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# Copyright (C) 2024 Charles O. Goddard
#
# This software is free software: you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This software 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 GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see http://www.gnu.org/licenses/.
"""
Module for computational graph execution.
Classes:
Task: Abstract base class representing a computational task.
Executor: Class for scheduling and executing directed acyclic task graphs.
"""
from abc import ABC, abstractmethod
from typing import Any, Dict, Iterator, List, Optional, Set, Tuple, Union
import networkx
import torch
import tqdm
from pydantic import BaseModel
from typing_extensions import Generic, TypeVar
ValueT = TypeVar("ValueT")
class Task(ABC, BaseModel, Generic[ValueT], frozen=True):
"""
Abstract base class representing a task in a computational graph.
This class should be extended to define specific tasks. Each task can have arguments (dependencies) and a defined execution strategy.
Attributes:
Generic[ValueT] (TypeVar): The type of the value that the task returns upon execution.
Methods:
arguments: Abstract method to define task arguments (dependencies).
execute: Abstract method to execute the task.
priority: Returns the priority of the task for scheduling purposes.
group_label: Returns an optional label for task grouping.
"""
@abstractmethod
def arguments(self) -> Dict[str, "Task"]:
"""
Returns a dictionary of arguments required for this task. The keys of the dictionary
are argument names, and the values are Task instances. These keys correspond to the
keyword argument names expected by the execute method.
For example, if this method returns {'input1': taskA, 'input2': taskB}, the execute
method should expect to be called as execute(input1=valueA, input2=valueB), where
valueA and valueB are the outputs of taskA and taskB respectively.
Returns:
Dict[str, "Task"]: A dictionary mapping argument names to Task instances.
"""
...
@abstractmethod
def execute(self, **kwargs) -> ValueT:
"""
Executes the task using the results of its dependencies.
The keyword arguments (**kwargs) for this method are dynamically determined based on
the dictionary returned by the 'arguments' method. Each key in the 'arguments' method's
return dictionary becomes a keyword argument in this method, with its value being
the result of the corresponding task's execution.
Returns:
ValueT: The result of the task execution.
"""
...
def priority(self) -> int:
"""
Returns the priority of the task for scheduling.
Higher numbers indicate higher priority. Default is 0.
Returns:
int: The priority of the task.
"""
return 0
def group_label(self) -> Optional[str]:
"""
Returns an optional label used for grouping tasks together.
Returns:
Optional[str]: The group label of the task, if any.
"""
return None
def uses_accelerator(self) -> bool:
"""
Returns True if the task can take advantage of matrix operation
acceleration (such as on a GPU).
"""
return False
class Executor:
"""
Schedules and executes a set of tasks and their dependencies.
Handles scheduling, execution, the movement of data between devices, and the lifecycle of intermediate results.
Attributes:
math_device (torch.device): Device used for tensor computations.
storage_device (torch.device): Device used for storing intermediate results.
targets (List[Task]): List of target tasks to be executed.
schedule (List[Task]): Calculated execution schedule of tasks.
dependencies (Dict[Task, Set[Task]]): Dependencies of each task.
"""
math_device: torch.device
storage_device: torch.device
targets: List[Task]
schedule: List[Task]
dependencies: Dict[Task, Set[Task]]
def __init__(
self,
tasks: List[Task],
math_device: torch.device = torch.device("cpu"),
storage_device: torch.device = torch.device("cpu"),
):
"""
Initializes the Executor with a list of tasks and device configurations.
Args:
tasks (List[Task]): The list of tasks to be executed.
math_device (torch.device, optional): The device for tensor computations. Defaults to CPU.
storage_device (torch.device, optional): The device for storing results. Defaults to CPU.
"""
self.math_device = math_device
self.storage_device = storage_device
self.schedule = self._make_schedule(tasks)
self.targets = tasks
def run(self, quiet: bool = False) -> Iterator[Tuple[Task, Any]]:
"""
Execute the computed schedule and yield the target values.
Yields:
Iterator[Tuple[Task, Any]]: An iterator of task-result pairs.
"""
# determine last usage of each value, so they can be evicted afterwards
last_use_index = {}
for idx, task in reversed(list(enumerate(self.schedule))):
for t in self.dependencies[task]:
if t not in last_use_index:
last_use_index[t] = idx
if task not in last_use_index:
last_use_index[task] = idx
values: Dict[Task, Any] = {}
for idx, task in (
pbar := tqdm.tqdm(
list(enumerate(self.schedule)),
disable=quiet,
desc="Executing graph",
)
):
use_math_device = task.uses_accelerator()
arguments = {}
for name, dep in task.arguments().items():
value = values[dep]
# ensure any input tensors are on math device if task asks for it
if use_math_device:
if (
isinstance(value, torch.Tensor)
and value.device != self.math_device
):
value = value.to(self.math_device)
elif isinstance(value, dict):
for key in value:
if (
isinstance(value[key], torch.Tensor)
and value[key].device != self.math_device
):
value[key] = value[key].to(self.math_device)
arguments[name] = value
del value
res = task.execute(**arguments)
del arguments
if isinstance(res, torch.Tensor) and res.device != self.storage_device:
res = res.to(self.storage_device)
values[task] = res
del res
if task in self.targets:
yield (task, values[task])
# evict unreferenced values
expired = []
for key in values:
if idx >= last_use_index[key]:
expired.append(key)
for key in expired:
del values[key]
del values
del pbar
def execute(self) -> None:
"""
Execute all tasks and discard results.
"""
for task, value in self.run():
pass
DUMMY_TASK_VALUE = "!!DUMMY!!"
def _make_schedule(self, targets: List[Task]) -> List[Task]:
self.schedule = []
self.dependencies = self._build_dependencies(targets)
edge_tups = []
for node in self.dependencies:
for dependency in self.dependencies[node]:
edge_tups.append((dependency, node))
for task in targets:
# add edges from a dummy node to each target to guarantee
# they will be included in the final schedule
edge_tups.append((Executor.DUMMY_TASK_VALUE, task))
def _compare_key(task: Union[Task, str]):
if task == Executor.DUMMY_TASK_VALUE:
return ("", 0)
return (
task.group_label() or "",
-task.priority(),
)
graph = networkx.DiGraph(edge_tups)
res = [
t
for t in networkx.lexicographical_topological_sort(graph, key=_compare_key)
if t != Executor.DUMMY_TASK_VALUE
]
return res
def _build_dependencies(self, targets: List[Task]) -> Dict[Task, Set[Task]]:
task_dependencies: Dict[Task, Set[Task]] = {}
to_process = list(targets)
while to_process:
child = to_process.pop()
if child in task_dependencies:
continue
task_dependencies[child] = set()
for _, dep in child.arguments().items():
task_dependencies[child].add(dep)
to_process.append(dep)
return task_dependencies
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