From 481e36a3aa8767c4b895eabfd7ef8b89132ab611 Mon Sep 17 00:00:00 2001 From: Egor Marin Date: Fri, 16 Aug 2024 22:12:31 +0300 Subject: [PATCH] [GSoC] Parallelisation of AnalysisBase with multiprocessing and dask (#4162) * add parallelisation to AnalysisBase * fixes #4158 DETAILED COMMENTS FROM COMMITS * Remove _scheduler attribute and make dask-based tests run properly * Refactor scheduler usage * Add multiple workers in dask for testing * Refactor _setup_bslices and add processes to dask scheduler kwargs * Create frame_indices and trajectory for each bslice during _setup_bslices * Use explicit initialisation of timeseries wiith zeros * Add non-trivial _parallel_conclude function * Fix tests for new dask fixture * Add type-matching _parallel_conclude * Add fixtures to test combinations of dask and multiprocessing * dask and multiprocessing works in test_atomicdistances.py * Fix bug in results is np.ndarray codepath * Add _setup_scheduler raising NotImplemented error in align.py::AverageStructure * dask and multiprocessing schedulers to test_align.py * dask scheduler for test_contacts.py and test for incompatibility with multiprocessing * dask and multiprocessing scheduler for test_density.py * Add _parallel_conclude implementation for dielectric * dask and multiprocessing schedulers for test_dielectric.py * dask and multiprocessing schedulers for test_diffusionmap.py * Add NotImplementedError for parallel schedulers in dihedrals.py * only current scheduler for test_dihedrals.py * dask and multiprocessing tests for test_encore.py -- but some fail because of RMSF module * Add NotImplementedError for _setup_scheduler in gnm.py * Add NotImplementedError for _setup_scheduler in helix_analysis.py * current process scheduler for test_helix_analysis.py * dask and multiprocessing schedulers for test_hole2.py * Add NotImplementedError in for not-None schedulers * current process scheduler and test for failing non-current ones in test_hydrogendbonds_analysis.py * current process only scheduler and failing test for others in test_lineardensity.py * Add NotImplementedError for non-current process schedulers * current process scheduler only and failing tests for non-current ones in test_msd.py * Add NotImplementedError for non-current process schedulers * Fix scope of fixtures * Add NotImplemented error for all non-current process schedulers * only current process scheduler and failing tests for test_nucleicacids.py * dask and multiprocessing schedulers for test_persistentlength.py * Add _parallel_conclude implementation * dask and multiprocessing schedulers for test_psa.py * Add _parallel_conclude implementation for RDF and RDF_S * dask and multiprocessing schedulers for test_rdf_s.py * dask and multiprocessing schedulers for test_rdf.py * Add NotImplementedError for RMSD and RMSF classes * only local process scheduler and failing tests for others for test_rms.py * current process scheduler only and failing test for others for test_wbridge.py * Add NotImplementedError in _setup_scheduler * Add more clear message during exception * Add timeseries aggregation function * dask and multiprocessing scheduler for most of the test_base.py testcases * dask and multiprocessing schedulers for test_rms.py::TestRMSD * Add NotImplementedError for pca and rms * dask and multiprocessing schedulers for test_bat * dcurrent process scheduler for test_gnm.py * dcurrent process scheduler for test_pca.py * Fix rmsf-related scheduler usage to only current process scheduler * remove fixme marks * Switch to enumerate in _compute main loop and fix code review comments * Add dask to CI setup actions * Remove local scheduler for progressbar test * Add installation with dask as asetup option * fix hole2 tests for -- implement only current scheduler and add failing test * fix progressbar test by changing order of ProgressBar and enumerate * use only frame indices and frames in _setup_bslices after writing a blogpost * Refactor _setup_bslices: move enumerate to numpy and fuse logic in defining type of input * Add documentation to AnalysisBase._parallel_conclude() * add functional-like interface draft * Implement proper Client class, separating computations from AnalysisBase * FINALLY implement working one-time dask cluster setup in kwargs of a client * Correct tests accordingly * Separately process case of only one remote worker * Add available_schedulers to AverageStructure * Use automatic fixture for AverageStructure * Add fixture for AverageStructure * Add fixture for AtomicDistances * Change default available_backends to all implemented in Client * Limit available backends for AverageStructure * Add fixture for BAT * Add fixture tests to Contacts * Fix n_workers check and boolean frames handling * Fix performance of backend="dask" * Add available_backends for Contacts * Remove _setup_scheduler * Use client fixture for Contacts * Use client fixture for RMSD/RMSF * Revert files to their state in develop * Delete files_for_undoing_changes.txt * Delete conftest.py * Delete parallel_analysis_demo.ipynb * Clean up notebook * Limit available schedulers in RMSF * Split test in two due to failing with "expectation" parametrization * Add fixture generator and fixtures for test_base and test_rms * Add dask to pyproject.toml * Return computation groups explicitly * Fix dask position in setup-deps/action.yaml * Add dask[distributed] to mdanalysis[parallel] installation * Undo autoformatter * Manually define available_backends for RMSD class * Create separate "parallel" entry * Add is_installed function to utils * Add dict-based validatdion and computation logic for ParallelExecutor * Add tests for ParallelExecutor * Add documentation for "apply" method of ParallelExecutor * Correct dask.distributed name * Use chunksize=1 instead of explicit Pool in _compute_with_dask * Remove unnecessary function in conftest * Fix function to retrieve dask client if dask is not installed * Fix base tests when dask is not installed * Use new LocalCluster every time * Fix client/backend logic * Add documentation to a silly square function * Switch to package-wise autouse fixture for dask.distributed.Client * Add explicit result() when computing with cluster * Fix codereview * Replace list with tuple in available_backends for RMSD * Remove unnecessary get_running_dask_client * Implement fixture injection for subclasses testing * Add warnings filters * Fix backend check when client is present * Return get_runnning_dask_client function * Change dask fixture scope * Close LocalCluster to avoid trillions of logs * Implement ResultsGroup based aggregation instead of type matching * Add non-default _get_aggregator() to RMS and Base classes * Mark test_multiprocessing.py::test_creating_multiple_universe_without_offset as skipped * Restore failing test * Make aggregation functions static methods of ResultsGroup * Remove test skip * Move parallel part into a separate file * Fix imports * Proof of concept for duck-typed backends * Remove unused code * Replace ParallelExecutor with multiple backend classes and add duck-typing backend in AnalysisBase.run() * Add all tests for analysis/parallel.py and fix bug in ResultsGroup.ndarray_mean * Change typing to py3.9 compatible syntax * Add _is_parallelizable to AnalysisFromFunction * Remove dask[distributed] even as an optional dependency * Update documentation * Remove function to get running dask client * Remove unused code from analysis/conftest.py * Fix documentation and minor issues from codereview * Update package/MDAnalysis/analysis/rms.py Co-authored-by: Irfan Alibay * Add more backend validation tests and fix autoformatter issues * Start implementing correct result sizes in separate computation groups * Continue working: diffusionmap and PCA tests fail * Fix bug in PCA trajectory iteration -- avoid explicit usage of self.start * update changelog and tests for PCA fix * Fix diffusionmap and pca * Make sure not to reset self.{start,stop,step} during self._compute * Change iteration pattern to sliced trajectory * Change iteration pattern to sliced trajectory * Update package/MDAnalysis/analysis/parallel.py Co-authored-by: Yuxuan Zhuang * Apply suggestions from code review Co-authored-by: Rocco Meli Co-authored-by: Yuxuan Zhuang * Split _setup_frames into two separate functions * Add docstrings for _prepare_sliced_trajectory and _define_run_frames * Remove dask-distributed from dependencies * Test only 2 processors with parallelizable backends * Rename available_backends and safe * Apply codereview changes * Make tests for AnalysisBase subclasses explicit * Exclude "multiprocessing" from analysis_class function available backends * Split parallel.py into results.py and parallel.py * Finalize separation of results and backends * Rename parallel.py to backends.py * Add results and backends to analysis/__init__.py * Fix pep8 errors in docstrings and code * Add versionadded to documentation * Update sphinx documentation with backends and results * Add parallelization reference to base.py * Switch to relative imports * Update documentation, adding introduced changes * Update documentation adding parallelization support for rms * Add module documentation to results and backends * Fix BackendSerial validation and add its tests * Fix calling of self._is_paralellizable() * Add tests on is_parallelizable and get_supported_backends * Fix bug with default progressbar_kwargs being dict * Apply suggestions from code review Co-authored-by: Rocco Meli Co-authored-by: Yuxuan Zhuang * Add docstrings to apply() in backends * Add double n_worker check * Apply suggestions from code review Co-authored-by: Paul Smith * Fix hasattr in double n_worker check * Revert test `with expectation` in test_align * Update testsuite/MDAnalysisTests/analysis/test_pca.py Co-authored-by: Irfan Alibay * Update package/MDAnalysis/lib/util.py Co-authored-by: Irfan Alibay * Update changelog * Apply suggestions from code review * Add parallelization section to the documentation * Fix versionadded in new classes * Finish parallelization section for documentation * Fix typos * Apply suggestions from code review Co-authored-by: Rocco Meli * Apply suggestions from code review Co-authored-by: Rocco Meli * Refactor TreadsBackend example and add a warning * Add n_workers instantiation from backend argument * Update package/MDAnalysis/analysis/backends.py Co-authored-by: Yuxuan Zhuang * Update package/doc/sphinx/source/documentation_pages/analysis/parallelization.rst Co-authored-by: Yuxuan Zhuang * Add remark about RMSF parallelization * Apply suggestions from codereview * Apply suggestions from code review * Fix documentation typo * Update dask installation test after exception text changed * edited documentation for parallelization - add reST/sphinx markup for methods and classes and ensure that (most of them) resolve; add intersphinx mapping to dask docs - added cross referencing between parallelization and backends docs - restructured analysis landing page with additional numbered headings for general use and parallelization - add citation for PMDA - fixed links - edited text for flow and readability - added SeeAlsos (eg for User Guide) - added notes/warnings * analysis top level docs fixes - mark analysis docs as documenting MDAnalysis.analysis so that references resolve properly - link fixes * Added comments regarding `_is_parallelizable` (and fixed documentation), fixed tests for `is_installed` * Rename AnalysisBase.parallelizable and fix parallelizable transformations * Remove explicit parallelizable=True in NoJump test call * Apply suggestions from code review * add explicit comment to AnalysisBase._analysis_algorithm_is_parallelizable * Add client_RMSD explanation * versioninformation markup fix in base.py * Apply suggestions from code review Co-authored-by: Irfan Alibay Co-authored-by: Rocco Meli * Apply suggestions from code review Co-authored-by: Irfan Alibay Co-authored-by: Rocco Meli * Add comments explaining client_... fixtures * Move class properties to the top of the class * Undo accidental versionadded change * Remove duplicating versionadded * Add versionadded for backend * Add link to github profile * Update package/doc/sphinx/source/documentation_pages/analysis/parallelization.rst Co-authored-by: Irfan Alibay * Update testsuite/MDAnalysisTests/analysis/test_backends.py Co-authored-by: Rocco Meli * minor text fixes * Update package/MDAnalysis/analysis/base.py Co-authored-by: Oliver Beckstein * Update package/MDAnalysis/analysis/base.py Co-authored-by: Oliver Beckstein * Remove issubclass check --------- Co-authored-by: Egor Marin Co-authored-by: Egor Marin Co-authored-by: Irfan Alibay Co-authored-by: Yuxuan Zhuang Co-authored-by: Rocco Meli Co-authored-by: Paul Smith Co-authored-by: Yuxuan Zhuang Co-authored-by: Oliver Beckstein --- .github/actions/setup-deps/action.yaml | 3 + package/CHANGELOG | 2 + package/MDAnalysis/analysis/__init__.py | 2 + package/MDAnalysis/analysis/backends.py | 333 ++++++++ package/MDAnalysis/analysis/base.py | 726 ++++++++++++++---- package/MDAnalysis/analysis/results.py | 321 ++++++++ package/MDAnalysis/analysis/rms.py | 27 +- package/MDAnalysis/lib/util.py | 22 + package/doc/sphinx/source/conf.py | 1 + .../documentation_pages/analysis/backends.rst | 4 + .../documentation_pages/analysis/base.rst | 2 +- .../analysis/parallelization.rst | 358 +++++++++ .../documentation_pages/analysis/results.rst | 4 + .../documentation_pages/analysis_modules.rst | 117 ++- .../source/images/AnalysisBase_parallel.png | Bin 0 -> 274539 bytes .../source/images/parallelization_time.png | Bin 0 -> 914392 bytes package/doc/sphinx/source/references.bib | 12 + package/pyproject.toml | 3 + .../MDAnalysisTests/analysis/conftest.py | 89 +++ .../MDAnalysisTests/analysis/test_backends.py | 72 ++ .../MDAnalysisTests/analysis/test_base.py | 338 ++++---- .../MDAnalysisTests/analysis/test_pca.py | 6 + .../MDAnalysisTests/analysis/test_results.py | 173 +++++ .../MDAnalysisTests/analysis/test_rms.py | 112 ++- testsuite/MDAnalysisTests/lib/test_util.py | 8 + 25 files changed, 2373 insertions(+), 362 deletions(-) create mode 100644 package/MDAnalysis/analysis/backends.py create mode 100644 package/MDAnalysis/analysis/results.py create mode 100644 package/doc/sphinx/source/documentation_pages/analysis/backends.rst create mode 100644 package/doc/sphinx/source/documentation_pages/analysis/parallelization.rst create mode 100644 package/doc/sphinx/source/documentation_pages/analysis/results.rst create mode 100644 package/doc/sphinx/source/images/AnalysisBase_parallel.png create mode 100644 package/doc/sphinx/source/images/parallelization_time.png create mode 100644 testsuite/MDAnalysisTests/analysis/conftest.py create mode 100644 testsuite/MDAnalysisTests/analysis/test_backends.py create mode 100644 testsuite/MDAnalysisTests/analysis/test_results.py diff --git a/.github/actions/setup-deps/action.yaml b/.github/actions/setup-deps/action.yaml index e5a0794450c..cceae40f99c 100644 --- a/.github/actions/setup-deps/action.yaml +++ b/.github/actions/setup-deps/action.yaml @@ -62,6 +62,8 @@ inputs: default: 'chemfiles-python>=0.9' clustalw: default: 'clustalw=2.1' + dask: + default: 'dask' distopia: default: 'distopia>=0.2.0' h5py: @@ -134,6 +136,7 @@ runs: ${{ inputs.biopython }} ${{ inputs.chemfiles-python }} ${{ inputs.clustalw }} + ${{ inputs.dask }} ${{ inputs.distopia }} ${{ inputs.gsd }} ${{ inputs.h5py }} diff --git a/package/CHANGELOG b/package/CHANGELOG index 750ddcf1cba..056282c6214 100644 --- a/package/CHANGELOG +++ b/package/CHANGELOG @@ -52,6 +52,8 @@ Fixes * Fix groups.py doctests using sphinx directives (Issue #3925, PR #4374) Enhancements + * Introduce parallelization API to `AnalysisBase` and to `analysis.rms.RMSD` class + (Issue #4158, PR #4304) * Improve error message for `AtomGroup.unwrap()` when bonds are not present.(Issue #4436, PR #4642) * Add `analysis.DSSP` module for protein secondary structure assignment, based on [pydssp](https://github.com/ShintaroMinami/PyDSSP) * Added a tqdm progress bar for `MDAnalysis.analysis.pca.PCA.transform()` diff --git a/package/MDAnalysis/analysis/__init__.py b/package/MDAnalysis/analysis/__init__.py index 5983bb8b61c..25450b759b9 100644 --- a/package/MDAnalysis/analysis/__init__.py +++ b/package/MDAnalysis/analysis/__init__.py @@ -23,6 +23,7 @@ __all__ = [ 'align', + 'backends', 'base', 'contacts', 'density', @@ -45,6 +46,7 @@ 'pca', 'psa', 'rdf', + 'results', 'rms', 'waterdynamics', ] diff --git a/package/MDAnalysis/analysis/backends.py b/package/MDAnalysis/analysis/backends.py new file mode 100644 index 00000000000..38917cb2ae7 --- /dev/null +++ b/package/MDAnalysis/analysis/backends.py @@ -0,0 +1,333 @@ +"""Analysis backends --- :mod:`MDAnalysis.analysis.backends` +============================================================ + +.. versionadded:: 2.8.0 + + +The :mod:`backends` module provides :class:`BackendBase` base class to +implement custom execution backends for +:meth:`MDAnalysis.analysis.base.AnalysisBase.run` and its +subclasses. + +.. SeeAlso:: :ref:`parallel-analysis` + +.. _backends: + +Backends +-------- + +Three built-in backend classes are provided: + +* *serial*: :class:`BackendSerial`, that is equivalent to using no + parallelization and is the default + +* *multiprocessing*: :class:`BackendMultiprocessing` that supports + parallelization via standard Python :mod:`multiprocessing` module + and uses default :mod:`pickle` serialization + +* *dask*: :class:`BackendDask`, that uses the same process-based + parallelization as :class:`BackendMultiprocessing`, but different + serialization algorithm via `dask `_ (see `dask + serialization algorithms + `_ for details) + +Classes +------- + +""" +import warnings +from typing import Callable +from MDAnalysis.lib.util import is_installed + + +class BackendBase: + """Base class for backend implementation. + + Initializes an instance and performs checks for its validity, such as + ``n_workers`` and possibly other ones. + + Parameters + ---------- + n_workers : int + number of workers (usually, processes) over which the work is split + + Examples + -------- + .. code-block:: python + + from MDAnalysis.analysis.backends import BackendBase + + class ThreadsBackend(BackendBase): + def apply(self, func, computations): + from multiprocessing.dummy import Pool + + with Pool(processes=self.n_workers) as pool: + results = pool.map(func, computations) + return results + + import MDAnalysis as mda + from MDAnalysis.tests.datafiles import PSF, DCD + from MDAnalysis.analysis.rms import RMSD + + u = mda.Universe(PSF, DCD) + ref = mda.Universe(PSF, DCD) + + R = RMSD(u, ref) + + n_workers = 2 + backend = ThreadsBackend(n_workers=n_workers) + R.run(backend=backend, unsupported_backend=True) + + .. warning:: + Using `ThreadsBackend` above will lead to erroneous results, since it + is an educational example. Do not use it for real analysis. + + + .. versionadded:: 2.8.0 + + """ + + def __init__(self, n_workers: int): + self.n_workers = n_workers + self._validate() + + def _get_checks(self): + """Get dictionary with ``condition: error_message`` pairs that ensure the + validity of the backend instance + + Returns + ------- + dict + dictionary with ``condition: error_message`` pairs that will get + checked during ``_validate()`` run + """ + return { + isinstance(self.n_workers, int) and self.n_workers > 0: + f"n_workers should be positive integer, got {self.n_workers=}", + } + + def _get_warnings(self): + """Get dictionary with ``condition: warning_message`` pairs that ensure + the good usage of the backend instance + + Returns + ------- + dict + dictionary with ``condition: warning_message`` pairs that will get + checked during ``_validate()`` run + """ + return dict() + + def _validate(self): + """Check correctness (e.g. ``dask`` is installed if using ``backend='dask'``) + and good usage (e.g. ``n_workers=1`` if backend is serial) of the backend + + Raises + ------ + ValueError + if one of the conditions in :meth:`_get_checks` is ``True`` + """ + for check, msg in self._get_checks().items(): + if not check: + raise ValueError(msg) + for check, msg in self._get_warnings().items(): + if not check: + warnings.warn(msg) + + def apply(self, func: Callable, computations: list) -> list: + """map function `func` to all tasks in the `computations` list + + Main method that will get called when using an instance of + ``BackendBase``. It is equivalent to running ``[func(item) for item in + computations]`` while using the parallel backend capabilities. + + Parameters + ---------- + func : Callable + function to be called on each of the tasks in computations list + computations : list + computation tasks to apply function to + + Returns + ------- + list + list of results of the function + + """ + raise NotImplementedError + + +class BackendSerial(BackendBase): + """A built-in backend that does serial execution of the function, without any + parallelization. + + Parameters + ---------- + n_workers : int + Is ignored in this class, and if ``n_workers`` > 1, a warning will be + given. + + + .. versionadded:: 2.8.0 + """ + + def _get_warnings(self): + """Get dictionary with ``condition: warning_message`` pairs that ensure + the good usage of the backend instance. Here, it checks if the number + of workers is not 1, otherwise gives warning. + + Returns + ------- + dict + dictionary with ``condition: warning_message`` pairs that will get + checked during ``_validate()`` run + """ + return { + self.n_workers == 1: + "n_workers is ignored when executing with backend='serial'" + } + + def apply(self, func: Callable, computations: list) -> list: + """ + Serially applies `func` to each task object in ``computations``. + + Parameters + ---------- + func : Callable + function to be called on each of the tasks in computations list + computations : list + computation tasks to apply function to + + Returns + ------- + list + list of results of the function + """ + return [func(task) for task in computations] + + +class BackendMultiprocessing(BackendBase): + """A built-in backend that executes a given function using the + :meth:`multiprocessing.Pool.map ` method. + + Parameters + ---------- + n_workers : int + number of processes in :class:`multiprocessing.Pool + ` to distribute the workload + between. Must be a positive integer. + + Examples + -------- + + .. code-block:: python + + from MDAnalysis.analysis.backends import BackendMultiprocessing + import multiprocessing as mp + + backend_obj = BackendMultiprocessing(n_workers=mp.cpu_count()) + + + .. versionadded:: 2.8.0 + + """ + + def apply(self, func: Callable, computations: list) -> list: + """Applies `func` to each object in ``computations`` using `multiprocessing`'s `Pool.map`. + + Parameters + ---------- + func : Callable + function to be called on each of the tasks in computations list + computations : list + computation tasks to apply function to + + Returns + ------- + list + list of results of the function + """ + from multiprocessing import Pool + + with Pool(processes=self.n_workers) as pool: + results = pool.map(func, computations) + return results + + +class BackendDask(BackendBase): + """A built-in backend that executes a given function with *dask*. + + Execution is performed with the :func:`dask.compute` function of + :class:`dask.delayed.Delayed` object (created with + :func:`dask.delayed.delayed`) with ``scheduler='processes'`` and + ``chunksize=1`` (this ensures uniform distribution of tasks among + processes). Requires the `dask package `_ + to be `installed `_. + + Parameters + ---------- + n_workers : int + number of processes in to distribute the workload + between. Must be a positive integer. Workers are actually + :class:`multiprocessing.pool.Pool` processes, but they use a different and + more flexible `serialization protocol + `_. + + Examples + -------- + + .. code-block:: python + + from MDAnalysis.analysis.backends import BackendDask + import multiprocessing as mp + + backend_obj = BackendDask(n_workers=mp.cpu_count()) + + + .. versionadded:: 2.8.0 + + """ + + def apply(self, func: Callable, computations: list) -> list: + """Applies `func` to each object in ``computations``. + + Parameters + ---------- + func : Callable + function to be called on each of the tasks in computations list + computations : list + computation tasks to apply function to + + Returns + ------- + list + list of results of the function + """ + from dask.delayed import delayed + import dask + + computations = [delayed(func)(task) for task in computations] + results = dask.compute(computations, + scheduler="processes", + chunksize=1, + num_workers=self.n_workers)[0] + return results + + def _get_checks(self): + """Get dictionary with ``condition: error_message`` pairs that ensure the + validity of the backend instance. Here checks if ``dask`` module is + installed in the environment. + + Returns + ------- + dict + dictionary with ``condition: error_message`` pairs that will get + checked during ``_validate()`` run + """ + base_checks = super()._get_checks() + checks = { + is_installed("dask"): + ("module 'dask' is missing. Please install 'dask': " + "https://docs.dask.org/en/stable/install.html") + } + return base_checks | checks diff --git a/package/MDAnalysis/analysis/base.py b/package/MDAnalysis/analysis/base.py index 04a0d25b506..47a7eccd137 100644 --- a/package/MDAnalysis/analysis/base.py +++ b/package/MDAnalysis/analysis/base.py @@ -103,16 +103,42 @@ contains a step-by-step example for writing analysis tools with :class:`AnalysisBase`. - .. _`Writing your own trajectory analysis`: https://userguide.mdanalysis.org/stable/examples/analysis/custom_trajectory_analysis.html +If your analysis is operating independently on each frame, you might consider +making it **parallelizable** via adding a :meth:`get_supported_backends` method, +and appropriate aggregation function for each of its results. For example, if +you have your :meth:`_single_frame` method storing important values under +:attr:`self.results.timeseries`, you will write: + +.. code-block:: python + + class MyAnalysis(AnalysisBase): + _analysis_algorithm_is_parallelizable = True + + @classmethod + def get_supported_backends(cls): + return ('serial', 'multiprocessing', 'dask',) + + + def _get_aggregator(self): + return ResultsGroup(lookup={'timeseries': ResultsGroup.ndarray_vstack}) + +See :mod:`MDAnalysis.analysis.results` for more on aggregating results. + +.. SeeAlso:: + + :ref:`parallel-analysis` + + + Classes ------- -The :class:`Results` and :class:`AnalysisBase` classes are the essential -building blocks for almost all MDAnalysis tools in the +The :class:`MDAnalysis.results.Results` and :class:`AnalysisBase` classes +are the essential building blocks for almost all MDAnalysis tools in the :mod:`MDAnalysis.analysis` module. They aim to be easily useable and extendable. @@ -121,103 +147,22 @@ tools if only the single-frame analysis function needs to be written. """ -from collections import UserDict import inspect -import logging import itertools +import logging +import warnings +from functools import partial +from typing import Iterable, Union import numpy as np -from MDAnalysis import coordinates -from MDAnalysis.core.groups import AtomGroup -from MDAnalysis.lib.log import ProgressBar - -logger = logging.getLogger(__name__) - - -class Results(UserDict): - r"""Container object for storing results. - - :class:`Results` are dictionaries that provide two ways by which values - can be accessed: by dictionary key ``results["value_key"]`` or by object - attribute, ``results.value_key``. :class:`Results` stores all results - obtained from an analysis after calling :meth:`~AnalysisBase.run()`. - - The implementation is similar to the :class:`sklearn.utils.Bunch` - class in `scikit-learn`_. - - .. _`scikit-learn`: https://scikit-learn.org/ - - Raises - ------ - AttributeError - If an assigned attribute has the same name as a default attribute. - - ValueError - If a key is not of type ``str`` and therefore is not able to be - accessed by attribute. - - Examples - -------- - >>> from MDAnalysis.analysis.base import Results - >>> results = Results(a=1, b=2) - >>> results['b'] - 2 - >>> results.b - 2 - >>> results.a = 3 - >>> results['a'] - 3 - >>> results.c = [1, 2, 3, 4] - >>> results['c'] - [1, 2, 3, 4] - - - .. versionadded:: 2.0.0 - """ - - def _validate_key(self, key): - if key in dir(self): - raise AttributeError(f"'{key}' is a protected dictionary " - "attribute") - elif isinstance(key, str) and not key.isidentifier(): - raise ValueError(f"'{key}' is not a valid attribute") - - def __init__(self, *args, **kwargs): - kwargs = dict(*args, **kwargs) - if "data" in kwargs.keys(): - raise AttributeError(f"'data' is a protected dictionary attribute") - self.__dict__["data"] = {} - self.update(kwargs) - - def __setitem__(self, key, item): - self._validate_key(key) - super().__setitem__(key, item) - - def __setattr__(self, attr, val): - if attr == 'data': - super().__setattr__(attr, val) - else: - self.__setitem__(attr, val) - - def __getattr__(self, attr): - try: - return self[attr] - except KeyError as err: - raise AttributeError("'Results' object has no " - f"attribute '{attr}'") from err - - def __delattr__(self, attr): - try: - del self[attr] - except KeyError as err: - raise AttributeError("'Results' object has no " - f"attribute '{attr}'") from err +from .. import coordinates +from ..core.groups import AtomGroup +from ..lib.log import ProgressBar - def __getstate__(self): - return self.data +from .backends import BackendDask, BackendMultiprocessing, BackendSerial, BackendBase +from .results import Results, ResultsGroup - def __setstate__(self, state): - self.data = state +logger = logging.getLogger(__name__) class AnalysisBase(object): @@ -231,8 +176,8 @@ class AnalysisBase(object): To define a new Analysis, :class:`AnalysisBase` needs to be subclassed and :meth:`_single_frame` must be defined. It is also possible to define :meth:`_prepare` and :meth:`_conclude` for pre- and post-processing. - All results should be stored as attributes of the :class:`Results` - container. + All results should be stored as attributes of the + :class:`MDAnalysis.analysis.results.Results` container. Parameters ---------- @@ -310,15 +255,140 @@ def _conclude(self): .. versionchanged:: 2.0.0 Added :attr:`results` + .. versionchanged:: 2.8.0 + Added ability to run analysis in parallel using either a + built-in backend (`multiprocessing` or `dask`) or a custom + `backends.BackendBase` instance with an implemented `apply` method + that is used to run the computations. """ + @classmethod + def get_supported_backends(cls): + """Tuple with backends supported by the core library for a given class. + User can pass either one of these values as ``backend=...`` to + :meth:`run()` method, or a custom object that has ``apply`` method + (see documentation for :meth:`run()`): + + - 'serial': no parallelization + - 'multiprocessing': parallelization using `multiprocessing.Pool` + - 'dask': parallelization using `dask.delayed.compute()`. Requires + installation of `mdanalysis[dask]` + + If you want to add your own backend to an existing class, pass a + :class:`backends.BackendBase` subclass (see its documentation to learn + how to implement it properly), and specify ``unsupported_backend=True``. + + Returns + ------- + tuple + names of built-in backends that can be used in :meth:`run(backend=...)` + + + .. versionadded:: 2.8.0 + """ + return ("serial",) + + # class authors: override _analysis_algorithm_is_parallelizable + # in derived classes and only set to True if you have confirmed + # that your algorithm works reliably when parallelized with + # the split-apply-combine approach (see docs) + _analysis_algorithm_is_parallelizable = False + + @property + def parallelizable(self): + """Boolean mark showing that a given class can be parallelizable with + split-apply-combine procedure. Namely, if we can safely distribute + :meth:`_single_frame` to multiple workers and then combine them with a + proper :meth:`_conclude` call. If set to ``False``, no backends except + for ``serial`` are supported. + + .. note:: If you want to check parallelizability of the whole class, without + explicitly creating an instance of the class, see + :attr:`_analysis_algorithm_is_parallelizable`. Note that you + setting it to other value will break things if the algorithm + behind the analysis is not trivially parallelizable. + + + Returns + ------- + bool + if a given ``AnalysisBase`` subclass instance + is parallelizable with split-apply-combine, or not + + + .. versionadded:: 2.8.0 + """ + return self._analysis_algorithm_is_parallelizable + def __init__(self, trajectory, verbose=False, **kwargs): self._trajectory = trajectory self._verbose = verbose self.results = Results() - def _setup_frames(self, trajectory, start=None, stop=None, step=None, - frames=None): + def _define_run_frames(self, trajectory, + start=None, stop=None, step=None, frames=None + ) -> Union[slice, np.ndarray]: + """Defines limits for the whole run, as passed by self.run() arguments + + Parameters + ---------- + trajectory : mda.Reader + a trajectory Reader + start : int, optional + start frame of analysis, by default None + stop : int, optional + stop frame of analysis, by default None + step : int, optional + number of frames to skip between each analysed frame, by default None + frames : array_like, optional + array of integers or booleans to slice trajectory; cannot be + combined with ``start``, ``stop``, ``step``; by default None + + Returns + ------- + Union[slice, np.ndarray] + Appropriate slicer for the trajectory that would give correct iteraction + order via trajectory[slicer] + + Raises + ------ + ValueError + if *both* `frames` and at least one of ``start``, ``stop``, or ``step`` + is provided (i.e. set to not ``None`` value). + + + .. versionadded:: 2.8.0 + """ + self._trajectory = trajectory + if frames is not None: + if not all(opt is None for opt in [start, stop, step]): + raise ValueError("start/stop/step cannot be combined with frames") + slicer = frames + else: + start, stop, step = trajectory.check_slice_indices(start, stop, step) + slicer = slice(start, stop, step) + self.start, self.stop, self.step = start, stop, step + return slicer + + def _prepare_sliced_trajectory(self, slicer: Union[slice, np.ndarray]): + """Prepares sliced trajectory for use in subsequent parallel computations: + namely, assigns self._sliced_trajectory and its appropriate attributes, + self.n_frames, self.frames and self.times. + + Parameters + ---------- + slicer : Union[slice, np.ndarray] + appropriate slicer for the trajectory + + + .. versionadded:: 2.8.0 + """ + self._sliced_trajectory = self._trajectory[slicer] + self.n_frames = len(self._sliced_trajectory) + self.frames = np.zeros(self.n_frames, dtype=int) + self.times = np.zeros(self.n_frames) + + def _setup_frames(self, trajectory, start=None, stop=None, step=None, frames=None): """Pass a Reader object and define the desired iteration pattern through the trajectory @@ -334,63 +404,303 @@ def _setup_frames(self, trajectory, start=None, stop=None, step=None, number of frames to skip between each analysed frame frames : array_like, optional array of integers or booleans to slice trajectory; cannot be - combined with `start`, `stop`, `step` + combined with ``start``, ``stop``, ``step`` .. versionadded:: 2.2.0 Raises ------ ValueError - if *both* `frames` and at least one of `start`, `stop`, or `frames` - is provided (i.e., set to another value than ``None``) + if *both* `frames` and at least one of ``start``, ``stop``, or + ``frames`` is provided (i.e., set to another value than ``None``) .. versionchanged:: 1.0.0 Added .frames and .times arrays as attributes - + .. versionchanged:: 2.2.0 Added ability to iterate through trajectory by passing a list of frame indices in the `frames` keyword argument - + + .. versionchanged:: 2.8.0 + Split function into two: :meth:`_define_run_frames` and + :meth:`_prepare_sliced_trajectory`: first one defines the limits + for the whole run and is executed once during :meth:`run` in + :meth:`_setup_frames`, second one prepares sliced trajectory for + each of the workers and gets executed twice: one time in + :meth:`_setup_frames` for the whole trajectory, second time in + :meth:`_compute` for each of the computation groups. """ - self._trajectory = trajectory - if frames is not None: - if not all(opt is None for opt in [start, stop, step]): - raise ValueError("start/stop/step cannot be combined with " - "frames") - slicer = frames - else: - start, stop, step = trajectory.check_slice_indices(start, stop, - step) - slicer = slice(start, stop, step) - self._sliced_trajectory = trajectory[slicer] - self.start = start - self.stop = stop - self.step = step - self.n_frames = len(self._sliced_trajectory) - self.frames = np.zeros(self.n_frames, dtype=int) - self.times = np.zeros(self.n_frames) + slicer = self._define_run_frames(trajectory, start, stop, step, frames) + self._prepare_sliced_trajectory(slicer) def _single_frame(self): """Calculate data from a single frame of trajectory Don't worry about normalising, just deal with a single frame. + Attributes accessible during your calculations: + + - ``self._frame_index``: index of the frame in results array + - ``self._ts`` -- Timestep instance + - ``self._sliced_trajectory`` -- trajectory that you're iterating over + - ``self.results`` -- :class:`MDAnalysis.analysis.results.Results` instance + holding run results initialized in :meth:`_prepare`. """ raise NotImplementedError("Only implemented in child classes") def _prepare(self): - """Set things up before the analysis loop begins""" + """ + Set things up before the analysis loop begins. + + Notes + ----- + ``self.results`` is initialized already in :meth:`self.__init__` with an + empty instance of :class:`MDAnalysis.analysis.results.Results` object. + You can still call your attributes as if they were usual ones, + ``Results`` just keeps track of that to be able to run a proper + aggregation after a parallel run, if necessary. + """ pass # pylint: disable=unnecessary-pass def _conclude(self): """Finalize the results you've gathered. Called at the end of the :meth:`run` method to finish everything up. + + Notes + ----- + Aggregation of results from individual workers happens in + :meth:`self.run()`, so here you have to implement everything as if you + had a non-parallel run. If you want to enable proper aggregation for + parallel runs for you analysis class, implement ``self._get_aggregator`` + and check :mod:`MDAnalysis.analysis.results` for how to use it. """ pass # pylint: disable=unnecessary-pass - def run(self, start=None, stop=None, step=None, frames=None, - verbose=None, *, progressbar_kwargs=None): + def _compute(self, indexed_frames: np.ndarray, + verbose: bool = None, + *, progressbar_kwargs={}) -> "AnalysisBase": + """Perform the calculation on a balanced slice of frames + that have been setup prior to that using _setup_computation_groups() + + Parameters + ---------- + indexed_frames : np.ndarray + np.ndarray of (n, 2) shape, where first column is frame iteration + indices and second is frame numbers + + verbose : bool, optional + Turn on verbosity + + progressbar_kwargs : dict, optional + ProgressBar keywords with custom parameters regarding progress bar + position, etc; see :class:`MDAnalysis.lib.log.ProgressBar` + for full list. + + + .. versionadded:: 2.8.0 + """ + logger.info("Choosing frames to analyze") + # if verbose unchanged, use class default + verbose = getattr(self, "_verbose", False) if verbose is None else verbose + + frames = indexed_frames[:, 1] + + logger.info("Starting preparation") + self._prepare_sliced_trajectory(slicer=frames) + self._prepare() + if len(frames) == 0: # if `frames` were empty in `run` or `stop=0` + return self + + for idx, ts in enumerate(ProgressBar( + self._sliced_trajectory, + verbose=verbose, + **progressbar_kwargs)): + self._frame_index = idx # accessed later by subclasses + self._ts = ts + self.frames[idx] = ts.frame + self.times[idx] = ts.time + self._single_frame() + logger.info("Finishing up") + return self + + def _setup_computation_groups( + self, n_parts: int, + start: int = None, stop: int = None, step: int = None, + frames: Union[slice, np.ndarray] = None + ) -> list[np.ndarray]: + """ + Splits the trajectory frames, defined by ``start/stop/step`` or + ``frames``, into ``n_parts`` even groups, preserving their indices. + + Parameters + ---------- + n_parts : int + number of parts to split the workload into + start : int, optional + start frame + stop : int, optional + stop frame + step : int, optional + step size for analysis (1 means to read every frame) + frames : array_like, optional + array of integers or booleans to slice trajectory; ``frames`` can + only be used *instead* of ``start``, ``stop``, and ``step``. Setting + *both* ``frames`` and at least one of ``start``, ``stop``, ``step`` + to a non-default value will raise a :exc:`ValueError`. + + Raises + ------ + ValueError + if *both* ``frames`` and at least one of ``start``, ``stop``, or + ``frames`` is provided (i.e., set to another value than ``None``) + + Returns + ------- + computation_groups : list[np.ndarray] + list of (n, 2) shaped np.ndarrays with frame indices and numbers + + + .. versionadded:: 2.8.0 + """ + if frames is None: + start, stop, step = self._trajectory.check_slice_indices(start, stop, step) + used_frames = np.arange(start, stop, step) + elif not all(opt is None for opt in [start, stop, step]): + raise ValueError("start/stop/step cannot be combined with frames") + else: + used_frames = frames + + if all(isinstance(obj, bool) for obj in used_frames): + arange = np.arange(len(used_frames)) + used_frames = arange[used_frames] + + # similar to list(enumerate(frames)) + enumerated_frames = np.vstack([np.arange(len(used_frames)), used_frames]).T + + return np.array_split(enumerated_frames, n_parts) + + def _configure_backend( + self, + backend: Union[str, BackendBase], + n_workers: int, + unsupported_backend: bool = False + ) -> BackendBase: + """Matches a passed backend string value with class attributes + :attr:`parallelizable` and :meth:`get_supported_backends()` + to check if downstream calculations can be performed. + + Parameters + ---------- + backend : Union[str, BackendBase] + backend to be used: + - ``str`` is matched to a builtin backend (one of "serial", + "multiprocessing" and "dask") + - ``BackendBase`` subclass is checked for the presence of + an :meth:`apply` method + n_workers : int + positive integer with number of workers (processes, in case of + built-in backends) to split the work between + unsupported_backend : bool, optional + if you want to run your custom backend on a parallelizable class + that has not been tested by developers, by default ``False`` + + Returns + ------- + BackendBase + instance of a ``BackendBase`` class that will be used for computations + + Raises + ------ + ValueError + if :attr:`parallelizable` is set to ``False`` but backend is + not ``serial`` + ValueError + if :attr:`parallelizable` is ``True`` and custom backend instance is used + without specifying ``unsupported_backend=True`` + ValueError + if your trajectory has associated parallelizable transformations + but backend is not serial + ValueError + if ``n_workers`` was specified twice -- in the run() method and durin + ``__init__`` of a custom backend + ValueError + if your backend object instance doesn't have an ``apply`` method + + + .. versionadded:: 2.8.0 + """ + builtin_backends = { + "serial": BackendSerial, + "multiprocessing": BackendMultiprocessing, + "dask": BackendDask + } + + backend_class = builtin_backends.get(backend, backend) + supported_backend_classes = [ + builtin_backends.get(b) + for b in self.get_supported_backends() + ] + + # check for serial-only classes + if not self.parallelizable and backend_class is not BackendSerial: + raise ValueError(f"Can not parallelize class {self.__class__}") + + # make sure user enabled 'unsupported_backend=True' for custom classes + if (not unsupported_backend and self.parallelizable + and backend_class not in supported_backend_classes): + raise ValueError(( + f"Must specify 'unsupported_backend=True'" + f"if you want to use a custom {backend_class=} for {self.__class__}" + )) + + # check for the presence of parallelizable transformations + if backend_class is not BackendSerial and any( + not t.parallelizable + for t in self._trajectory.transformations): + raise ValueError(( + "Trajectory should not have " + "associated unparallelizable transformations")) + + # conclude mapping from string to backend class if it's a builtin backend + if isinstance(backend, str): + return backend_class(n_workers=n_workers) + + # make sure we haven't specified n_workers twice + if ( + isinstance(backend, BackendBase) + and n_workers is not None + and hasattr(backend, 'n_workers') + and backend.n_workers != n_workers + ): + raise ValueError(( + f"n_workers specified twice: in {backend.n_workers=}" + f"and in run({n_workers=}). Remove it from run()" + )) + + # or pass along an instance of the class itself + # after ensuring it has apply method + if not isinstance(backend, BackendBase) or not hasattr(backend, "apply"): + raise ValueError(( + f"{backend=} is invalid: should have 'apply' method " + "and be instance of MDAnalysis.analysis.backends.BackendBase" + )) + return backend + + def run( + self, + start: int = None, + stop: int = None, + step: int = None, + frames: Iterable = None, + verbose: bool = None, + n_workers: int = None, + n_parts: int = None, + backend: Union[str, BackendBase] = None, + *, + unsupported_backend: bool = False, + progressbar_kwargs=None, + ): """Perform the calculation Parameters @@ -402,20 +712,42 @@ def run(self, start=None, stop=None, step=None, frames=None, step : int, optional number of frames to skip between each analysed frame frames : array_like, optional - array of integers or booleans to slice trajectory; `frames` can - only be used *instead* of `start`, `stop`, and `step`. Setting - *both* `frames` and at least one of `start`, `stop`, `step` to a - non-default value will raise a :exc:`ValueError`. + array of integers or booleans to slice trajectory; ``frames`` can + only be used *instead* of ``start``, ``stop``, and ``step``. Setting + *both* ``frames`` and at least one of ``start``, ``stop``, ``step`` + to a non-default value will raise a :exc:`ValueError`. .. versionadded:: 2.2.0 - verbose : bool, optional Turn on verbosity progressbar_kwargs : dict, optional ProgressBar keywords with custom parameters regarding progress bar - position, etc; see :class:`MDAnalysis.lib.log.ProgressBar` for full - list. + position, etc; see :class:`MDAnalysis.lib.log.ProgressBar` + for full list. Available only for ``backend='serial'`` + backend : Union[str, BackendBase], optional + By default, performs calculations in a serial fashion. + Otherwise, user can choose a backend: ``str`` is matched to a + builtin backend (one of ``serial``, ``multiprocessing`` and + ``dask``), or a :class:`MDAnalysis.analysis.results.BackendBase` + subclass. + + .. versionadded:: 2.8.0 + n_workers : int + positive integer with number of workers (processes, in case of + built-in backends) to split the work between + + .. versionadded:: 2.8.0 + n_parts : int, optional + number of parts to split computations across. Can be more than + number of workers. + + .. versionadded:: 2.8.0 + unsupported_backend : bool, optional + if you want to run your custom backend on a parallelizable class + that has not been tested by developers, by default False + + .. versionadded:: 2.8.0 .. versionchanged:: 2.2.0 @@ -425,34 +757,88 @@ def run(self, start=None, stop=None, step=None, frames=None, .. versionchanged:: 2.5.0 Add `progressbar_kwargs` parameter, allowing to modify description, position etc of tqdm progressbars - """ - logger.info("Choosing frames to analyze") - # if verbose unchanged, use class default - verbose = getattr(self, '_verbose', - False) if verbose is None else verbose - self._setup_frames(self._trajectory, start=start, stop=stop, - step=step, frames=frames) - logger.info("Starting preparation") - self._prepare() - logger.info("Starting analysis loop over %d trajectory frames", - self.n_frames) - if progressbar_kwargs is None: - progressbar_kwargs = {} + .. versionchanged:: 2.8.0 + Introduced ``backend``, ``n_workers``, ``n_parts`` and + ``unsupported_backend`` keywords, and refactored the method logic to + support parallelizable execution. + """ + # default to serial execution + backend = "serial" if backend is None else backend + + progressbar_kwargs = {} if progressbar_kwargs is None else progressbar_kwargs + if ((progressbar_kwargs or verbose) and + not (backend == "serial" or + isinstance(backend, BackendSerial))): + raise ValueError("Can not display progressbar with non-serial backend") + + # if number of workers not specified, try getting the number from + # the backend instance if possible, or set to 1 + if n_workers is None: + n_workers = ( + backend.n_workers + if isinstance(backend, BackendBase) and hasattr(backend, "n_workers") + else 1 + ) + + # set n_parts and check that is has a reasonable value + n_parts = n_workers if n_parts is None else n_parts + + # do this as early as possible to check client parameters + # before any computations occur + executor = self._configure_backend( + backend=backend, + n_workers=n_workers, + unsupported_backend=unsupported_backend) + if ( + hasattr(executor, "n_workers") and n_parts < executor.n_workers + ): # using executor's value here for non-default executors + warnings.warn(( + f"Analysis not making use of all workers: " + f"{executor.n_workers=} is greater than {n_parts=}")) + + # start preparing the run + worker_func = partial( + self._compute, + progressbar_kwargs=progressbar_kwargs, + verbose=verbose) + self._setup_frames( + trajectory=self._trajectory, + start=start, stop=stop, step=step, frames=frames) + computation_groups = self._setup_computation_groups( + start=start, stop=stop, step=step, frames=frames, n_parts=n_parts + ) + + # get all results from workers in other processes. + # we need `AnalysisBase` classes + # since they hold `frames`, `times` and `results` attributes + remote_objects: list["AnalysisBase"] = executor.apply( + worker_func, computation_groups) + self.frames = np.hstack([obj.frames for obj in remote_objects]) + self.times = np.hstack([obj.times for obj in remote_objects]) + + # aggregate results from results obtained in remote workers + remote_results = [obj.results for obj in remote_objects] + results_aggregator = self._get_aggregator() + self.results = results_aggregator.merge(remote_results) - for i, ts in enumerate(ProgressBar( - self._sliced_trajectory, - verbose=verbose, - **progressbar_kwargs)): - self._frame_index = i - self._ts = ts - self.frames[i] = ts.frame - self.times[i] = ts.time - self._single_frame() - logger.info("Finishing up") self._conclude() return self + def _get_aggregator(self) -> ResultsGroup: + """Returns a default aggregator that takes entire results + if there is a single object, and raises ValueError otherwise + + Returns + ------- + ResultsGroup + aggregating object + + + .. versionadded:: 2.8.0 + """ + return ResultsGroup(lookup=None) + class AnalysisFromFunction(AnalysisBase): r"""Create an :class:`AnalysisBase` from a function working on AtomGroups @@ -503,8 +889,18 @@ def rotation_matrix(mobile, ref): .. versionchanged:: 2.0.0 Former :attr:`results` are now stored as :attr:`results.timeseries` + + .. versionchanged:: 2.8.0 + Added :meth:`get_supported_backends()`, introducing 'serial', 'multiprocessing' + and 'dask' backends. """ + _analysis_algorithm_is_parallelizable = True + + @classmethod + def get_supported_backends(cls): + return ("serial", "multiprocessing", "dask") + def __init__(self, function, trajectory=None, *args, **kwargs): if (trajectory is not None) and (not isinstance( trajectory, coordinates.base.ProtoReader)): @@ -531,9 +927,11 @@ def __init__(self, function, trajectory=None, *args, **kwargs): def _prepare(self): self.results.timeseries = [] + def _get_aggregator(self): + return ResultsGroup({"timeseries": ResultsGroup.flatten_sequence}) + def _single_frame(self): - self.results.timeseries.append(self.function(*self.args, - **self.kwargs)) + self.results.timeseries.append(self.function(*self.args, **self.kwargs)) def _conclude(self): self.results.frames = self.frames @@ -594,8 +992,11 @@ def RotationMatrix(mobile, ref): class WrapperClass(AnalysisFromFunction): def __init__(self, trajectory=None, *args, **kwargs): - super(WrapperClass, self).__init__(function, trajectory, - *args, **kwargs) + super(WrapperClass, self).__init__(function, trajectory, *args, **kwargs) + + @classmethod + def get_supported_backends(cls): + return ("serial", "dask") return WrapperClass @@ -633,9 +1034,11 @@ def _filter_baseanalysis_kwargs(function, kwargs): base_argspec = inspect.getargspec(AnalysisBase.__init__) n_base_defaults = len(base_argspec.defaults) - base_kwargs = {name: val - for name, val in zip(base_argspec.args[-n_base_defaults:], - base_argspec.defaults)} + base_kwargs = { + name: val + for name, val in zip(base_argspec.args[-n_base_defaults:], + base_argspec.defaults) + } try: # pylint: disable=deprecated-method @@ -648,7 +1051,8 @@ def _filter_baseanalysis_kwargs(function, kwargs): if base_kw in argspec.args: raise ValueError( "argument name '{}' clashes with AnalysisBase argument." - "Now allowed are: {}".format(base_kw, base_kwargs.keys())) + "Now allowed are: {}".format(base_kw, base_kwargs.keys()) + ) base_args = {} for argname, default in base_kwargs.items(): diff --git a/package/MDAnalysis/analysis/results.py b/package/MDAnalysis/analysis/results.py new file mode 100644 index 00000000000..8aa2062d2bc --- /dev/null +++ b/package/MDAnalysis/analysis/results.py @@ -0,0 +1,321 @@ +"""Analysis results and their aggregation --- :mod:`MDAnalysis.analysis.results` +================================================================================ + +Module introduces two classes, :class:`Results` and :class:`ResultsGroup`, +used for storing and aggregating data in +:meth:`MDAnalysis.analysis.base.AnalysisBase.run()`, respectively. + + +Classes +------- + +The :class:`Results` class is an extension of a built-in dictionary +type, that holds all assigned attributes in :attr:`self.data` and +allows for access either via dict-like syntax, or via class-like syntax: + +.. code-block:: python + + from MDAnalysis.analysis.results import Results + r = Results() + r.array = [1, 2, 3, 4] + assert r['array'] == r.array == [1, 2, 3, 4] + + +The :class:`ResultsGroup` can merge multiple :class:`Results` objects. +It is mainly used by :class:`MDAnalysis.analysis.base.AnalysisBase` class, +that uses :meth:`ResultsGroup.merge()` method to aggregate results from +multiple workers, initialized during a parallel run: + +.. code-block:: python + + from MDAnalysis.analysis.results import Results, ResultsGroup + import numpy as np + + r1, r2 = Results(), Results() + r1.masses = [1, 2, 3, 4, 5] + r2.masses = [0, 0, 0, 0] + r1.vectors = np.arange(10).reshape(5, 2) + r2.vectors = np.arange(8).reshape(4, 2) + + group = ResultsGroup( + lookup = { + 'masses': ResultsGroup.flatten_sequence, + 'vectors': ResultsGroup.ndarray_vstack + } + ) + + r = group.merge([r1, r2]) + assert r.masses == list((*r1.masses, *r2.masses)) + assert (r.vectors == np.vstack([r1.vectors, r2.vectors])).all() +""" +from collections import UserDict +import numpy as np +from typing import Callable, Sequence + + +class Results(UserDict): + r"""Container object for storing results. + + :class:`Results` are dictionaries that provide two ways by which values + can be accessed: by dictionary key ``results["value_key"]`` or by object + attribute, ``results.value_key``. :class:`Results` stores all results + obtained from an analysis after calling :meth:`~AnalysisBase.run()`. + + The implementation is similar to the :class:`sklearn.utils.Bunch` + class in `scikit-learn`_. + + .. _`scikit-learn`: https://scikit-learn.org/ + .. _`sklearn.utils.Bunch`: https://scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html + + Raises + ------ + AttributeError + If an assigned attribute has the same name as a default attribute. + + ValueError + If a key is not of type ``str`` and therefore is not able to be + accessed by attribute. + + Examples + -------- + >>> from MDAnalysis.analysis.base import Results + >>> results = Results(a=1, b=2) + >>> results['b'] + 2 + >>> results.b + 2 + >>> results.a = 3 + >>> results['a'] + 3 + >>> results.c = [1, 2, 3, 4] + >>> results['c'] + [1, 2, 3, 4] + + + .. versionadded:: 2.0.0 + + .. versionchanged:: 2.8.0 + Moved :class:`Results` to :mod:`MDAnalysis.analysis.results` + """ + + def _validate_key(self, key): + if key in dir(self): + raise AttributeError(f"'{key}' is a protected dictionary attribute") + elif isinstance(key, str) and not key.isidentifier(): + raise ValueError(f"'{key}' is not a valid attribute") + + def __init__(self, *args, **kwargs): + kwargs = dict(*args, **kwargs) + if "data" in kwargs.keys(): + raise AttributeError(f"'data' is a protected dictionary attribute") + self.__dict__["data"] = {} + self.update(kwargs) + + def __setitem__(self, key, item): + self._validate_key(key) + super().__setitem__(key, item) + + def __setattr__(self, attr, val): + if attr == "data": + super().__setattr__(attr, val) + else: + self.__setitem__(attr, val) + + def __getattr__(self, attr): + try: + return self[attr] + except KeyError as err: + raise AttributeError(f"'Results' object has no attribute '{attr}'") from err + + def __delattr__(self, attr): + try: + del self[attr] + except KeyError as err: + raise AttributeError(f"'Results' object has no attribute '{attr}'") from err + + def __getstate__(self): + return self.data + + def __setstate__(self, state): + self.data = state + + +class ResultsGroup: + """ + Management and aggregation of results stored in :class:`Results` instances. + + A :class:`ResultsGroup` is an optional description for :class:`Result` "dictionaries" + that are used in analysis classes based on :class:`AnalysisBase`. For each *key* in a + :class:`Result` it describes how multiple pieces of the data held under the key are + to be aggregated. This approach is necessary when parts of a trajectory are analyzed + independently (e.g., in parallel) and then need to me merged (with :meth:`merge`) to + obtain a complete data set. + + Parameters + ---------- + lookup : dict[str, Callable], optional + aggregation functions lookup dict, by default None + + Examples + -------- + + .. code-block:: python + + from MDAnalysis.analysis.results import ResultsGroup, Results + group = ResultsGroup(lookup={'mass': ResultsGroup.float_mean}) + obj1 = Results(mass=1) + obj2 = Results(mass=3) + assert {'mass': 2.0} == group.merge([obj1, obj2]) + + + .. code-block:: python + + # you can also set `None` for those attributes that you want to skip + lookup = {'mass': ResultsGroup.float_mean, 'trajectory': None} + group = ResultsGroup(lookup) + objects = [Results(mass=1, skip=None), Results(mass=3, skip=object)] + assert group.merge(objects, require_all_aggregators=False) == {'mass': 2.0} + + .. versionadded:: 2.8.0 + """ + + def __init__(self, lookup: dict[str, Callable] = None): + self._lookup = lookup + + def merge(self, objects: Sequence[Results], require_all_aggregators: bool = True) -> Results: + """Merge multiple Results into a single Results instance. + + Merge multiple :class:`Results` instances into a single one, using the + `lookup` dictionary to determine the appropriate aggregator functions for + each named results attribute. If the resulting object only contains a single + element, it just returns it without using any aggregators. + + Parameters + ---------- + objects : Sequence[Results] + Multiple :class:`Results` instances with the same data attributes. + require_all_aggregators : bool, optional + if True, raise an exception when no aggregation function for a + particular argument is found. Allows to skip aggregation for the + parameters that aren't needed in the final object -- + see :class:`ResultsGroup`. + + Returns + ------- + Results + merged :class:`Results` + + Raises + ------ + ValueError + if no aggregation function for a key is found and ``require_all_aggregators=True`` + """ + if len(objects) == 1: + merged_results = objects[0] + return merged_results + + merged_results = Results() + for key in objects[0].keys(): + agg_function = self._lookup.get(key, None) + if agg_function is not None: + results_of_t = [obj[key] for obj in objects] + merged_results[key] = agg_function(results_of_t) + elif require_all_aggregators: + raise ValueError(f"No aggregation function for {key=}") + return merged_results + + @staticmethod + def flatten_sequence(arrs: list[list]): + """Flatten a list of lists into a list + + Parameters + ---------- + arrs : list[list] + list of lists + + Returns + ------- + list + flattened list + """ + return [item for sublist in arrs for item in sublist] + + @staticmethod + def ndarray_sum(arrs: list[np.ndarray]): + """sums an ndarray along ``axis=0`` + + Parameters + ---------- + arrs : list[np.ndarray] + list of input arrays. Must have the same shape. + + Returns + ------- + np.ndarray + sum of input arrays + """ + return np.array(arrs).sum(axis=0) + + @staticmethod + def ndarray_mean(arrs: list[np.ndarray]): + """calculates mean of input ndarrays along ``axis=0`` + + Parameters + ---------- + arrs : list[np.ndarray] + list of input arrays. Must have the same shape. + + Returns + ------- + np.ndarray + mean of input arrays + """ + return np.array(arrs).mean(axis=0) + + @staticmethod + def float_mean(floats: list[float]): + """calculates mean of input float values + + Parameters + ---------- + floats : list[float] + list of float values + + Returns + ------- + float + mean value + """ + return np.array(floats).mean() + + @staticmethod + def ndarray_hstack(arrs: list[np.ndarray]): + """Performs horizontal stack of input arrays + + Parameters + ---------- + arrs : list[np.ndarray] + input numpy arrays + + Returns + ------- + np.ndarray + result of stacking + """ + return np.hstack(arrs) + + @staticmethod + def ndarray_vstack(arrs: list[np.ndarray]): + """Performs vertical stack of input arrays + + Parameters + ---------- + arrs : list[np.ndarray] + input numpy arrays + + Returns + ------- + np.ndarray + result of stacking + """ + return np.vstack(arrs) diff --git a/package/MDAnalysis/analysis/rms.py b/package/MDAnalysis/analysis/rms.py index 36067d76b6d..afb11ed7d2e 100644 --- a/package/MDAnalysis/analysis/rms.py +++ b/package/MDAnalysis/analysis/rms.py @@ -166,10 +166,10 @@ import logging import warnings -import MDAnalysis.lib.qcprot as qcp -from MDAnalysis.analysis.base import AnalysisBase -from MDAnalysis.exceptions import SelectionError, NoDataError -from MDAnalysis.lib.util import asiterable, iterable, get_weights +from ..lib import qcprot as qcp +from ..analysis.base import AnalysisBase, ResultsGroup +from ..exceptions import SelectionError +from ..lib.util import asiterable, iterable, get_weights logger = logging.getLogger('MDAnalysis.analysis.rmsd') @@ -358,8 +358,17 @@ class RMSD(AnalysisBase): .. versionchanged:: 2.0.0 :attr:`rmsd` results are now stored in a :class:`MDAnalysis.analysis.base.Results` instance. - + .. versionchanged:: 2.8.0 + introduced a :meth:`get_supported_backends` allowing for execution on with + ``multiprocessing`` and ``dask`` backends. """ + _analysis_algorithm_is_parallelizable = True + + @classmethod + def get_supported_backends(cls): + return ('serial', 'multiprocessing', 'dask',) + + def __init__(self, atomgroup, reference=None, select='all', groupselections=None, weights=None, weights_groupselections=False, tol_mass=0.1, ref_frame=0, **kwargs): @@ -670,6 +679,9 @@ def _prepare(self): self._mobile_coordinates64 = self.mobile_atoms.positions.copy().astype(np.float64) + def _get_aggregator(self): + return ResultsGroup(lookup={'rmsd': ResultsGroup.ndarray_vstack}) + def _single_frame(self): mobile_com = self.mobile_atoms.center(self.weights_select).astype(np.float64) self._mobile_coordinates64[:] = self.mobile_atoms.positions @@ -739,6 +751,11 @@ class RMSF(AnalysisBase): in the array :attr:`RMSF.results.rmsf`. """ + + @classmethod + def get_supported_backends(cls): + return ('serial',) + def __init__(self, atomgroup, **kwargs): r"""Parameters ---------- diff --git a/package/MDAnalysis/lib/util.py b/package/MDAnalysis/lib/util.py index 8600c390e1b..666a8c49279 100644 --- a/package/MDAnalysis/lib/util.py +++ b/package/MDAnalysis/lib/util.py @@ -49,6 +49,11 @@ .. autofunction:: format_from_filename_extension .. autofunction:: guess_format +Modules and packages +-------------------- + +.. autofunction:: is_installed + Streams ------- @@ -147,6 +152,7 @@ .. autofunction:: convert_aa_code .. autofunction:: parse_residue .. autofunction:: conv_float +.. autofunction:: atoi Class decorators ---------------- @@ -205,6 +211,7 @@ from functools import wraps import textwrap import weakref +import importlib import itertools import mmtf @@ -1333,6 +1340,7 @@ def fixedwidth_bins(delta, xmin, xmax): dx = 0.5 * (N * _delta - _length) # add half of the excess to each end return {'Nbins': N, 'delta': _delta, 'min': _xmin - dx, 'max': _xmax + dx} + def get_weights(atoms, weights): """Check that a `weights` argument is compatible with `atoms`. @@ -2592,3 +2600,17 @@ def atoi(s: str) -> int: return int(''.join(itertools.takewhile(str.isdigit, s.strip()))) except ValueError: return 0 + + +def is_installed(modulename: str): + """Checks if module is installed + + Parameters + ---------- + modulename : str + name of the module to be tested + + + .. versionadded:: 2.8.0 + """ + return importlib.util.find_spec(modulename) is not None diff --git a/package/doc/sphinx/source/conf.py b/package/doc/sphinx/source/conf.py index dfc6c606a13..40a096c0275 100644 --- a/package/doc/sphinx/source/conf.py +++ b/package/doc/sphinx/source/conf.py @@ -351,4 +351,5 @@ class KeyStyle(UnsrtStyle): 'waterdynamics': ('https://www.mdanalysis.org/waterdynamics/', None), 'pathsimanalysis': ('https://www.mdanalysis.org/PathSimAnalysis/', None), 'mdahole2': ('https://www.mdanalysis.org/mdahole2/', None), + 'dask': ('https://docs.dask.org/en/stable/', None), } diff --git a/package/doc/sphinx/source/documentation_pages/analysis/backends.rst b/package/doc/sphinx/source/documentation_pages/analysis/backends.rst new file mode 100644 index 00000000000..36f2a40cf11 --- /dev/null +++ b/package/doc/sphinx/source/documentation_pages/analysis/backends.rst @@ -0,0 +1,4 @@ +.. automodule:: MDAnalysis.analysis.backends + :members: + :private-members: + diff --git a/package/doc/sphinx/source/documentation_pages/analysis/base.rst b/package/doc/sphinx/source/documentation_pages/analysis/base.rst index 4eda92ceac6..2c2d884fc71 100644 --- a/package/doc/sphinx/source/documentation_pages/analysis/base.rst +++ b/package/doc/sphinx/source/documentation_pages/analysis/base.rst @@ -1,4 +1,4 @@ .. automodule:: MDAnalysis.analysis.base :members: - + :private-members: diff --git a/package/doc/sphinx/source/documentation_pages/analysis/parallelization.rst b/package/doc/sphinx/source/documentation_pages/analysis/parallelization.rst new file mode 100644 index 00000000000..91ae05fceca --- /dev/null +++ b/package/doc/sphinx/source/documentation_pages/analysis/parallelization.rst @@ -0,0 +1,358 @@ +.. -*- coding: utf-8 -*- + +.. _parallel-analysis: + +================= +Parallel analysis +================= + +.. versionadded:: 2.8.0 + Parallelization of analysis classes was added during Google Summer of Code + 2023 by `@marinegor `_ and MDAnalysis GSoC mentors. + +This section explains the implementation and background for +parallelization with the :class:`MDAnalysis.analysis.base.AnalysisBase`, what +users and developers need to know, when you should use parallelization (almost +always!), and when you should abstain from doing so (rarely). + + +How to use parallelization +========================== + +In order to use parallelization in a built-in analysis class ``SomeAnalysisClass``, +simply check which backends are available (see :ref:`backends` for backends +that are generally available), and then just enable them by providing +``backend='multiprocessing'`` and ``n_workers=...`` to ``SomeClass.run(...)`` +method: + +.. code-block:: python + + u = mda.Universe(...) + my_run = SomeClass(trajectory) + assert SomeClass.get_supported_backends() == ('serial', 'multiprocessing', 'dask') + + my_run.run(backend='multiprocessing', n_workers=12) + +For some classes, such as :class:`MDAnalysis.analysis.rms.RMSF` (in its current implementation), +split-apply-combine parallelization isn't possible, and running them will be +impossible with any but the ``serial`` backend. + +.. Note:: + + Parallelization is getting added to existing analysis classes. Initially, + only :class:`MDAnalysis.analysis.rms.RMSD` supports parallel analysis, but + we aim to increase support in future releases. + + +How does parallelization work +============================= + +The main idea behind its current implementation is that a trajectory analysis is +often trivially parallelizable, meaning you can analyze all frames +independently, and then merge them in a single object. This approach is also +known as "split-apply-combine", and isn't new to MDAnalysis users, since it was +first introduced in `PMDA`_ :footcite:p:`Fan2019`. +Version 2.8.0 of MDAnalysis brings this approach to the main library. + +.. _`PMDA`: https://github.com/mdanalysis/pmda + + +split-apply-combine +------------------- + +The following scheme explains the current :meth:`AnalysisBase.run +` protocol (user-implemented methods +are highlighted in orange): + +.. figure:: /images/AnalysisBase_parallel.png + + +In short, after checking input parameters and configuring the backend, +:class:`AnalysisBase <` splits all the +frames into *computation groups* (equally sized sequential groups of frames to +be processed by each worker). All groups then get **split** between workers of +a backend configured early, the main instance gets serialized and distributed +between workers, and then +:meth:`~MDAnalysis.analysis.base.AnalysisBase._compute()` method gets called +for all frames of a computation group. Within this method, a user-implemented +:meth:`~MDAnalysis.analysis.base.AnalysisBase._single_frame` method gets +**applied** to each frame in a computation group. After that, the main +instance gets an object that will **combine** all the objects from other +workers, and all instances get *merged* with an instance of +:class:`MDAnalysis.analysis.results.ResultsGroup`. Then, a normal +user-implemented :meth:`~MDAnalysis.analysis.base.AnalysisBase._compute` method +is called. + +Parallelization is fully compatible with existing code and does *not* break +any existing code pre-2.8.0. The parallelization protocol mimics the +single-process workflow where possible. Thus, user-implemented methods such as +:meth:`~MDAnalysis.analysis.base.AnalysisBase._prepare`, +:meth:`~MDAnalysis.analysis.base.AnalysisBase._single_frame` and +:meth:`~MDAnalysis.analysis.base.AnalysisBase._conclude` won't need to know +they are operating on an instance within the main python process, or on a +remote instance, since the executed code is the same in both cases. + + +Methods in ``AnalysisBase`` for parallelization +----------------------------------------------- + +For developers of new analysis tools +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If you want to write your own *parallel* analysis class, you have to implement +:meth:`~MDAnalysis.analysis.base.AnalysisBase._prepare`, +:meth:`~MDAnalysis.analysis.base.AnalysisBase._single_frame` and +:meth:`~MDAnalysis.analysis.base.AnalysisBase._conclude`. You also have to +denote if your analysis can run in parallel by following the steps under +:ref:`adding-parallelization`. + + +For MDAnalysis developers +~~~~~~~~~~~~~~~~~~~~~~~~~ + +From a developer point of view, there are a few methods that are important in +order to understand how parallelization is implemented: + +#. :meth:`MDAnalysis.analysis.base.AnalysisBase._define_run_frames` +#. :meth:`MDAnalysis.analysis.base.AnalysisBase._prepare_sliced_trajectory` +#. :meth:`MDAnalysis.analysis.base.AnalysisBase._configure_backend` +#. :meth:`MDAnalysis.analysis.base.AnalysisBase._setup_computation_groups` +#. :meth:`MDAnalysis.analysis.base.AnalysisBase._compute` +#. :meth:`MDAnalysis.analysis.base.AnalysisBase._get_aggregator` + +The first two methods share the functionality of :meth:`_setup_frames`. +:meth:`_define_run_frames` is run once during analysis, as it checks that input +parameters `start`, `stop`, `step` or `frames` are consistent with the given +trajectory and prepares the ``slicer`` object that defines the iteration +pattern through the trajectory. :meth:`_prepare_sliced_trajectory` assigns to +the :attr:`self._sliced_trajectory` attribute, computes the number of frames in +it, and fills the :attr:`self.frames` and :attr:`self.times` arrays. In case +the computation will be later split between other processes, this method will +be called again on each of the computation groups. + +The method :meth:`_configure_backend` performs basic health checks for a given +analysis class -- namely, it compares a given backend (if it's a :class:`str` +instance, such as ``'multiprocessing'``) with the list of builtin backends (and +also the backends implemented for a given analysis subclass), and configures a +:class:`MDAnalysis.analysis.backends.BackendBase` instance accordingly. If the +user decides to provide a custom backend (any subclass of +:class:`MDAnalysis.analysis.backends.BackendBase`, or anything with an +:meth:`apply` method), it ensures that the number of workers wasn't specified +twice (during backend initialization and in :meth:`run` arguments). + +After a backend is configured, :meth:`_setup_computation_groups` splits the +frames prepared earlier in :attr:`self._prepare_sliced_trajectory` into a +number of groups, by default equal to the number of workers. + +In the :meth:`_compute` method, frames get initialized again with +:meth:`_prepare_sliced_trajectory`, and attributes necessary for a specific +analysis get initialized with the :meth:`_prepare` method. Then the function +iterates over :attr:`self._sliced_trajectory`, assigning +:attr:`self._frame_index` and :attr:`self._ts` as frame index (within a +computation group) and timestamp, and also setting respective +:attr:`self.frames` and :attr:`self.times` array values. + +After :meth:`_compute` has finished, the main analysis instance calls the +:meth:`_get_aggregator` method, which merges the :attr:`self.results` +attributes from other processes into a single +:class:`MDAnalysis.analysis.results.Results` instance, making it look for the +subsequent :meth:`_conclude` method as if the run was performed in a serial +fashion, without parallelization. + + +Helper classes for parallelization +================================== + +``ResultsGroup`` +---------------- + +:class:`MDAnalysis.analysis.results.ResultsGroup` extends the functionality of +the :class:`MDAnalysis.analysis.results.Results` class. Since the ``Results`` +class is basically a dictionary that also keeps track of assigned attributes, it +is possible to iterate over all these attributes later. ``ResultsGroup`` does +exactly that: given a list of the ``Results`` objects with the same attributes, +it applies a specific aggregation function to every attribute, and stores it as +a same attribute of the returned object: + +.. code-block:: python + + from MDAnalysis.analysis.results import ResultsGroup, Results + group = ResultsGroup(lookup={'mass': ResultsGroup.float_mean}) + obj1 = Results(mass=1) + obj2 = Results(mass=3) + assert group.merge([obj1, obj2]) == Results(mass=2.0) + + +``BackendBase`` +--------------- + +:class:`MDAnalysis.analysis.backends.BackendBase` holds all backend attributes, +and also implements an :meth:`MDAnalysis.analysis.backends.BackendBase.apply` +method, applying a given function to a list of its parameters, but in a parallel +fashion. Although in ``AnalysisBase`` it is used to apply a ``_compute`` +function, in principle it can be used to any arbitrary function and arguments, +given they're serializable. + + +When to use parallelization? (Known limitations) +================================================ + +For now, the syntax for running parallel analysis is explicit, meaning by +default the ``serial`` version will be run, and the parallelization won't be +enabled by default. Although we expect the parallelization to be useful in most +cases, there are some known caveats from the inital benchmarks. + +Fast ``_single_frame`` compared to reading from disk +---------------------------------------------------- + +In all cases, parallelization will not be useful only when frames are being +processed faster than being read from the disk, otherwise reading is the +bottleneck here. Hence, you'll benefit from parallelization only if you have +relatively much compute per frame, or a fast drive, as illustrated below: + +.. figure:: /images/parallelization_time.png + +In other words, if you have *fast* analysis (say, +:class:`MDAnalysis.analysis.rms.RMSD`) **and** a slow HDD drive, you are likely +to not get any benefits from parallelization. Otherwise, you should be fine. + +Serialization issues +-------------------- + +For built-in analysis classes, the default serialization with both +:mod:`multiprocessing` and :mod:`dask` is known to work. If you're using some custom +analysis class that e.g. stores a non-serializable object in one of its +attributes, you might get a serialization error (:exc:`PicklingError` if you're +using a ``multiprocessing`` backend). If you want to get around that, we suggest +trying ``backend='dask'`` (it uses ``dask`` serialization engine instead of +:mod:`pickle`). + +Out of memory issues +-------------------- + +If you have large memory footprint of each worker, you can run into +out-of-memory errors (i.e. your server freezes when executing a run). In this +case we suggest decreasing the number of workers from all available CPUs (that +you can get with :func:`multiprocessing.cpu_count`) to a smaller number. + +Progress bar is missing +----------------------- + +It is yet not possible to get a progress bar running with any parallel backend. +If you want an ETA of your analysis, we suggest running it in ``serial`` mode +for the first 10-100 frames with ``verbose=True``, and then running it with +multiple workers. Processing time scales almost linearly, so you can get your +ETA by dividing ``serial`` ETA by the number of workers. + + +.. _adding-parallelization: + +Adding parallelization to your own analysis class +================================================= + +If you want to add parallelization to your own analysis class, first make sure +your algorithm allows you to do that, i.e. you can process each frame independently. +Then it's rather simple -- let's look at the actual code that added +parallelization to the :class:`MDAnalysis.analysis.rms.RMSD`: + +.. code-block:: python + + from MDAnalysis.analysis.base import AnalysisBase + from MDAnalysis.analysis.results import ResultsGroup + + class RMSD(BackendBase): + @classmethod + def get_supported_backends(cls): + return ('serial', 'multiprocessing', 'dask',) + + _analysis_algorithm_is_parallelizable = True + + def _get_aggregator(self): + return ResultsGroup(lookup={'rmsd': ResultsGroup.ndarray_vstack}) + + +That's it! The first two methods are boilerplate -- +:meth:`get_supported_backends` returns a tuple with built-in backends that will +work for your class (if there are no serialization issues, it should be all +three), and ``_is_parallelizable`` is ``True`` (which is set to ``False`` in +``AnalysisBase``, hence we have to re-define it), and :meth:`_get_aggregator` +will be used as described earlier. Note that :mod:`MDAnalysis.analysis.results` +also provides a few convenient functions (defined as class methods of +:class:`~MDAnalysis.analysis.results.ResultsGroup`) for results aggregation: + +#. :meth:`~MDAnalysis.analysis.results.ResultsGroup.flatten_sequence` +#. :meth:`~MDAnalysis.analysis.results.ResultsGroup.ndarray_sum` +#. :meth:`~MDAnalysis.analysis.results.ResultsGroup.ndarray_mean` +#. :meth:`~MDAnalysis.analysis.results.ResultsGroup.float_mean` +#. :meth:`~MDAnalysis.analysis.results.ResultsGroup.ndarray_hstack` +#. :meth:`~MDAnalysis.analysis.results.ResultsGroup.ndarray_vstack` + + +So you'll likely find appropriate functions for basic aggregation there. + +Writing custom backends +======================= + +In order to write your custom backend (e.g. using :mod:`dask.distributed`), inherit +from the :class:`MDAnalysis.analysis.backends.BackendBase` and (re)-implement +:meth:`__init__` and :meth:`apply` methods. Optionally, you can implement methods for +validation of correct backend initialization -- :meth:`_get_checks` and +:meth:`_get_warnings`, that would raise an exception or give a warning, respectively, +when a new class instance is created: + +#. :meth:`MDAnalysis.analysis.backends._get_checks` +#. :meth:`MDAnalysis.analysis.backends._get_warnings` + +.. code-block:: python + + from MDAnalysis.analysis.backends import BackendBase + class ThreadsBackend(BackendBase): + def __init__(self, n_workers: int, starting_message: str = "Useless backend"): + self.n_workers = n_workers + self.starting_message = starting_message + self._validate() + + def _get_warnings(self): + return {True: 'warning: this backend is useless'} + + def _get_checks(self): + return {isinstance(self.n_workers, int), 'error: self.n_workers is not an integer'} + + def apply(self, func, computations): + from multiprocessing.dummy import Pool + + with Pool(processes=self.n_workers) as pool: + print(self.starting_message) + results = pool.map(func, computations) + return results + + +In order to use a custom backend with another analysis class that does not +explicitly support it, you must *explicitly state* that you're about to use an +unsupported_backend by passing the keyword argument +``unsupported_backend=True``: + +.. code-block:: python + + from MDAnalysis.analysis.rms import RMSD + R = RMSD(...) # setup the run + n_workers = 2 + backend = ThreadsBackend(n_workers=n_workers) + R.run(backend=backend, unsupported_backend=True) + +In this way, you will override the check for supported backends. + +.. Warning:: + + When you use ``unsupported_backend=True`` you should make sure that you get + the same results as when using a supported backend for which the analysis + class was tested. + + Before reporting a problem with an analysis class, make sure you tested it + with a supported backend. When reporting *always mention if you used* + ``unsupported_backend=True``. + + +.. rubric:: References +.. footbibliography:: + diff --git a/package/doc/sphinx/source/documentation_pages/analysis/results.rst b/package/doc/sphinx/source/documentation_pages/analysis/results.rst new file mode 100644 index 00000000000..22fc9fd81f3 --- /dev/null +++ b/package/doc/sphinx/source/documentation_pages/analysis/results.rst @@ -0,0 +1,4 @@ +.. automodule:: MDAnalysis.analysis.results + :members: + + diff --git a/package/doc/sphinx/source/documentation_pages/analysis_modules.rst b/package/doc/sphinx/source/documentation_pages/analysis_modules.rst index ee7f6568758..ec484b0382d 100644 --- a/package/doc/sphinx/source/documentation_pages/analysis_modules.rst +++ b/package/doc/sphinx/source/documentation_pages/analysis_modules.rst @@ -1,23 +1,42 @@ .. Contains the formatted docstrings from the analysis modules located in 'mdanalysis/MDAnalysis/analysis', although in some cases the documentation imports functions and docstrings from other files which are also curated to reStructuredText markup. +.. module:: MDAnalysis.analysis + **************** Analysis modules **************** The :mod:`MDAnalysis.analysis` module contains code to carry out specific -analysis functionality for MD trajectories. -It is based on the core functionality (i.e. trajectory -I/O, selections etc). The analysis modules can be used as examples for how to -use MDAnalysis but also as working code for research projects; typically all -contributed code has been used by the authors in their own work. -An analysis using the available modules -usually follows the same structure +analysis functionality for MD trajectories. It is based on the core +functionality (i.e. trajectory I/O, selections etc). The analysis modules can +be used as examples for how to use MDAnalysis but also as working code for +research projects; typically all contributed code has been used by the authors +in their own work. + +Getting started with analysis +============================= + +.. SeeAlso:: + + The `User Guide: Analysis`_ contains extensive documentation of the analysis + capabilities with user-friendly examples. + +.. _`User Guide: Analysis`: + https://userguide.mdanalysis.org/stable/examples/analysis/README.html + +Using the analysis classes +-------------------------- + +Most analysis tools in MDAnalysis are written as a single class. An analysis +usually follows the same pattern: #. Import the desired module, since analysis modules are not imported by default. #. Initialize the analysis class instance from the previously imported module. -#. Run the analysis, optionally for specific trajectory slices. -#. Access the analysis from the :attr:`results` attribute +#. Run the analysis with the :meth:`~MDAnalysis.analysis.base.AnalysisBase.run` + method, optionally for specific trajectory slices. +#. Access the analysis from the + :attr:`~MDAnalysis.analysis.base.AnalysisBase.results` attribute .. code-block:: python @@ -31,28 +50,81 @@ usually follows the same structure Please see the individual module documentation for any specific caveats and also read and cite the reference papers associated with these algorithms. -.. rubric:: Additional dependencies + +Using parallelization for built-in analysis runs +------------------------------------------------ + +.. versionadded:: 2.8.0 + +:class:`~MDAnalysis.analysis.base.AnalysisBase` subclasses can run on a backend +that supports parallelization (see :mod:`MDAnalysis.analysis.backends`). All +analysis runs use ``backend='serial'`` by default, i.e., they do not use +parallelization by default, which has been standard before release 2.8.0 of +MDAnalysis. + +Without any dependencies, only one backend is supported -- built-in +:mod:`multiprocessing`, that processes parts of a trajectory running separate +*processes*, i.e. utilizing multi-core processors properly. + +.. Note:: + + For now, parallelization has only been added to + :class:`MDAnalysis.analysis.rms.RMSD`, but by release 3.0 version it will be + introduced to all subclasses that can support it. + +In order to use that feature, simply add ``backend='multiprocessing'`` to your +run, and supply it with proper ``n_workers`` (use ``multiprocessing.cpu_count()`` +for maximum available on your machine): + +.. code-block:: python + + import multiprocessing + import MDAnalysis as mda + from MDAnalysisTests.datafiles import PSF, DCD + from MDAnalysis.analysis.rms import RMSD + from MDAnalysis.analysis.align import AverageStructure + + # initialize the universe + u = mda.Universe(PSF, DCD) + + # calculate average structure for reference + avg = AverageStructure(mobile=u).run() + ref = avg.results.universe + + # initialize RMSD run + rmsd = RMSD(u, ref, select='backbone') + rmsd.run(backend='multiprocessing', n_workers=multiprocessing.cpu_count()) + +For now, you have to be explicit and specify both ``backend`` and ``n_workers``, +since the feature is new and there are no good defaults for it. For example, +if you specify a too big `n_workers`, and your trajectory frames are big, +you might get and out-of-memory error when executing your run. + +You can also implement your own backends -- see :mod:`MDAnalysis.analysis.backends`. + + +Additional dependencies +----------------------- Some of the modules in :mod:`MDAnalysis.analysis` require additional Python packages to enable full functionality. For example, :mod:`MDAnalysis.analysis.encore` provides more options if `scikit-learn`_ is -installed. If you installed MDAnalysis with -:program:`pip` (see :ref:`installation-instructions`) -these packages are *not automatically installed*. -Although, one can add the ``[analysis]`` tag to the -:program:`pip` command to force their installation. If you installed -MDAnalysis with :program:`conda` then a -*full set of dependencies* is automatically installed. +installed. If you installed MDAnalysis with :program:`pip` (see +:ref:`installation-instructions`) these packages are *not automatically +installed* although one can add the ``[analysis]`` tag to the :program:`pip` +command to force their installation. If you installed MDAnalysis with +:program:`conda` then a *full set of dependencies* is automatically installed. Other modules require external programs. For instance, the -:mod:`MDAnalysis.analysis.hole2.hole` module requires an installation of the -HOLE_ suite of programs. You will need to install these external dependencies -by following their installation instructions before you can use the -corresponding MDAnalysis module. +:mod:`MDAnalysis.analysis.hole2` module requires an installation of the HOLE_ +suite of programs. You will need to install these external dependencies by +following their installation instructions before you can use the corresponding +MDAnalysis module. .. _scikit-learn: http://scikit-learn.org/ .. _HOLE: http://www.holeprogram.org/ + Building blocks for Analysis ============================ @@ -64,6 +136,9 @@ To build your own analysis class start by reading the documentation. :maxdepth: 1 analysis/base + analysis/backends + analysis/results + analysis/parallelization Distances and contacts ====================== diff --git a/package/doc/sphinx/source/images/AnalysisBase_parallel.png b/package/doc/sphinx/source/images/AnalysisBase_parallel.png new file mode 100644 index 0000000000000000000000000000000000000000..960e7cc257b0a6756d5df9f1b16bc67c46498923 GIT binary patch literal 274539 zcmdSA_g@q1^9Pzjm1girFG^96CWI_A=z<`=2ll2R5&sf*s1A)$f3=mgtgl4YJhNOvG)m42DVY~Yw>_gH^v@5F^^D_yN z`{>(ym#^Pvu9Y|uaQen(-hG?OUL~nBC-QP#5!Wy0xt@Hk`mX>(+SXxpDSkQqQqlX& 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