__copyright__ = "Copyright (C) 2013 Andreas Kloeckner"
__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
import numpy as np
import loopy as lp
from loopy.version import MOST_RECENT_LANGUAGE_VERSION
from sumpy.tools import KernelCacheWrapper, KernelComputation
import logging
logger = logging.getLogger(__name__)
__doc__ = """
Particle-to-expansion
---------------------
.. autoclass:: P2EBase
.. autoclass:: P2EFromSingleBox
.. autoclass:: P2EFromCSR
"""
# {{{ P2E base class
[docs]class P2EBase(KernelComputation, KernelCacheWrapper):
"""Common input processing for kernel computations."""
def __init__(self, ctx, expansion, kernels=None,
name=None, device=None, strength_usage=None):
"""
:arg expansion: a subclass of :class:`sympy.expansion.ExpansionBase`
:arg strength_usage: A list of integers indicating which expression
uses which source strength indicator. This implicitly specifies the
number of strength arrays that need to be passed.
Default: all kernels use the same strength.
"""
from sumpy.kernel import (TargetTransformationRemover,
SourceTransformationRemover)
txr = TargetTransformationRemover()
sxr = SourceTransformationRemover()
if kernels is None:
kernels = [txr(expansion.kernel)]
else:
kernels = kernels
expansion = expansion.with_kernel(sxr(txr(expansion.kernel)))
for knl in kernels:
assert txr(knl) == knl
assert sxr(knl) == expansion.kernel
KernelComputation.__init__(self, ctx=ctx, target_kernels=[],
source_kernels=kernels,
strength_usage=strength_usage, value_dtypes=None,
name=name, device=device)
self.expansion = expansion
self.dim = expansion.dim
def get_loopy_instructions(self):
from sumpy.symbolic import make_sym_vector
avec = make_sym_vector("a", self.dim)
import sumpy.symbolic as sp
rscale = sp.Symbol("rscale")
from sumpy.assignment_collection import SymbolicAssignmentCollection
sac = SymbolicAssignmentCollection()
strengths = [sp.Symbol(f"strength_{i}") for i in self.strength_usage]
coeffs = self.expansion.coefficients_from_source_vec(self.source_kernels,
avec, None, rscale, strengths, sac=sac)
coeff_names = []
for i, coeff in enumerate(coeffs):
sac.add_assignment(f"coeff{i}", coeff)
coeff_names.append(f"coeff{i}")
sac.run_global_cse()
code_transformers = [self.expansion.get_code_transformer()] \
+ [kernel.get_code_transformer() for kernel in self.source_kernels]
from sumpy.codegen import to_loopy_insns
return to_loopy_insns(
sac.assignments.items(),
vector_names={"a"},
pymbolic_expr_maps=code_transformers,
retain_names=coeff_names,
complex_dtype=np.complex128 # FIXME
)
def get_cache_key(self):
return (type(self).__name__, self.name, self.expansion,
tuple(self.source_kernels), tuple(self.strength_usage))
# }}}
# {{{ P2E from single box (P2M, likely)
[docs]class P2EFromSingleBox(P2EBase):
default_name = "p2e_from_single_box"
def get_kernel(self):
ncoeffs = len(self.expansion)
from sumpy.tools import gather_loopy_source_arguments
loopy_knl = lp.make_kernel(
[
"{[isrc_box]: 0<=isrc_box<nsrc_boxes}",
"{[isrc,idim]: isrc_start<=isrc<isrc_end and 0<=idim<dim}",
],
["""
for isrc_box
<> src_ibox = source_boxes[isrc_box]
<> isrc_start = box_source_starts[src_ibox]
<> isrc_end = isrc_start+box_source_counts_nonchild[src_ibox]
<> center[idim] = centers[idim, src_ibox] {id=fetch_center}
for isrc
<> a[idim] = center[idim] - sources[idim, isrc] {dup=idim}
"""] + self.get_loopy_instructions() + ["""
end
"""] + [f"<> strength_{i} = strengths[{i}, isrc]" for
i in set(self.strength_usage)] + [f"""
tgt_expansions[src_ibox-tgt_base_ibox, {coeffidx}] = \
simul_reduce(sum, isrc, coeff{coeffidx}) \
{{id_prefix=write_expn}}
""" for coeffidx in range(ncoeffs)] + ["""
end
"""],
[
lp.GlobalArg("sources", None, shape=(self.dim, "nsources"),
dim_tags="sep,c"),
lp.GlobalArg("strengths", None,
shape="strength_count, nsources", dim_tags="sep,C"),
lp.GlobalArg("box_source_starts,box_source_counts_nonchild",
None, shape=None),
lp.GlobalArg("centers", None, shape="dim, aligned_nboxes"),
lp.ValueArg("rscale", None),
lp.GlobalArg("tgt_expansions", None,
shape=("nboxes", ncoeffs), offset=lp.auto),
lp.ValueArg("nboxes,aligned_nboxes,tgt_base_ibox", np.int32),
lp.ValueArg("nsources", np.int32),
"..."
] + gather_loopy_source_arguments(self.source_kernels
+ (self.expansion,)),
name=self.name,
assumptions="nsrc_boxes>=1",
silenced_warnings="write_race(write_expn*)",
default_offset=lp.auto,
fixed_parameters=dict(dim=self.dim,
strength_count=self.strength_count),
lang_version=MOST_RECENT_LANGUAGE_VERSION)
for knl in self.source_kernels:
loopy_knl = knl.prepare_loopy_kernel(loopy_knl)
loopy_knl = lp.tag_inames(loopy_knl, "idim*:unr")
return loopy_knl
def get_optimized_kernel(self):
# FIXME
knl = self.get_kernel()
knl = lp.split_iname(knl, "isrc_box", 16, outer_tag="g.0")
knl = self._allow_redundant_execution_of_knl_scaling(knl)
return knl
def __call__(self, queue, **kwargs):
"""
:arg expansions:
:arg source_boxes:
:arg box_source_starts:
:arg box_source_counts_nonchild:
:arg centers:
:arg sources:
:arg strengths:
:arg rscale:
"""
centers = kwargs.pop("centers")
# "1" may be passed for rscale, which won't have its type
# meaningfully inferred. Make the type of rscale explicit.
rscale = centers.dtype.type(kwargs.pop("rscale"))
knl = self.get_cached_optimized_kernel()
return knl(queue, centers=centers, rscale=rscale, **kwargs)
# }}}
# {{{ P2E from CSR-like interaction list
[docs]class P2EFromCSR(P2EBase):
default_name = "p2e_from_csr"
def get_kernel(self):
ncoeffs = len(self.expansion)
from sumpy.tools import gather_loopy_source_arguments
arguments = (
[
lp.GlobalArg("sources", None, shape=(self.dim, "nsources"),
dim_tags="sep,c"),
lp.GlobalArg("strengths", None, shape="strength_count, nsources",
dim_tags="sep,C"),
lp.GlobalArg("source_box_starts,source_box_lists",
None, shape=None, offset=lp.auto),
lp.GlobalArg("box_source_starts,box_source_counts_nonchild",
None, shape=None),
lp.GlobalArg("centers", None, shape="dim, naligned_boxes"),
lp.GlobalArg("tgt_expansions", None,
shape=("ntgt_level_boxes", ncoeffs), offset=lp.auto),
lp.ValueArg("naligned_boxes,ntgt_level_boxes,tgt_base_ibox",
np.int32),
lp.ValueArg("nsources", np.int32),
"..."
] + gather_loopy_source_arguments(self.source_kernels
+ (self.expansion,)))
loopy_knl = lp.make_kernel(
[
"{[itgt_box]: 0<=itgt_box<ntgt_boxes}",
"{[isrc_box]: isrc_box_start<=isrc_box<isrc_box_stop}",
"{[isrc]: isrc_start<=isrc<isrc_end}",
"{[idim]: 0<=idim<dim}",
],
["""
for itgt_box
<> tgt_ibox = target_boxes[itgt_box]
<> center[idim] = centers[idim, tgt_ibox] {id=fetch_center}
<> isrc_box_start = source_box_starts[itgt_box]
<> isrc_box_stop = source_box_starts[itgt_box+1]
for isrc_box
<> src_ibox = source_box_lists[isrc_box]
<> isrc_start = box_source_starts[src_ibox]
<> isrc_end = isrc_start+box_source_counts_nonchild[src_ibox]
for isrc
<> a[idim] = center[idim] - sources[idim, isrc] \
{dup=idim}
"""] + [f"""
<> strength_{i} = strengths[{i}, isrc]
""" for i in set(self.strength_usage)] + self.get_loopy_instructions() + ["""
end
end"""] + [f"""
tgt_expansions[tgt_ibox - tgt_base_ibox, {coeffidx}] = \
simul_reduce(sum, (isrc_box, isrc), coeff{coeffidx}) \
{{id_prefix=write_expn}}
""" for coeffidx in range(ncoeffs)] + ["""
end
"""],
arguments,
name=self.name,
assumptions="ntgt_boxes>=1",
silenced_warnings="write_race(write_expn*)",
default_offset=lp.auto,
fixed_parameters=dict(dim=self.dim,
strength_count=self.strength_count),
lang_version=MOST_RECENT_LANGUAGE_VERSION)
for knl in self.source_kernels:
loopy_knl = knl.prepare_loopy_kernel(loopy_knl)
loopy_knl = lp.tag_inames(loopy_knl, "idim*:unr")
return loopy_knl
def get_optimized_kernel(self):
# FIXME
knl = self.get_kernel()
knl = lp.split_iname(knl, "itgt_box", 16, outer_tag="g.0")
knl = self._allow_redundant_execution_of_knl_scaling(knl)
return knl
def __call__(self, queue, **kwargs):
"""
:arg expansions:
:arg source_boxes:
:arg box_source_starts:
:arg box_source_counts_nonchild:
:arg centers:
:arg sources:
:arg strengths:
:arg rscale:
"""
knl = self.get_cached_optimized_kernel()
centers = kwargs.pop("centers")
# "1" may be passed for rscale, which won't have its type
# meaningfully inferred. Make the type of rscale explicit.
rscale = centers.dtype.type(kwargs.pop("rscale"))
return knl(queue, centers=centers, rscale=rscale, **kwargs)
# }}}
# vim: foldmethod=marker