# Bumps documentation

## bumps.mapper

# Source code for bumps.mapper

    """
    Parallel and serial mapper implementations.

    The API is a bit crufty since interprocess communication has evolved from
    the original implementation. And the names are misleading.

    Usage::

        Mapper.start_worker(problem)
        mapper = Mapper.start_mapper(problem, None, cpus)
        result = mapper(points)
        ...
        mapper = Mapper.start_mapper(problem, None, cpus)
        result = mapper(points)
        Mapper.stop_mapper()
    """

    import sys
    import os
    import signal

    from cloudpickle import dumps, loads
    # {{{ http://code.activestate.com/recipes/496767/ (r1)
    # Converted to use ctypes by Paul Kienzle

    PROCESS_ALL_ACCESS = 0x1F0FFF

    def can_pickle(problem, check=False):
        """
        Returns True if *problem* can be pickled.

        If this method returns False then MPMapper cannot be used and
        SerialMapper should be used instead.

        If *check* is True then call *nllf()* on the duplicated object as a
        "smoke test" to verify that the function will run after copying. This
        is not foolproof. For example, access to a database may work in the
        duplicated object because the connection is open and available in the
        current process, but it will fail when trying to run on a remote machine.
        """
        try:
            dup = loads(dumps(problem))
            if check:
                dup.nllf()
            return True
        except Exception:
            return False

    def setpriority(pid=None, priority=1):
        """
        Set The Priority of a Windows Process.  Priority is a value between 0-5
        where 2 is normal priority and 5 is maximum.  Default sets the priority
        of the current python process but can take any valid process ID.
        """

        # import win32api,win32process,win32con
        from ctypes import windll

        priorityclasses = [
            0x40,  # IDLE_PRIORITY_CLASS,
            0x4000,  # BELOW_NORMAL_PRIORITY_CLASS,
            0x20,  # NORMAL_PRIORITY_CLASS,
            0x8000,  # ABOVE_NORMAL_PRIORITY_CLASS,
            0x80,  # HIGH_PRIORITY_CLASS,
            0x100,  # REALTIME_PRIORITY_CLASS
        ]
        if pid is None:
            pid = windll.kernel32.GetCurrentProcessId()
        handle = windll.kernel32.OpenProcess(PROCESS_ALL_ACCESS, True, pid)
        windll.kernel32.SetPriorityClass(handle, priorityclasses[priority])

    # end of http://code.activestate.com/recipes/496767/ }}}

    def nice():
        if os.name == "nt":
            setpriority(priority=1)
        else:
            os.nice(5)

    # Noise so that the type checker is happy

    class BaseMapper(object):
        has_problem = False

        @staticmethod
        def start_worker(problem):
            """Called with the problem to initialize the worker"""
            raise NotImplementedError()

        @staticmethod
        def start_mapper(problem, modelargs=None, cpus=0):
            """Called with the problem on a new fit."""
            raise NotImplementedError()

        # TODO: deprecate mapper parameter

        @staticmethod
        def stop_mapper(mapper=None):
            raise NotImplementedError()

    class SerialMapper(BaseMapper):

        @staticmethod
        def start_worker(problem):
            pass

        @staticmethod
        def start_mapper(problem, modelargs=None, cpus=0):
            # Note: map is n iterator in python 3.x
            return lambda points: list(map(problem.nllf, points))

        @staticmethod
        def stop_mapper(mapper=None):
            pass

    # Load the problem in the remote process rather than pickling
    # def _MP_load_problem(*modelargs):
    #    from .fitproblem import load_problem
    #    _MP_set_problem(load_problem(*modelargs))

    def _MP_setup():
        # Using MPMapper class variables to store worker globals.
        # It doesn't matter if they conflict with the controller values since
        # they are in a different process.
        signal.signal(signal.SIGINT, signal.SIG_IGN)
        nice()

    def _MP_run_problem(problem_point_tuple):
        problem_id, point, shared_pickled_problem = problem_point_tuple
        if problem_id != MPMapper.problem_id:
            # print(f"Fetching problem {problem_id} from namespace")
            MPMapper.problem = loads(shared_pickled_problem[:].tobytes())
            MPMapper.problem_id = problem_id
        return MPMapper.problem.nllf(point)

    class MPMapper(BaseMapper):
        # Note: suprocesses are using the same variables
        pool = None
        manager = None
        problem_id = 0
        shared_pickled_problem = None
        problem = None

        @staticmethod
        def start_worker(problem):
            pass

        @staticmethod
        def start_mapper(problem, modelargs=None, cpus=0):
            import multiprocessing

            # Set up the process pool on the first call.
            if MPMapper.pool is None:
                # Create a sync namespace to distribute the problem description.
                MPMapper.manager = multiprocessing.Manager()
                # Start the process pool, sending the namespace handle
                if cpus == 0:
                    cpus = multiprocessing.cpu_count()
                MPMapper.pool = multiprocessing.Pool(cpus, _MP_setup)

            # Increment the problem number and store the problem in the namespace.
            # The store action uses pickle to transfer python objects to the
            # manager process. Since this may fail for lambdas and for functions
            # defined within the model file, instead use cloudpickle
            # to pickle the problem before storing.
            MPMapper.problem_id += 1
            MPMapper.pickled_problem = dumps(problem)
            MPMapper.shared_pickled_problem = MPMapper.manager.Array("B", MPMapper.pickled_problem)

            # Set the mapper to send problem_id/point/shared_pickled_problem value triples
            def mapper(points):
                try:
                    return MPMapper.pool.map(
                        _MP_run_problem, ((MPMapper.problem_id, p, MPMapper.shared_pickled_problem) for p in points)
                    )
                except KeyboardInterrupt:
                    MPMapper.stop_mapper()

            return mapper

        @staticmethod
        def stop_mapper(mapper=None):
            # reset pool and manager
            if MPMapper.pool is not None:
                MPMapper.pool.terminate()
                MPMapper.pool = None
                MPMapper.manager.shutdown()
                MPMapper.manager = None

            # Don't reset problem id; it keeps count even when mapper is restarted.
            ##MPMapper.problem_id = 0

    def _MPI_set_problem(problem, comm, root=0):
        pickled_problem = dumps(problem) if comm.rank == root else None
        pickled_problem = comm.bcast(pickled_problem, root=root)
        return problem if comm.rank == root else loads(pickled_problem)

    def _MPI_map(problem, points, comm, root=0):
        import numpy as np
        from mpi4py import MPI

        # print(f"{comm.rank}: mapping points")

        # Send number of points and number of variables per point.
        # root: return result if there are points otherwise return False
        # worker: return True if there are points otherwise return False
        npoints, nvars = comm.bcast(points.shape if comm.rank == root else None, root=root)
        if npoints == 0:
            return None

        # Divvy points equally across all processes
        whole = points if comm.rank == root else None
        idx = np.arange(comm.size)
        size = np.ones(comm.size, idx.dtype) * (npoints // comm.size) + (idx < npoints % comm.size)
        offset = np.cumsum(np.hstack((0, size[:-1])))
        part = np.empty((size[comm.rank], nvars), dtype="d")
        comm.Scatterv((whole, (size * nvars, offset * nvars), MPI.DOUBLE), (part, MPI.DOUBLE), root=root)

        # Evaluate models assigned to each processor
        partial_result = np.array([problem.nllf(pk) for pk in part], dtype="d")

        # Collect results
        result = np.empty(npoints, dtype="d") if comm.rank == root else True
        comm.Barrier()
        comm.Gatherv((partial_result, MPI.DOUBLE), (result, (size, offset), MPI.DOUBLE), root=root)
        comm.Barrier()
        return result

    def using_mpi():
        # Can look for environment variables defined by mpirun
        #   mpich: PMI_SIZE, PMI_*, MPI_*
        #   openmp: OMPI_COMM_WORLD_SIZE, OMPI_* PMIX_*
        #   impi_rt (intel): PMI_SIZE I_MPI_* HYDRA_*
        #   msmpi (microsoft): PMI_SIZE PMI_* MSMPI_*
        size = os.environ.get("PMI_SIZE", None)
        if size is None:
            size = os.environ.get("OMPI_COMM_WORLD_SIZE", None)
        return size is not None and int(size) > 1

    class MPIMapper(BaseMapper):
        has_problem = True
        """For MPIMapper only the worker is initialized with the fit problem."""

        @staticmethod
        def start_worker(problem):
            """
            Start the worker process.

            For the main process this does nothing and returns immediately. The
            worker processes never return.

            Each worker sits in a loop waiting for the next batch of points
            for the problem, or for the next problem. Set t
            problem is set to None, then exit the process and never
            """
            from mpi4py import MPI

            comm, root = MPI.COMM_WORLD, 0
            MPIMapper.rank = comm.rank
            rank = comm.rank
            # print(f"MPI {rank} of {comm.size} initializing")

            # If worker, sit in a loop waiting for the next point.
            # If the point is empty, then wait for a new problem.
            # If the problem is None then we are done, otherwise wait for next point.
            if rank != root:
                # print(f"{rank}: looping")
                while True:
                    result = _MPI_map(problem, None, comm, root)
                    if result is None:
                        problem = _MPI_set_problem(None, comm, root)
                        if problem is None:
                            break
                        # print(f"{rank}: changing problem")

                # print(f"{rank}: finalizing")
                MPI.Finalize()

                # Exit the program after the worker is done. Don't return
                # to the caller since that is continuing on with the main
                # thread, and in particular, attempting to rerun the fit on
                # each worker.
                # print(f"{rank}: Worker exiting")
                sys.exit(0)
                # print(f"{rank}: Worker exited")
            else:
                # Root initialization:
                MPIMapper.has_problem = problem is not None

                # print("mapper has problem", MPIMapper.has_problem)

        @staticmethod
        def start_mapper(problem, modelargs=None, cpus=0):
            # Only root can get here---worker is stuck in start_worker
            from mpi4py import MPI
            import numpy as np

            comm, root = MPI.COMM_WORLD, 0

            # Signal new problem then send it, but not on the first fit. We do this
            # so that we can still run MPI fits even if the problem itself cannot
            # be pickled, but only the first one. (You can still fit a series even
            # if the problem can't be pickled, but you will need to restart the
            # MPI job separately for each fit.)
            # Note: setting problem to None stops the program, so call finalize().
            def mapper(points):
                return _MPI_map(problem, points, comm, root)

            if not MPIMapper.has_problem:  # Only true on the first fit
                # print(f"*** {comm.rank}: replacing problem")
                # Send an empty set of points to signal a new problem is coming.
                mapper(np.empty((0, 0), "d"))
                _MPI_set_problem(problem, comm, root)
                if problem is None:
                    # print(f"{comm.rank}: finalizing root")
                    MPI.Finalize()
            MPIMapper.has_problem = False
            return mapper

        @staticmethod
        def stop_mapper(mapper=None):
            # print("stopping mapper")
            # Set problem=None to stop the program.
            MPIMapper.start_mapper(None, None)