vectorization.py

""" Sum factorization vectorization """

import logging

from dune.perftool.loopy.vcl import get_vcl_type_size
from dune.perftool.loopy.symbolic import SumfactKernel, VectorizedSumfactKernel
from dune.perftool.generation import (backend,
                                      generator_factory,
                                      get_backend,
                                      get_counted_variable,
                                      get_global_context_value,
                                      )
from dune.perftool.pdelab.restriction import (Restriction,
                                              restricted_name,
                                              )
from dune.perftool.sumfact.tabulation import (BasisTabulationMatrixArray,
                                              quadrature_points_per_direction,
                                              set_quadrature_points,
                                              )
from dune.perftool.error import PerftoolError
from dune.perftool.options import get_option
from dune.perftool.tools import add_to_frozendict,round_to_multiple

from frozendict import frozendict
import itertools as it
import loopy as lp
import numpy as np


@generator_factory(item_tags=("vecinfo", "dryrundata"), cache_key_generator=lambda o, n: o)
def _cache_vectorization_info(old, new):
    if new is None:
        raise PerftoolError("Vectorization info for sum factorization kernel was not gathered correctly!")
    return new


_collect_sumfact_nodes = generator_factory(item_tags=("sumfactnodes", "dryrundata"), context_tags="kernel", no_deco=True)


def get_all_sumfact_nodes():
    from dune.perftool.generation import retrieve_cache_items
    return [i for i in retrieve_cache_items("kernel_default and sumfactnodes")]


def attach_vectorization_info(sf):
    assert isinstance(sf, SumfactKernel)
    if get_global_context_value("dry_run"):
        return _collect_sumfact_nodes(sf)
    else:
        return _cache_vectorization_info(sf, None)


@backend(interface="vectorization_costfunction", name="greedy")
def greedy_costfunction(sf):
    return 1


@backend(interface="vectorization_costfunction", name="explicit")
def explicit_costfunction(sf):
    # Read the explicitly set values for horizontal and vertical vectorization
    width = get_vcl_type_size(np.float64)
    horizontal = int(get_option("vectorization_horizontal", width))
    vertical = int(get_option("vectorization_vertical", 1))

    if sf.horizontal_width == horizontal and sf.vertical_width == vertical:
        return 1
    else:
        return 2


def strategy_cost(strategy):
    qp, strategy = strategy
    set_quadrature_points(qp)
    func = get_backend(interface="vectorization_costfunction",
                       name=get_option("vectorization_strategy"))
    return sum(float(func(sf)) for sf in strategy.values())


def stringify_vectorization_strategy(strategy):
    result = []
    qp, strategy = strategy

    result.append["Printing potential vectorization strategy:"]
    result.append["Quadrature point tuple: {}".format(qp)]

    # Look for all realizations in the strategy and iterate over them
    cache_keys = frozenset(v.cache_key for v in strategy.values())
    for ck in cache_keys:
        # Filter all the kernels that are realized by this and print
        for key in strategy:
            if strategy[key].cache_key == ck:
                result.append["{}:".format(key)]

        # Find one representative to print
        for val in strategy.values():
            if val.cache_key == ck:
                result.append["    {}".format(val)]
                break


def decide_vectorization_strategy():
    """ Decide how to vectorize!
    Note that the vectorization of the quadrature loop is independent of this,
    as it is implemented through a post-processing (== loopy transformation) step.
    """
    logger = logging.getLogger(__name__)

    # Retrieve all sum factorization kernels for stage 1 and 3
    from dune.perftool.generation import retrieve_cache_items
    all_sumfacts = [i for i in retrieve_cache_items("kernel_default and sumfactnodes")]

    # Stage 1 sumfactorizations that were actually used
    basis_sumfacts = [i for i in retrieve_cache_items('kernel_default and basis_sf_kernels')]

    # This means we can have sum factorizations that will not get used
    inactive_sumfacts = [i for i in all_sumfacts if i.stage == 1 and i not in basis_sumfacts]

    # All sum factorization kernels that get used
    active_sumfacts = [i for i in all_sumfacts if i.stage == 3 or i in basis_sumfacts]

    # If no vectorization is needed, abort now
    if get_option("vectorization_strategy") == "none":
        for sf in all_sumfacts:
            _cache_vectorization_info(sf, sf.copy(buffer=get_counted_variable("buffer")))
        return

    logger.debug("decide_vectorization_strategy: Found {} active sum factorization nodes"
                 .format(len(active_sumfacts)))

    # Find the best vectorization strategy by using a costmodel
    width = get_vcl_type_size(np.float64)
    strategy = min(vectorization_opportunity_generator(active_sumfacts, width),
                   key=strategy_cost)

    # Treat the quadrature points
    qp, sfdict = strategy
    set_quadrature_points(qp)

    logger.debug("decide_vectorization_strategy: Decided for the following strategy:"
                 "\n".join(stringify_vectorization_strategy(strategy)))

    # We map inactive sum factorization kernels to 0
    sfdict = add_to_frozendict(sfdict, {sf: 0 for sf in inactive_sumfacts})

    # Register the results
    for sf in all_sumfacts:
        _cache_vectorization_info(sf, sfdict.get(sf, no_vec(sf)))


def vectorization_opportunity_generator(sumfacts, width):
    """ Generator that yields all vectorization opportunities for the given
    sum factorization kernels as tuples of quadrature point tuple and vectorization
    dictionary """
    #
    # Find all the possible quadrature point tuples
    #
    quad_points = [quadrature_points_per_direction()]

    if True or get_option("vectorization_allow_quadrature_changes"):
        sf = next(iter(sumfacts))
        depth = 1
        while depth <= width:
            i = 0 if sf.matrix_sequence[0].face is None else 1
            quad = list(quadrature_points_per_direction())
            quad[i] = round_to_multiple(quad[i], depth)
            quad_points.append(tuple(quad))
            depth = depth * 2
        quad_points = list(set(quad_points))

    for qp in quad_points:
        #
        # Determine vectorization opportunities given a fixed quadrature point number
        #
        for opp in fixed_quad_vectorization_opportunity_generator(frozenset(sumfacts), width, qp):
            yield qp, opp


def fixed_quad_vectorization_opportunity_generator(sumfacts, width, qp, already=frozendict()):
    if len(sumfacts) == 0:
        # We have gone into recursion deep enough to have all sum factorization nodes
        # assigned their vectorized counterpart. We can yield the result now!
        yield already
        return

    # Otherwise we pick a random sum factorization kernel and construct all the vectorization
    # opportunities realizing this particular kernel and go into recursion.
    sf_to_decide = next(iter(sumfacts))

    # Have "unvectorized" as an option, although it is not good
    for opp in fixed_quad_vectorization_opportunity_generator(sumfacts.difference({sf_to_decide}),
                                                              width,
                                                              qp,
                                                              add_to_frozendict(already,
                                                                                {sf_to_decide: sf_to_decide.copy(buffer=get_counted_variable("buffer"))}
                                                                                ),
                                                              ):
        yield opp

    # Find all the sum factorization kernels that the chosen kernel can be parallelized with
    #
    # TODO: Right now we check for same input, which is not actually needed in order
    #       to be a suitable candidate! We should relax this concept at some point!
    candidates = filter(lambda sf: sf.input_key == sf_to_decide.input_key, sumfacts)

    horizontal = 1
    while horizontal <= width:
        # Iterate over the possible combinations of sum factorization kernels
        # taking into account all the permutations of kernels. This also includes
        # combinations which use a padding of 1.
        for combo in it.chain(it.permutations(candidates, horizontal),
                              it.permutations(candidates, horizontal - 1)):
            # The chosen kernels must be part of the kernels for recursion
            # to work correctly
            if sf_to_decide not in combo:
                continue

            # Set up the vectorization dict for this combo
            vecdict = get_vectorization_dict(combo, width // horizontal, horizontal, qp)
            if vecdict is None:
                # This particular choice was rejected for some reason.
                # Possible reasons:
                # * the quadrature point tuple not being suitable
                #   for this vectorization strategy
                continue

            # Go into recursion to also vectorize all kernels not in this combo
            for opp in fixed_quad_vectorization_opportunity_generator(sumfacts.difference(combo),
                                                                      width,
                                                                      qp,
                                                                      add_to_frozendict(already, vecdict),
                                                                      ):
                yield opp

        horizontal = horizontal * 2


def get_vectorization_dict(sumfacts, vertical, horizontal, qp):
    # Enhance the list of sumfact nodes by adding vertical splittings
    kernels = []
    for sf in sumfacts:
        # No slicing needed in the pure horizontal case
        if vertical == 1:
            kernels.append(sf)
            continue

        # Determine the slicing direction
        slice_direction = 0 if sf.matrix_sequence[0].face is None else 1
        if qp[slice_direction] % vertical != 0:
            return None

        # Split the basis tabulation matrices
        oldtab = sf.matrix_sequence[slice_direction]
        for i in range(vertical):
            seq = list(sf.matrix_sequence)
            seq[slice_direction] = oldtab.copy(slice_size=vertical,
                                               slice_index=i)
            kernels.append(sf.copy(matrix_sequence=tuple(seq)))


    # Join the new kernels into a sum factorization node
    buffer = get_counted_variable("joined_buffer")
    return {sf: VectorizedSumfactKernel(kernels=tuple(kernels),
                                        horizontal_width=horizontal,
                                        vertical_width=vertical,
                                        buffer=buffer,
                                        ) for sf in sumfacts}