Source code for covalent._workflow.lattice

# 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
# 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.
# Relief from the License may be granted by purchasing a commercial license.

"""Class corresponding to computation workflow."""

import json
import os
import warnings
from builtins import list
from contextlib import redirect_stdout
from copy import deepcopy
from dataclasses import asdict
from functools import wraps
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union

from .._shared_files import logger
from .._shared_files.config import get_config
from .._shared_files.context_managers import active_lattice_manager
from .._shared_files.defaults import DefaultMetadataValues
from .._shared_files.utils import get_named_params, get_serialized_function_str
from .depsbash import DepsBash
from .depscall import DepsCall
from .depspip import DepsPip
from .transport import TransportableObject, _TransportGraph, encode_metadata

    from .._results_manager.result import Result
    from ..executor import BaseExecutor

from .._shared_files.utils import get_imports, get_serialized_function_str

consumable_constraints = []

DEFAULT_METADATA_VALUES = asdict(DefaultMetadataValues())

app_log = logger.app_log
log_stack_info = logger.log_stack_info

[docs]class Lattice: """ A lattice workflow object that holds the work flow graph and is returned by :obj:`lattice <covalent.lattice>` decorator. Attributes: workflow_function: The workflow function that is decorated by :obj:`lattice <covalent.lattice>` decorator. transport_graph: The transport graph which will be the basis on how the workflow is executed. metadata: Dictionary of metadata of the lattice. post_processing: Boolean to indicate if the lattice is in post processing mode or not. kwargs: Keyword arguments passed to the workflow function. electron_outputs: Dictionary of electron outputs received after workflow execution. """ def __init__( self, workflow_function: Callable, transport_graph: _TransportGraph = None ) -> None: self.workflow_function = workflow_function self.workflow_function_string = get_serialized_function_str(self.workflow_function) self.transport_graph = transport_graph or _TransportGraph() self.metadata = {} self.__name__ = self.workflow_function.__name__ self.__doc__ = self.workflow_function.__doc__ self.post_processing = False self.args = [] self.kwargs = {} self.named_args = {} self.named_kwargs = {} self.electron_outputs = {} self.lattice_imports, self.cova_imports = get_imports(self.workflow_function) self.cova_imports.update({"electron"}) self.workflow_function = TransportableObject.make_transportable(self.workflow_function) # To be called after build_graph def serialize_to_json(self) -> str: attributes = deepcopy(self.__dict__) attributes["workflow_function"] = self.workflow_function.to_dict() attributes["metadata"] = encode_metadata(self.metadata) attributes["transport_graph"] = None if self.transport_graph: attributes["transport_graph"] = self.transport_graph.serialize_to_json() attributes["args"] = [] attributes["kwargs"] = {} for arg in self.args: attributes["args"].append(arg.to_dict()) for k, v in self.kwargs.items(): attributes["kwargs"][k] = v.to_dict() for k, v in self.named_args.items(): attributes["named_args"][k] = v.to_dict() for k, v in self.named_kwargs.items(): attributes["named_kwargs"][k] = v.to_dict() attributes["electron_outputs"] = {} for node_name, output in self.electron_outputs.items(): attributes["electron_outputs"][node_name] = output.to_dict() attributes["cova_imports"] = list(self.cova_imports) # for k, v in attributes.items(): # print(k, type(v)) return json.dumps(attributes) @staticmethod def deserialize_from_json(json_data: str) -> None: attributes = json.loads(json_data) attributes["cova_imports"] = set(attributes["cova_imports"]) for node_name, object_dict in attributes["electron_outputs"].items(): attributes["electron_outputs"][node_name] = TransportableObject.from_dict(object_dict) for k, v in attributes["named_kwargs"].items(): attributes["named_kwargs"][k] = TransportableObject.from_dict(v) for k, v in attributes["named_args"].items(): attributes["named_args"][k] = TransportableObject.from_dict(v) for k, v in attributes["kwargs"].items(): attributes["kwargs"][k] = TransportableObject.from_dict(v) for i, arg in enumerate(attributes["args"]): attributes["args"][i] = TransportableObject.from_dict(arg) if attributes["transport_graph"]: tg = _TransportGraph() tg.deserialize_from_json(attributes["transport_graph"]) attributes["transport_graph"] = tg attributes["workflow_function"] = TransportableObject.from_dict( attributes["workflow_function"] ) def dummy_function(x): return x lat = Lattice(dummy_function) lat.__dict__ = attributes return lat
[docs] def set_metadata(self, name: str, value: Any) -> None: """ Function to add/edit metadata of given name and value to lattice's metadata. Args: name: Name of the metadata to be added/edited. value: Value of the metadata to be added/edited. Returns: None """ self.metadata[name] = value
[docs] def get_metadata(self, name: str) -> Any: """ Get value of the metadata of given name. Args: name: Name of the metadata whose value is needed. Returns: value: Value of the metadata of given name. Raises: KeyError: If metadata of given name is not present. """ return self.metadata.get(name, None)
[docs] def build_graph(self, *args, **kwargs) -> None: """ Builds the transport graph for the lattice by executing the workflow function which will trigger the call of all underlying electrons and they will get added to the transport graph for later execution. Also redirects any print statements inside the lattice function to null and ignores any exceptions caused while executing the function. GRAPH WILL NOT BE BUILT AFTER AN EXCEPTION HAS OCCURRED. Args: *args: Positional arguments to be passed to the workflow function. **kwargs: Keyword arguments to be passed to the workflow function. Returns: None """ self.args = [TransportableObject.make_transportable(arg) for arg in args] self.kwargs = {k: TransportableObject.make_transportable(v) for k, v in kwargs.items()} self.transport_graph.reset() workflow_function = self.workflow_function.get_deserialized() named_args, named_kwargs = get_named_params(workflow_function, self.args, self.kwargs) self.named_args = named_args self.named_kwargs = named_kwargs new_args = [v.get_deserialized() for _, v in named_args.items()] new_kwargs = {k: v.get_deserialized() for k, v in named_kwargs.items()} with redirect_stdout(open(os.devnull, "w")): with active_lattice_manager.claim(self): try: workflow_function(*new_args, **new_kwargs) except Exception: warnings.warn( "Please make sure you are not manipulating an object inside the lattice." ) raise # Set workflow executor if not set by user postprocessor = self.metadata["workflow_executor"] if not postprocessor: pp = {} pp["workflow_executor"] = DEFAULT_METADATA_VALUES["workflow_executor"] pp = encode_metadata(pp) self.metadata["workflow_executor"] = pp["workflow_executor"] self.metadata["workflow_executor_data"] = pp["workflow_executor_data"]
[docs] def draw(self, *args, **kwargs) -> None: """ Generate lattice graph and display in UI taking into account passed in arguments. Args: *args: Positional arguments to be passed to build the graph. **kwargs: Keyword arguments to be passed to build the graph. Returns: None """ import covalent_ui.result_webhook as result_webhook self.build_graph(*args, **kwargs) result_webhook.send_draw_request(self)
def __call__(self, *args, **kwargs): """Execute lattice as an ordinary function for testing purposes.""" workflow_function = self.workflow_function.get_deserialized() return workflow_function(*args, **kwargs)
[docs] def dispatch(self, *args, **kwargs) -> str: """ DEPRECATED: Function to dispatch workflows. Args: *args: Positional arguments for the workflow **kwargs: Keyword arguments for the workflow Returns: Dispatch id assigned to job """ app_log.warning( "workflow.dispatch(your_arguments_here) is deprecated and may get removed without notice in future releases. Please use covalent.dispatch(workflow)(your_arguments_here) instead.", exc_info=DeprecationWarning, ) from .._dispatcher_plugins import local_dispatch return local_dispatch(self)(*args, **kwargs)
[docs] def dispatch_sync(self, *args, **kwargs) -> "Result": """ DEPRECATED: Function to dispatch workflows synchronously by waiting for the result too. Args: *args: Positional arguments for the workflow **kwargs: Keyword arguments for the workflow Returns: Result of workflow execution """ app_log.warning( "workflow.dispatch_sync(your_arguments_here) is deprecated and may get removed without notice in future releases. Please use covalent.dispatch_sync(workflow)(your_arguments_here) instead.", exc_info=DeprecationWarning, ) from .._dispatcher_plugins import local_dispatch_sync return local_dispatch_sync(self)(*args, **kwargs)
def lattice( _func: Optional[Callable] = None, *, backend: Optional[str] = None, executor: Optional[Union[List[Union[str, "BaseExecutor"]], Union[str, "BaseExecutor"]]] = None, results_dir: Optional[str] = get_config("dispatcher.results_dir"), workflow_executor: Optional[ Union[List[Union[str, "BaseExecutor"]], Union[str, "BaseExecutor"]] ] = None, # Add custom metadata fields here deps_bash: Union[DepsBash, list, str] = None, deps_pip: Union[DepsPip, list] = None, call_before: Union[List[DepsCall], DepsCall] = [], call_after: Union[List[DepsCall], DepsCall] = [], # e.g. schedule: True, whether to use a custom scheduling logic or not ) -> Lattice: """ Lattice decorator to be called upon a function. Returns a new `Lattice <covalent._workflow.lattice.Lattice>` object. Args: _func: function to be decorated Keyword Args: backend: DEPRECATED: Same as `executor`. executor: Alternative executor object to be used in the execution of each node. If not passed, the local executor is used by default. workflow_executor: Executor for postprocessing the workflow. Defaults to the built-in dask executor or the local executor depending on whether Covalent is started with the `--no-cluster` option. results_dir: Directory to store the results deps_bash: An optional DepsBash object specifying a list of shell commands to run before `_func` deps_pip: An optional DepsPip object specifying a list of PyPI packages to install before running `_func` call_before: An optional list of DepsCall objects specifying python functions to invoke before the electron call_after: An optional list of DepsCall objects specifying python functions to invoke after the electron Returns: :obj:`Lattice <covalent._workflow.lattice.Lattice>` : Lattice object inside which the decorated function exists. """ if backend: app_log.warning( "backend is deprecated and will be removed in a future release. Please use executor keyword instead.", exc_info=DeprecationWarning, ) executor = backend results_dir = str(Path(results_dir).expanduser().resolve()) deps = {} if isinstance(deps_bash, DepsBash): deps["bash"] = deps_bash if isinstance(deps_bash, (list, str)): deps["bash"] = DepsBash(commands=deps_bash) if isinstance(deps_pip, DepsPip): deps["pip"] = deps_pip if isinstance(deps_pip, list): deps["pip"] = DepsPip(packages=deps_pip) if isinstance(call_before, DepsCall): call_before = [call_before] if isinstance(call_after, DepsCall): call_after = [call_after] constraints = { "executor": executor, "results_dir": results_dir, "workflow_executor": workflow_executor, "deps": deps, "call_before": call_before, "call_after": call_after, } constraints = encode_metadata(constraints) def decorator_lattice(func=None): @wraps(func) def wrapper_lattice(*args, **kwargs): lattice_object = Lattice(workflow_function=func) for k, v in constraints.items(): lattice_object.set_metadata(k, v) lattice_object.transport_graph.lattice_metadata = lattice_object.metadata return lattice_object return wrapper_lattice() if _func is None: # decorator is called with arguments return decorator_lattice else: # decorator is called without arguments return decorator_lattice(_func)