_dict:
            self.__dict__ = copy.deepcopy(object_dict["attributes"])
        return self

    @property
    def task_stdout(self):
        return self.__dict__.get("_task_stdout")

    @property
    def task_stderr(self):
        return self.__dict__.get("_task_stderr")


[docs]class BaseExecutor(_AbstractBaseExecutor): """ Base executor class to be used for defining any executor plugin. Subclassing this class will allow you to define your own executor plugin which can be used in covalent. Attributes: log_stdout: The path to the file to be used for redirecting stdout. log_stderr: The path to the file to be used for redirecting stderr. cache_dir: The location used for cached files in the executor. time_limit: time limit for the task retries: Number of times to retry execution upon failure """ def __init__( self, *args, **kwargs, ) -> None: super().__init__(*args, **kwargs) def _init_runtime( self, loop: Optional[asyncio.AbstractEventLoop] = None, cancel_pool: Optional[ThreadPoolExecutor] = None, ) -> None: """ Create the required queues for cancel task messages to be shared back and forth Arg(s) loop: Asyncio event loop to create tasks on cancel_pool: A ThreadPoolExecutor object to submit tasks to Return(s) None """ self._send_queue = asyncio.Queue() self._recv_queue = queue.Queue() self._loop = loop self._cancel_pool = cancel_pool def _notify(self, action: Signals, body: Any = None) -> None: """ Notifies a waiting thread with the necessary action to take along with the arguments passed in as body Arg(s) action: One of three possible actions that a waiting thread must take -> Signals.GET, Signals.PUT, Signals.EXIT body: Respective arguments for each action Return(s) None """ self._loop.call_soon_threadsafe(self._send_queue.put_nowait, (action, body)) def _notify_sync(self, action: Signals, body: Any = None) -> Any: """ Blocking version of the _notify method Arg(s) action: One of three possible actions that a waiting thread must take -> Signals.GET, Signals.PUT, Signals.EXIT body: Respective arguments for each action """ self._notify(action, body) return self._wait_for_response() def _wait_for_response(self, timeout: int = 5) -> Any: """ Wait for response from a thread depending on the action/body parameters sent Arg(s): timeout: Number of seconds to wait for the result to become available in the queue. Defaults to five seconds Return(s) body: Response to the corresponding action """ status, body = self._recv_queue.get(timeout=timeout) if status is None: raise RuntimeError("Error waiting for response") return body
[docs] def get_cancel_requested(self) -> bool: """ Check if the task was requested to be cancelled by the user Arg(s) None Return(s) True/False whether task cancellation was requested """ return self._notify_sync(Signals.GET, "cancel_requested")
[docs] def set_job_handle(self, handle: TypeJSON) -> Any: """ Save the job_id/handle returned by the backend executing the task Arg(s) handle: Any JSONable type to identifying the task being executed by the backend Return(s) Response from saving the job handle to database """ return self._notify_sync(Signals.PUT, ("job_handle", json.dumps(handle)))
[docs] def write_streams_to_file( self, stream_strings: Iterable[str], filepaths: Iterable[str], dispatch_id: str, results_dir: str, ) -> None: """ Write the contents of stdout and stderr to respective files. Args: stream_strings: The stream_strings to be written to files. filepaths: The filepaths to be used for writing the streams. dispatch_id: The ID of the dispatch which initiated the request. results_dir: The location of the results directory. """ for ss, filepath in zip(stream_strings, filepaths): if filepath: # If it is a relative path, attach to results dir if not Path(filepath).expanduser().is_absolute(): filepath = os.path.join(results_dir, dispatch_id, filepath) filename = Path(filepath) filename = filename.expanduser() filename.parent.mkdir(parents=True, exist_ok=True) filename.touch(exist_ok=True) with open(filepath, "a") as f: f.write(ss)
async def _execute( self, function: Callable, args: List, kwargs: Dict, dispatch_id: str, results_dir: str, node_id: int = -1, ) -> Any: loop = asyncio.get_running_loop() return await loop.run_in_executor( None, self.execute, function, args, kwargs, dispatch_id, results_dir, node_id, )
[docs] def execute( self, function: Callable, args: List, kwargs: Dict, dispatch_id: str, results_dir: str, node_id: int = -1, ) -> Any: """ Execute the function with the given arguments. This calls the executor-specific `run()` method. Args: function: The input python function which will be executed and whose result is ultimately returned by this function. args: List of positional arguments to be used by the function. kwargs: Dictionary of keyword arguments to be used by the function. dispatch_id: The unique identifier of the external lattice process which is calling this function. results_dir: The location of the results directory. node_id: ID of the node in the transport graph which is using this executor. Returns: output: The result of the function execution. """ dispatch_info = DispatchInfo(dispatch_id) fn_version = function.args[0].python_version self._task_stdout = io.StringIO() self._task_stderr = io.StringIO() task_metadata = { "dispatch_id": dispatch_id, "node_id": node_id, "results_dir": results_dir, } try: self.setup(task_metadata=task_metadata) result = self.run(function, args, kwargs, task_metadata) job_status = RESULT_STATUS.COMPLETED except TaskRuntimeError as err: job_status = RESULT_STATUS.FAILED result = None except TaskCancelledError as err: job_status = RESULT_STATUS.CANCELLED result = None finally: self._notify(Signals.EXIT) self.teardown(task_metadata=task_metadata) self.write_streams_to_file( (self._task_stdout.getvalue(), self._task_stderr.getvalue()), (self.log_stdout, self.log_stderr), dispatch_id, results_dir, ) return (result, self._task_stdout.getvalue(), self._task_stderr.getvalue(), job_status)
[docs] def setup(self, task_metadata: Dict) -> Any: """Placeholder to run any executor specific tasks""" pass
[docs] @abstractmethod def run(self, function: Callable, args: List, kwargs: Dict, task_metadata: Dict) -> Any: """Abstract method to run a function in the executor. Args: function: The function to run in the executor args: List of positional arguments to be used by the function kwargs: Dictionary of keyword arguments to be used by the function. task_metadata: Dictionary of metadata for the task. Current keys are `dispatch_id` and `node_id` Returns: output: The result of the function execution """ raise NotImplementedError
[docs] def cancel(self, task_metadata: Dict, job_handle: Any) -> Literal[False]: """ Method to cancel the job identified uniquely by the `job_handle` (base class) Arg(s) task_metadata: Metadata of the task to be cancelled job_handle: Unique ID of the job assigned by the backend Return(s) False by default """ app_log.debug(f"Cancel not implemented for executor {type(self)}") return False
async def _cancel(self, task_metadata: Dict, job_handle: Any) -> Any: """ Cancel the task in a non-blocking manner Arg(s) task_metadata: Metadata of the task to be cancelled job_handle: Unique ID of the job assigned by the backend Return(s) Result of the task cancellation """ cancel_result = await self._loop.run_in_executor( self._cancel_pool, self.cancel, task_metadata, job_handle ) await self._loop.run_in_executor(self._cancel_pool, self.teardown, task_metadata) return cancel_result
[docs] def teardown(self, task_metadata: Dict) -> Any: """Placeholder to run any executor specific cleanup/teardown actions""" pass
[docs]class AsyncBaseExecutor(_AbstractBaseExecutor): """Async base executor class to be used for defining any executor plugin. Subclassing this class will allow you to define your own executor plugin which can be used in covalent. This is analogous to `BaseExecutor` except the `run()` method, together with the optional `setup()` and `teardown()` methods, are coroutines. Attributes: log_stdout: The path to the file to be used for redirecting stdout. log_stderr: The path to the file to be used for redirecting stderr. cache_dir: The location used for cached files in the executor. time_limit: time limit for the task retries: Number of times to retry execution upon failure """ def __init__( self, *args, **kwargs, ) -> None: super().__init__(*args, **kwargs) def _init_runtime( self, loop: Optional[asyncio.AbstractEventLoop] = None, cancel_pool: Optional[ThreadPoolExecutor] = None, ) -> None: """ Initialize the send and receive queues for communication between dispatcher and the executor Arg(s) loop: Asyncio event loop cancel_pool: Instance of a threadpool executor class Return(s) None """ self._send_queue = asyncio.Queue() self._recv_queue = asyncio.Queue() def _notify(self, action: Signals, body: Any = None) -> None: """ Notify the listener with the corresponding signal (async) Arg(s) action: Signal to the listener to trigger the corresponding action body: Message to be sent to the listener Return(s) None """ self._send_queue.put_nowait((action, body)) async def _notify_sync(self, action: Signals, body: Any = None) -> Any: """ Blocking call to the `_notify` method to wait for response Arg(s) action: Signal to the listener to trigger the corresponding action body: Message to be sent to the listener Return(s) Response from the listener """ self._notify(action, body) return await self._wait_for_response() async def _wait_for_response(self, timeout: int = 5) -> Any: """ Block the thread until a response is recevied Arg(s) timeout: Number of seconds to wait until timing out Return(s) Response from the listener """ aw = self._recv_queue.get() status, body = await asyncio.wait_for(aw, timeout=timeout) if status is False: raise RuntimeError("Error waiting for response") return body
[docs] async def get_cancel_requested(self) -> Any: """ Get if the task was requested to be canceled Arg(s) None Return(s) Whether the task has been requested to be cancelled """ return await self._notify_sync(Signals.GET, "cancel_requested")
[docs] async def set_job_handle(self, handle: TypeJSON) -> Any: """ Save the job handle to database Arg(s) handle: JSONable type identifying the job being executed by the backend Return(s) Response from the listener that handles inserting the job handle to database """ return await self._notify_sync(Signals.PUT, ("job_handle", json.dumps(handle)))
[docs] async def write_streams_to_file( self, stream_strings: Iterable[str], filepaths: Iterable[str], dispatch_id: str, results_dir: str, ) -> None: """ Write the contents of stdout and stderr to respective files. Args: stream_strings: The stream_strings to be written to files. filepaths: The filepaths to be used for writing the streams. dispatch_id: The ID of the dispatch which initiated the request. results_dir: The location of the results directory. This uses aiofiles to avoid blocking the event loop. """ for ss, filepath in zip(stream_strings, filepaths): if filepath: # If it is a relative path, attach to results dir if not Path(filepath).expanduser().is_absolute(): filepath = os.path.join(results_dir, dispatch_id, filepath) filename = Path(filepath) filename = filename.expanduser() filename.parent.mkdir(parents=True, exist_ok=True) filename.touch(exist_ok=True) async with aiofiles.open(filepath, "a") as f: await f.write(ss)
async def _execute( self, function: Callable, args: List, kwargs: Dict, dispatch_id: str, results_dir: str, node_id: int = -1, ) -> Any: return await self.execute( function, args, kwargs, dispatch_id, results_dir, node_id, ) async def execute( self, function: Callable, args: List, kwargs: Dict, dispatch_id: str, results_dir: str, node_id: int = -1, ) -> Any: self._task_stdout = io.StringIO() self._task_stderr = io.StringIO() task_metadata = { "dispatch_id": dispatch_id, "node_id": node_id, "results_dir": results_dir, } try: await self.setup(task_metadata=task_metadata) result = await self.run(function, args, kwargs, task_metadata) job_status = RESULT_STATUS.COMPLETED except TaskCancelledError as err: job_status = RESULT_STATUS.CANCELLED result = None except TaskRuntimeError as err: job_status = RESULT_STATUS.FAILED result = None finally: self._notify(Signals.EXIT) await self.teardown(task_metadata=task_metadata) await self.write_streams_to_file( (self._task_stdout.getvalue(), self._task_stderr.getvalue()), (self.log_stdout, self.log_stderr), dispatch_id, results_dir, ) return (result, self._task_stdout.getvalue(), self._task_stderr.getvalue(), job_status)
[docs] async def setup(self, task_metadata: Dict): """Executor specific setup method""" pass
[docs] async def teardown(self, task_metadata: Dict): """Executor specific teardown method""" pass
[docs] @abstractmethod async def run(self, function: Callable, args: List, kwargs: Dict, task_metadata: Dict) -> Any: """Abstract method to run a function in the executor in async-aware manner. Args: function: The function to run in the executor args: List of positional arguments to be used by the function kwargs: Dictionary of keyword arguments to be used by the function. task_metadata: Dictionary of metadata for the task. Current keys are `dispatch_id` and `node_id` Returns: output: The result of the function execution """ raise NotImplementedError
[docs] async def cancel(self, task_metadata: Dict, job_handle: Any) -> Literal[False]: """ Executor specific task cancellation method Arg(s) task_metadata: Metadata associated with the task to be cancelled job_handle: Unique ID assigned to the job by the backend Return(s) False by default """ app_log.debug(f"Cancel not implemented for executor {type(self)}") return False
async def _cancel(self, task_metadata: Dict, job_handle: Any) -> Literal[False]: """ Cancel the task in a non-blocking manner and teardown the infrastructure Arg(s) task_metadata: Metadata associated with the task to be cancelled job_handle: Unique ID assigned to the job by the backend Return(s) Result from cancelling the task """ cancel_result = await self.cancel(task_metadata, job_handle) await self.teardown(task_metadata) return cancel_result