Source code for gmx.workflow

Provide workflow-level utilities and classes

Single-sim example:

.. code-block:: python

    >>> md = gmx.workflow.from_tpr(filename)
    >>> # The above is shorthand for
    >>> md = gmx.workflow.from_tpr(filename)
    >>> with gmx.get_context(md.workspec) as session:

Array sim example:

.. code-block:: python

    >>> md = gmx.workflow.from_tpr([filename1, filename2])

The representation of work and the way it is dispatched are areas of active
development. See also

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

__all__ = ['WorkSpec', 'WorkElement']

import warnings
import weakref

from gmx import exceptions
from gmx import logging
from gmx.util import to_string
from gmx.util import to_utf8

# Module-level logger
logger = logging.getLogger(__name__)'Importing gmx.workflow')

# Work specification version string.
workspec_version = "gmxapi_workspec_0_1""Using schema version {}.".format(workspec_version))

# module-level constant indicating a workflow implementing parallel array work.
# ARRAY = 0

class GmxMap(dict):
    """Utility/compatibility class to ensure consistent keys.

    Allow subscripting to use native str or Python 2 Unicode objects.
    Internally, converts all keys to native str for the current interpreter.
    def keys(self):
        for key in dict.keys(self):
            if not isinstance(key, str):
                raise exceptions.ApiError('Invalid key type found: {} {}'.format(key, type(key)))
            yield key

    def __getitem__(self, key):
        return super(GmxMap, self).__getitem__(str(key))

    def __setitem__(self, key, item):
        super(GmxMap, self).__setitem__(str(key), item)

    def __delitem__(self, key):
        super(GmxMap, self).__delitem__(str(key))

[docs]class WorkSpec(object): """ Container of workflow elements with data dependency information and requirements for execution. An element cannot be added to a WorkSpec if it has dependencies that are not in the WorkSpec. Work is added to the specification by passing a WorkElement object to :py:func:`WorkSpec.add_element()`. Any dependencies in the WorkElement must already be specified in the target WorkSpec. When iterated over, a WorkSpec object returns WorkElement objects. WorkElement objects are yielded in a valid order to keep dependencies satisfied, but not necessarily the same order in which add_element() calls were originally made. In other words, the WorkSpec is a directed acyclic dependency graph, and its iterator returns nodes in an arbitrary but topologically correct order. The string representation of a WorkSpec object is a valid JSON serialized data object. The schema for version 0.1 of the specification allows data structures like the following. :: { 'version': 'gmxapi_workspec_0_1', 'elements': { 'myinput': { 'namespace': 'gromacs', 'operation': 'load_tpr', 'params': {'input': ['tpr_filename1', 'tpr_filename2']} }, 'mydata': { 'namespace': 'gmxapi', 'operation': 'open_global_data_with_barrier', 'params': ['data_filename'] }, 'mypotential': { 'namespace': 'myplugin', 'operation': 'create_mdmodule', 'params': {...}, 'depends': ['mydata'] }, 'mysim': { 'namespace': 'gmxapi', 'operation': 'md', 'depends': ['myinput', 'mypotential'] } } } The first mapping (``version``) is required as shown. The ``elements`` map contains uniquely named elements specifying an operation, the operation's namespace, and parameters and dependencies of the operation for this element. ``depends`` is a sequence of string names of elements that are also in the work spec. ``params`` is a key-value map with string keys and values that are valid JSON data. ``namespace`` and ``operation`` are strings that the :py:class:`Context <gmx.context.Context>` can map to directors it uses to construct the session. Namespace ``gmxapi`` is reserved for operations specified by the API. Namespace ``gromacs`` is reserved for operations implemented as GROMACS adapters (versioned separately from gmxapi). The period character (".") has special meaning and should not be used in naming elements, namespaces, or operations. """ def __init__(self): self.version = workspec_version self.elements = GmxMap() self.__context_weak_ref = None @property def _context(self): referent = None if self.__context_weak_ref is not None: referent = self.__context_weak_ref() return referent @_context.setter def _context(self, context): # We're moving towards having the context own the work, so the work should # not own the context. self.__context_weak_ref = weakref.ref(context) def _chase_deps(self, source_set, name_list): """Helper to recursively generate dependencies before dependents. Given a set of WorkElement objects and a list of element names, generate WorkElements for the members of name_list plus their dependencies in an order such that dependencies are guaranteed to occur before their dependent elements. For example, to sequence an entire work specification into a reasonable order for instantiation, use >>> workspec._chase_deps(set(workspec.elements.keys()), list(workspec.elements.keys())) Note: as a member function of WorkSpec, we have access to the full WorkSpec elements data at all times, giving us extra flexibility in implementation and arguments. Args: sources: a (super)set of element names from the current work spec (will be consumed) name_list: subset of *sources* to be sequenced Returns: Sequence of WorkElement objects drawn from the names in *source_set* Requires that WorkElements named in *name_list* and any elements on which they depend are all named in *source_list* and available in the current work spec. Note: *source_set* is a reference to an object that is modified arbitrarily. The caller should not re-use the object after calling _chase_deps(). TODO: Separate out DAG topology operations from here and Context.__enter__() Our needs are simple enough that we probably don't need an external dependency like networkx... """ # Recursively (depth-first) generate a topologically valid serialized DAG from source_set. assert isinstance(source_set, set) # Warning: This is not at all a rigorous check. # It is hard to check whether this is string-like or list-like in both Py 2.7 and 3.x if not isinstance(name_list, (list, tuple, set)): raise exceptions.ValueError('Must disambiguate "name_list" by passing a list or tuple.') # Make a copy of name_list in case the input reference is being used elsewhere during # iteration, such as for source_set, which is modified during the loop. for name in tuple(name_list): if name in source_set: source_set.remove(name) element = WorkElement.deserialize(self.elements[name], name=name, workspec=self) dependencies = element.depends # items in element.depends are either element names or ensembles of element names. for item in dependencies: if isinstance(item, (list, tuple, set)): dependency_list = item else: if not isinstance(item, str): raise exceptions.ValueError( 'Dependencies should be a string or sequence of strings. Got {}'.format(type(item))) dependency_list = [item] for dependency in dependency_list: for recursive_dep in self._chase_deps(source_set, (dependency,)): yield recursive_dep yield element else: # Note: The user is responsible for ensuring that source_set is complete. # Otherwise, we would need to maintain a list of elements previously yielded. pass def __iter__(self): source_set = set(self.elements.keys()) for element in self._chase_deps(source_set, source_set): yield element def __hash__(self): """Uniquely identify this work specification. Allows the spec to be used as a dictionary key in Python. Note that this hash is possibly dependent on the Python implementation. It is not part of the gmxapi specification and should not be used outside of a single invocation of a script. """ # Hash the serialized elements, concatenated as a single string. Note that the order of elements and their # contents is not guaranteed, but should be consistent within a script invocation. return hash(to_string(self.serialize()))
[docs] def add_element(self, element): """Add an element to a work specification if possible. Adding an element to a WorkSpec must preserve the validity of the workspec, which involves several checks. We do not yet check for element uniqueness beyond a string name. If an element is added that was previously in another WorkSpec, it must first be removed from the other WorkSpec. """ if hasattr(element, "namespace") and hasattr(element, "operation") and hasattr(element, "serialize"): if not hasattr(element, "name") or is None or len(str( < 1: raise exceptions.UsageError("Only named elements may be added to a WorkSpec.") if in self.elements: raise exceptions.UsageError("Elements in WorkSpec must be uniquely identifiable.") if hasattr(element, "depends"): for dependency in element.depends: if not dependency in self.elements: raise exceptions.UsageError("Element dependencies must already be specified before an Element may be added.") # Okay, it looks like we have an element we can add if hasattr(element, "workspec") and element.workspec is not None and element.workspec is not self: raise exceptions.Error("Element must be removed from its current WorkSpec to be added to this WorkSpec, but element removal is not yet implemented.") self.elements[] = element.serialize() element.workspec = self else: raise exceptions.ValueError("Provided object does not appear to be compatible with gmx.workflow.WorkElement.")"Added element {} to workspec.".format(
# def remove_element(self, name): # """Remove named element from work specification. # # Does not delete references to WorkElement objects, but WorkElement objects will be moved to a None WorkSpec.""" # To implement, WorkElement attributes should be reworked as properties that dynamically act on the # workspec reference. Additionally, WorkSpec may have to keep weak references to WorkElements in order # to reset the WorkElement.workspec strong reference. # def add(self, spec): # """ # Merge the provided spec into this one. # # We can't easily replace references to ``spec`` with references to the WorkSpec we are merging into, but we can # steal the work elements out of ``spec`` and leave it empty. We could also set an ``alias`` attribute in it or # something, but that seems unnecessary. Alternatively, we can set the new and old spec to be equal, but we would # need an additional abstraction layer to keep them from diverging again. Since client code will retain references # to the elements in the work spec, we need to be clear about when we are duplicating a WorkSpec versus obtaining # different references to the same. # # This is an implementation detail that can be unresolved and hidden for now. The high-level interface only # requires that client code can bind different workflow elements together in a sensible way and get expected # results. # # :param spec: WorkSpec to be merged into this one. # :return: # # To do: consider instead a gmx.workflow.merge(workspecA, workspecB) free function that returns a new WorkSpec. # """
[docs] def serialize(self): """Serialize the work specification in a form suitable to pass to any Context implementation. Serialization is performed with the JSON data serialization module. To simplify unique identification of work specifications, this function will also impose rules for reproducibility. 1. All key-value maps are sorted alphanumerically by their string keys. 2. Strings must consist of valid ASCII characters. 3. Output is a byte sequence of the utf-8 encoded densely formatted JSON document. Returns: ``unicode`` object in Python 2, ``bytes`` object in Python 3 Output of serialize() should be explicitly converted to a string before passing to a JSON deserializer. >>> my_object = my_workspec.serialize() >>> my_data_structure = json.loads(my_object.decode('utf-8')) >>> # or... >>> my_data_structure = json.loads(my_object, encoding='utf-8') """ import json # Build the normalized dictionary dict_representation = {'version': self.version, 'elements': {} } for name, element in [(e, json.loads(to_string(self.elements[e]))) for e in sorted(self.elements.keys())]: dict_representation['elements'][str(name)] = element serialization = json.dumps(dict_representation, ensure_ascii=True, sort_keys=True, separators=(',',':')) return serialization.encode('utf-8')
@classmethod def deserialize(serialized): import json workspec = WorkSpec() dict_representation = json.loads(to_string(serialized)) ver_in = dict_representation['version'] ver_out = workspec.version if ver_in != ver_out: message = "Expected work spec version {}. Got work spec version {}.".format(ver_out, ver_in) raise exceptions.CompatibilityError(message) for element in dict_representation['elements']: workspec.elements[element] = dict_representation['elements'][element] return workspec
[docs] def uid(self): """Get a unique identifier for this work specification. Returns: hash value Generate a cryptographic hash of this work specification that is guaranteed to match that of another equivalent work specification. The returned string is a 64-character hexadecimal encoded SHA-256 hash digest of the serialized WorkSpec. The definition of equivalence is likely to evolve, but currently means a work spec of the same version with the same named elements containing the same operations, dependencies, and parameters, as represented in the serialized version of the work specification. Note that this does not include checks on the actual contents of input files or anything that does not appear in the work specification directly. Also, the hash is lossy, so it is remotely conceivable that two specs could have the same hash. The work specs should be compared before making any expensive decisions based on work spec equivalence, such as with hash(workspec). Element names probably shouldn't be included in the unique identifying information (so that we can optimize out duplicated artifacts), but they are. A future API specification may add unique identification to the elements... """ # Get an alphanumeric string of the checksum of the serialized work spec. SHA-256 should require about 43 characters # of base64 to represent, which seems reasonable. We need to replace some of the base64 characters to make them # filesystem friendly, though. Hexadecimal may be more friendly, but would require 64 characters. import hashlib data = to_utf8(self.serialize()) result = hashlib.sha256(data) return result.hexdigest()
def __str__(self): """Generate string representation for str() or print(). The string output should look like the abstract schema for gmxapi_workspec_1_0, but the exact format is unspecified and may change in future versions. For consistent JSON output, use WorkSpec.serialize(). """ import json string = to_string(self.serialize()) data = json.loads(string) reserialized = json.dumps(data, indent=4, sort_keys=True) return str(reserialized) def __repr__(self): """Generate Pythonic representation for repr(workspec).""" return 'gmx.workflow.WorkSpec()'
# A possible alternative name for WorkElement would be Operator, since there is a one-to-one # mapping between WorkElements and applications of "operation"s. We need to keep in mind the # sensible distinction between the WorkElement abstraction and the API objects and DAG nodes.
[docs]class WorkElement(object): """Encapsulate an element of a work specification.""" def __init__(self, namespace="gmxapi", operation=None, params=None, depends=()): self._namespace = str(to_string(namespace)) # We can add an operations submodule to validate these. E.g. self.operation = gmx.workflow.operations.normalize(operation) if operation is not None: self._operation = str(to_string(operation)) else: raise exceptions.UsageError("Invalid argument type for operation.") # Note: Nothing currently prevents attribute updates by assignment after adding the element to a workspec, # but this protocol will be clarified with if params is None: self.params = GmxMap() elif isinstance(params, dict): self.params = GmxMap({to_string(name): params[name] for name in params}) else: raise exceptions.UsageError("If provided, params must be a dictionary of keyword arguments") self.depends = [] for d in depends: if isinstance(d, (list, tuple)): self.depends.append([str(name) for name in d]) else: self.depends.append(str(d)) # The Python class for work elements keeps a strong reference to a WorkSpec object containing its description self._name = None self._workspec = None @property def namespace(self): assert isinstance(self._namespace, str) return self._namespace @property def operation(self): assert isinstance(self._operation, str) return self._operation @property def name(self): assert isinstance(self._name, (str, type(None))) return self._name @name.setter def name(self, new_name): self._name = str(to_string(new_name)) @property def workspec(self): return self._workspec @workspec.setter def workspec(self, input): self._workspec = input
[docs] def add_dependency(self, element): """Add another element as a dependency. First move the provided element to the same WorkSpec, if not already here. Then, add to ``depends`` and update the WorkSpec. """ def check_element(element): if element.workspec is None: self.workspec.add_element(element) assert element.workspec is self.workspec assert in self.workspec.elements elif element.workspec is not self.workspec: raise exceptions.ApiError("Element will need to be moved to the same workspec.") return True if hasattr(element, 'workspec') and hasattr(element, 'name'): check_element(element) self.depends.append( else: assert isinstance(element, (list, tuple)) self.depends.append(tuple([ for item in element if check_element(item)])) self.workspec.elements[] = self.serialize()
[docs] def serialize(self): """Create a byte sequence representation of the work element. The WorkElement class exists just to provide convenient handles in Python. The WorkSpec is not actually a container of WorkElement objects. Returns: Byte sequence of utf-8 encoded JSON document. May need to be decoded if needed as a (Unicode) string. """ import json output_dict = {'namespace': self.namespace, 'operation': self.operation, 'params': self.params, 'depends': self.depends } serialization = json.dumps(output_dict) return to_utf8(serialization)
[docs] @classmethod def deserialize(cls, input, name=None, workspec=None): """Create a new WorkElement object from a serialized representation. Arguments: input: a serialized WorkElement name: new element name (optional) (deprecated) workspec: an existing workspec to attach this element to (optional) When subclasses become distinct, this factory function will need to do additional dispatching to create an object of the correct type. Alternatively, instead of subclassing, a slightly heavier single class may suffice, or more flexible duck typing might be better. """ import json input_string = to_string(input) args = json.loads(input_string) element = cls(namespace=args['namespace'], operation=args['operation'], params=args['params'], depends=args['depends']) if name is not None: = name # This conditional is nested because we can only add named elements to a WorkSpec. if workspec is not None: element.workspec = workspec if not in workspec.elements: workspec.add_element(element) return element
class SharedDataElement(WorkElement): """Work element with MD-specific extensions. The schema may not need to be changed, but the API object may be expected to provide additional functionality. """ def __init__(self, params, name=None): """Create a blank SharedDataElement representation. It may be appropriate to insist on creating objects of this type via helpers or factories, particularly if creation requires additional parameters. """ self.args = params['args'] self.kwargs = params['kwargs'] super(SharedDataElement, self).__init__(namespace="gmxapi", operation="global_data", params={'args': self.args, 'kwargs': self.kwargs}) = name
[docs]def get_source_elements(workspec): """Get an iterator of the starting nodes in the work spec. Source elements have no dependencies and can be processed immediately. Elements with dependencies cannot be processed, instantiated, or added to a work spec until after their dependencies have been. Args: workspec : an existing work specification to analyze, such as by a Context implementation preparing to schedule work. Returns: iterator of gmx.workflow.WorkElement objects that may be processed without dependencies. This function is provided in the API to allow flexibility in how source elements are determined. """ for name in workspec.elements: element_data = workspec.elements[name] element = WorkElement.deserialize(element_data) if len(element.depends) == 0: = name element.workspec = workspec yield(element)
[docs]def from_tpr(input=None, **kwargs): """Create a WorkSpec from a (list of) tpr file(s). Generates a work specification based on the provided simulation input and returns a handle to the MD simulation element of the workflow. Key word arguments can override simulation behavior from ``input``. If the MD operation discovers artifacts from a previous simulation that was launched from the same input, the simulation resumes from the last checkpointed step. If ``append_output`` is set ``False``, existing artifacts are kept separate from new output with the standard file naming convention, and new output begins from the last checkpointed step, if any. Setting ``end_time`` redefines the end point of the simulation trajectory from what was provided in ``input``. It is equivalent to changing the number of steps requested in the MDP (or TPR) input, but it time is provided as picoseconds instead of a number of time steps. .. deprecated:: 0.0.7 If ``steps=N`` is provided and N is an integer greater than or equal to 1, the MD operation advances the trajectory by ``N`` steps, regardless of the number of simulation steps specified in ``input`` or ``end_time``. For convenience, setting ``steps=None`` does not override ``input``. Note that when it is not ``None``, ``steps`` takes precedence over ``end_time`` and ``input``, but can still be superceded by a signal, such as if an MD plugin or other code has a simulation completion condition that occurs before ``N`` additional steps have run. Where key word arguments correspond to ``gmx mdrun`` command line options, the corresponding flags are noted below. Keyword Arguments: input (str): *Required* string or list of strings giving the filename(s) of simulation input append_output (bool): Append output for continuous trajectories if True, truncate existing output data if False. (default True) end_time (float): Specify the final time in the simulation trajectory, overriding input read from TPR. grid (tuple): Domain decomposition grid divisions (nx, ny, nz). (-dd) max_hours (float): Terminate after 0.99 times this many hours if simulation is still running. (-maxh) pme_ranks (int): number of separate ranks to be used for PME electrostatics. (-npme) pme_threads_per_rank (int): Number of OpenMP threads per PME rank. (-ntomp_pme) steps (int): Override input files and run for this many steps. (-nsteps; deprecated) threads (int): Total number of threads to start. (-nt) threads_per_rank (int): number of OpenMP threads to start per MPI rank. (-ntomp) tmpi (int): number of thread-MPI ranks to start. (-ntmpi) Returns: simulation member of a gmx.workflow.WorkSpec object Produces a WorkSpec with the following data:: version: gmxapi_workspec_0_1 elements: tpr_input: namespace: gromacs operation: load_tpr params: {'input': ['tpr_filename1', 'tpr_filename2', ...]} md_sim: namespace: gmxapi operation: md depends: ['tpr_input'] params: {'kw1': arg1, 'kw2': arg2, ...} Bugs: version 0.0.6 * There is not a way to programatically check the current step number on disk. See and """ import os usage = "argument to from_tpr() should be a valid filename or list of filenames, followed by optional key word arguments." # Normalize to tuple input type. if isinstance(input, list) or isinstance(input, tuple): tpr_list = tuple([to_string(element) for element in input]) else: try: tpr_list = (to_string(input),) except: raise exceptions.UsageError(usage) # Check for valid filenames for arg in tpr_list: if not (os.path.exists(arg) and os.path.isfile(arg)): arg_path = os.path.abspath(arg) raise exceptions.UsageError(usage + " Got {}".format(arg_path)) # Note: These are runner parameters, not MD parameters, and should be in the call to instead of here. # Reference params = {} for arg_key in kwargs: if arg_key == 'grid' or arg_key == 'dd': params['grid'] = tuple(kwargs[arg_key]) elif arg_key == 'pme_ranks' or arg_key == 'npme': params['pme_ranks'] = int(kwargs[arg_key]) elif arg_key == 'threads' or arg_key == 'nt': params['threads'] = int(kwargs[arg_key]) elif arg_key == 'tmpi' or arg_key == 'ntmpi': params['tmpi'] = int(kwargs[arg_key]) elif arg_key == 'threads_per_rank' or arg_key == 'ntomp': params['threads_per_rank'] = int(kwargs[arg_key]) elif arg_key == 'pme_threads_per_rank' or arg_key == 'ntomp_pme': params['pme_threads_per_rank'] = int(kwargs[arg_key]) elif arg_key == 'steps' or arg_key == 'nsteps': if kwargs[arg_key] is None: # None means "don't override the input" which is indicated by a parameter value of -2 in GROMACS 2019 steps = -2 else: # Otherwise we require steps to be a positive integer try: steps = int(kwargs[arg_key]) if steps < 1: raise exceptions.ValueError('steps to run must be at least 1') except (TypeError, ValueError) as e: # steps is not an integer. raise exceptions.TypeError('"steps" could not be interpreted as an integer.') # The "nsteps" command line flag will be removed in GROMACS 2020 # and so "steps" is deprecated in gmxapi 0.0.7 warnings.warn("`steps` keyword argument is deprecated. Consider `end_time` instead.", DeprecationWarning) params['steps'] = steps elif arg_key == 'max_hours' or arg_key == 'maxh': params['max_hours'] = float(kwargs[arg_key]) elif arg_key == 'append_output': # Try not to encourage confusion with the `mdrun` `-noappend` flag, which would be a confusing double negative if represented as a bool. params['append_output'] = bool(kwargs[arg_key]) elif arg_key == 'end_time': params[arg_key] = float(kwargs[arg_key]) else: raise exceptions.UsageError("Invalid key word argument: {}. {}".format(arg_key, usage)) # Create an empty WorkSpec workspec = WorkSpec() # Create and add the Element for the tpr file(s) inputelement = WorkElement(namespace='gromacs', operation='load_tpr', params={'input': tpr_list}) = 'tpr_input' if not in workspec.elements: # Operations such as this need to be replaced with accessors or properties that can check the validity of the WorkSpec workspec.elements[] = inputelement.serialize() inputelement.workspec = workspec # Create and add the simulation element # We can add smarter handling of the `depends` argument, but it is only critical to check when adding the element # to a WorkSpec. mdelement = WorkElement(operation='md', depends=[], params=params) = 'md_sim' # Check that the element has not already been added, but that its dependency has. workspec.add_element(mdelement) return mdelement