def combine(dtypes):
# dtypes may just be a generator expr
dtypes = list(dtypes)
from loopy.types import LoopyType, NumpyType
assert all(isinstance(dtype, LoopyType) for dtype in dtypes)
if not all(isinstance(dtype, NumpyType) for dtype in dtypes):
from pytools import is_single_valued, single_valued
if not is_single_valued(dtypes):
raise TypeInferenceFailure(
"Nothing known about operations between '%s'"
% ", ".join(str(dt) for dt in dtypes))
return single_valued(dtypes)
dtypes = [dtype.dtype for dtype in dtypes]
result = dtypes.pop()
while dtypes:
other = dtypes.pop()
if result.fields is None and other.fields is None:
if (result, other) in [
(np.int32, np.float32), (np.float32, np.int32)]:
# numpy makes this a double. I disagree.
result = np.dtype(np.float32)
else:
result = (
np.empty(0, dtype=result)
+ np.empty(0, dtype=other)
).dtype
elif result.fields is None and other.fields is not None:
# assume the non-native type takes over
# (This is used for vector types.)
result = other
elif result.fields is not None and other.fields is None:
# assume the non-native type takes over
# (This is used for vector types.)
pass
else:
if result is not other:
raise TypeInferenceFailure(
"nothing known about result of operation on "
"'%s' and '%s'" % (result, other))
return NumpyType(result)
def __init__(self, basis=None, metric_matrix=None):
"""
:arg basis: A sequence of names of basis vectors, or an integer (the
number of dimensions) to use the default names ``e0`` through ``eN``.
:arg metric_matrix: See :attr:`metric_matrix`.
If *None*, the Euclidean metric is assumed.
"""
if basis is None and metric_matrix is None:
raise TypeError("at least one of 'basis' and 'metric_matrix' "
"must be passed")
if basis is None:
basis = int(metric_matrix.shape[0])
from numbers import Integral
if isinstance(basis, Integral):
basis = ["e%d" % i for i in range(basis)]
if metric_matrix is None:
metric_matrix = np.eye(len(basis), dtype=np.object)
from pytools import all
if not (
len(metric_matrix.shape) == 2
and
all(dim == len(basis) for dim in metric_matrix.shape)):
raise ValueError("metric_matrix has the wrong shape")
self.basis_names = basis
self.metric_matrix = metric_matrix
def get_next_step(self, available_names, done_insns):
from pytools import all, argmax2
available_insns = [(insn, insn.priority) for insn in self.instructions
if insn not in done_insns and all(
dep.name in available_names
for dep in insn.get_dependencies())]
if not available_insns:
raise self.NoInstructionAvailable
from pytools import flatten
discardable_vars = set(available_names) - set(
flatten([dep.name for dep in insn.get_dependencies()]
for insn in self.instructions if insn not in done_insns))
# {{{ make sure results do not get discarded
dm = mappers.DependencyMapper(composite_leaves=False)
def remove_result_variable(result_expr):
# The extra dependency mapper run is necessary
# because, for instance, subscripts can make it
# into the result expression, which then does
# not consist of just variables.
for var in dm(result_expr):
assert isinstance(var, Variable)
discardable_vars.discard(var.name)
obj_array_vectorize(remove_result_variable, self.result)
# }}}
return argmax2(available_insns), discardable_vars
def _get_reduction_source(
ctx, out_type, out_type_size,
neutral, reduce_expr, map_expr, parsed_args,
name="reduce_kernel", preamble="", arg_prep="",
device=None, max_group_size=None):
if device is not None:
devices = [device]
else:
devices = ctx.devices
# {{{ compute group size
def get_dev_group_size(device):
# dirty fix for the RV770 boards
max_work_group_size = device.max_work_group_size
if "RV770" in device.name:
max_work_group_size = 64
# compute lmem limit
from pytools import div_ceil
lmem_wg_size = div_ceil(max_work_group_size, out_type_size)
result = min(max_work_group_size, lmem_wg_size)
# round down to power of 2
from pyopencl.tools import bitlog2
return 2**bitlog2(result)
group_size = min(get_dev_group_size(dev) for dev in devices)
if max_group_size is not None:
group_size = min(max_group_size, group_size)
# }}}
from mako.template import Template
from pytools import all
from pyopencl.characterize import has_double_support
src = str(Template(KERNEL).render(
out_type=out_type,
arguments=", ".join(arg.declarator() for arg in parsed_args),
group_size=group_size,
neutral=neutral,
reduce_expr=_process_code_for_macro(reduce_expr),
map_expr=_process_code_for_macro(map_expr),
name=name,
preamble=preamble,
arg_prep=arg_prep,
double_support=all(has_double_support(dev) for dev in devices),
))
from pytools import Record
class ReductionInfo(Record):
pass
return ReductionInfo(
context=ctx,
source=src,
group_size=group_size)
def __init__(self, basis=None, metric_matrix=None):
"""
:arg basis: A sequence of names of basis vectors, or an integer (the
number of dimensions) to use the default names ``e0`` through ``eN``.
:arg metric_matrix: See :attr:`metric_matrix`.
If *None*, the Euclidean metric is assumed.
"""
if basis is None and metric_matrix is None:
raise TypeError("at least one of 'basis' and 'metric_matrix' "
"must be passed")
if basis is None:
basis = int(metric_matrix.shape[0])
from numbers import Integral
if isinstance(basis, Integral):
basis = ["e%d" % i for i in range(basis)]
if metric_matrix is None:
metric_matrix = np.eye(len(basis), dtype=np.object)
from pytools import all
if not (len(metric_matrix.shape) == 2
and all(dim == len(basis) for dim in metric_matrix.shape)):
raise ValueError("metric_matrix has the wrong shape")
self.basis_names = basis
self.metric_matrix = metric_matrix
def get_expr_dataset(self, expression, description=None, unit=None):
"""Prepare a time-series dataset for a given expression.
@arg expression: A C{pymbolic} expression that may involve
the time-series variables and the constants in this :class:`LogManager`.
If there is data from multiple ranks for a quantity occuring in
this expression, an aggregator may have to be specified.
@return: C{(description, unit, table)}, where C{table}
is a list of tuples C{(tick_nbr, value)}.
Aggregators are specified as follows:
- C{qty.min}, C{qty.max}, C{qty.avg}, C{qty.sum}, C{qty.norm2}
- C{qty[rank_nbr]}
- C{qty.loc}
"""
parsed = self._parse_expr(expression)
parsed, dep_data = self._get_expr_dep_data(parsed)
# aggregate table data
for dd in dep_data:
table = self.get_table(dd.name)
table.sort(["step"])
dd.table = table.aggregated(["step"], "value", dd.agg_func).data
# evaluate unit and description, if necessary
if unit is None:
from pymbolic import substitute, parse
unit_dict = dict((dd.varname, dd.qdat.unit) for dd in dep_data)
from pytools import all
if all(v is not None for v in six.itervalues(unit_dict)):
unit_dict = dict(
(k, parse(v)) for k, v in six.iteritems(unit_dict))
unit = substitute(parsed, unit_dict)
else:
unit = None
if description is None:
description = expression
# compile and evaluate
from pymbolic import compile
compiled = compile(parsed, [dd.varname for dd in dep_data])
data = []
for key, values in _join_by_first_of_tuple(dd.table
for dd in dep_data):
try:
data.append((key, compiled(*values)))
except ZeroDivisionError:
pass
return (description, unit, data)
def get_expr_dataset(self, expression, description=None, unit=None):
"""Prepare a time-series dataset for a given expression.
@arg expression: A C{pymbolic} expression that may involve
the time-series variables and the constants in this :class:`LogManager`.
If there is data from multiple ranks for a quantity occuring in
this expression, an aggregator may have to be specified.
@return: C{(description, unit, table)}, where C{table}
is a list of tuples C{(tick_nbr, value)}.
Aggregators are specified as follows:
- C{qty.min}, C{qty.max}, C{qty.avg}, C{qty.sum}, C{qty.norm2}
- C{qty[rank_nbr]}
- C{qty.loc}
"""
parsed = self._parse_expr(expression)
parsed, dep_data = self._get_expr_dep_data(parsed)
# aggregate table data
for dd in dep_data:
table = self.get_table(dd.name)
table.sort(["step"])
dd.table = table.aggregated(["step"], "value", dd.agg_func).data
# evaluate unit and description, if necessary
if unit is None:
from pymbolic import substitute, parse
unit_dict = dict((dd.varname, dd.qdat.unit) for dd in dep_data)
from pytools import all
if all(v is not None for v in six.itervalues(unit_dict)):
unit_dict = dict((k, parse(v)) for k, v in six.iteritems(unit_dict))
unit = substitute(parsed, unit_dict)
else:
unit = None
if description is None:
description = expression
# compile and evaluate
from pymbolic import compile
compiled = compile(parsed, [dd.varname for dd in dep_data])
data = []
for key, values in _join_by_first_of_tuple(dd.table for dd in dep_data):
try:
data.append((key, compiled(*values)))
except ZeroDivisionError:
pass
return (description, unit, data)
def map_sum(self, expr):
dtypes = []
small_integer_dtypes = []
for child in expr.children:
dtype = self.rec(child)
if is_integer(child) and abs(child) < 1024:
small_integer_dtypes.append(dtype)
else:
dtypes.append(dtype)
from pytools import all
if all(dtype.kind == "i" for dtype in dtypes):
dtypes.extend(small_integer_dtypes)
return self.combine(dtypes)
def get_contained_fluxes(self, expr):
from hedge.optemplate.mappers import FluxCollector
contained_flux_ops = FluxCollector()(expr)
from hedge.optemplate.operators import WholeDomainFluxOperator
from pytools import all
assert all(isinstance(op, WholeDomainFluxOperator)
for op in contained_flux_ops), \
"not all flux operators were of the expected type"
return [self.FluxRecord(
flux_expr=wdflux,
dependencies=set(wdflux.interior_deps) | set(wdflux.boundary_deps),
repr_op=wdflux.repr_op())
for wdflux in contained_flux_ops]
def get_contained_fluxes(self, expr):
from hedge.optemplate.mappers import FluxCollector
contained_flux_ops = FluxCollector()(expr)
from hedge.optemplate.operators import WholeDomainFluxOperator
from pytools import all
assert all(isinstance(op, WholeDomainFluxOperator)
for op in contained_flux_ops), \
"not all flux operators were of the expected type"
return [
self.FluxRecord(flux_expr=wdflux,
dependencies=set(wdflux.interior_deps)
| set(wdflux.boundary_deps),
repr_op=wdflux.repr_op())
for wdflux in contained_flux_ops
]
def get_next_step(self, available_names, done_insns):
from pytools import all, argmax2
available_insns = [
(insn, insn.priority) for insn in self.instructions
if insn not in done_insns
and all(dep.name in available_names
for dep in insn.get_dependencies())]
if not available_insns:
raise self.NoInstructionAvailable
needed_vars = set([
dep.name
for insn in self.instructions
if insn not in done_insns
for dep in insn.get_dependencies()
])
discardable_vars = set(available_names) - needed_vars
# {{{ make sure results do not get discarded
from pytools.obj_array import with_object_array_or_scalar
from pytential.symbolic.mappers import DependencyMapper
dm = DependencyMapper(composite_leaves=False)
def remove_result_variable(result_expr):
# The extra dependency mapper run is necessary
# because, for instance, subscripts can make it
# into the result expression, which then does
# not consist of just variables.
for var in dm(result_expr):
from pymbolic.primitives import Variable
assert isinstance(var, Variable)
discardable_vars.discard(var.name)
with_object_array_or_scalar(remove_result_variable, self.result)
# }}}
return argmax2(available_insns), discardable_vars
def get_next_step(self, available_names, done_insns):
from pytools import all, argmax2
available_insns = [(insn, insn.priority) for insn in self.instructions
if insn not in done_insns and all(
dep.name in available_names
for dep in insn.get_dependencies())]
if not available_insns:
raise self.NoInstructionAvailable
needed_vars = set([
dep.name for insn in self.instructions if insn not in done_insns
for dep in insn.get_dependencies()
])
discardable_vars = set(available_names) - needed_vars
# {{{ make sure results do not get discarded
from pytools.obj_array import with_object_array_or_scalar
from pytential.symbolic.mappers import DependencyMapper
dm = DependencyMapper(composite_leaves=False)
def remove_result_variable(result_expr):
# The extra dependency mapper run is necessary
# because, for instance, subscripts can make it
# into the result expression, which then does
# not consist of just variables.
for var in dm(result_expr):
from pymbolic.primitives import Variable
assert isinstance(var, Variable)
discardable_vars.discard(var.name)
with_object_array_or_scalar(remove_result_variable, self.result)
# }}}
return argmax2(available_insns), discardable_vars
def __init__(self,
ctx,
dtype,
scan_expr,
neutral=None,
name_prefix="scan",
options=[],
preamble="",
devices=None):
if isinstance(self, ExclusiveScanKernel) and neutral is None:
raise ValueError(
"neutral element is required for exclusive scan")
self.context = ctx
dtype = self.dtype = np.dtype(dtype)
self.neutral = neutral
if devices is None:
devices = ctx.devices
self.devices = devices
max_wg_size = min(dev.max_work_group_size for dev in self.devices)
# Thrust says these are good for GT200
self.scan_wg_size = min(max_wg_size, 128)
self.update_wg_size = min(max_wg_size, 256)
if self.scan_wg_size < 16:
# Hello, Apple CPU. Nice to see you.
self.scan_wg_seq_batches = 128 # FIXME: guesswork
else:
self.scan_wg_seq_batches = 6
from pytools import all
from pyopencl.characterize import has_double_support
kw_values = dict(preamble=preamble,
name_prefix=name_prefix,
scan_type=dtype_to_ctype(dtype),
scan_expr=scan_expr,
neutral=neutral,
double_support=all(
has_double_support(dev) for dev in devices))
scan_intervals_src = str(
SCAN_INTERVALS_SOURCE.render(
wg_size=self.scan_wg_size,
wg_seq_batches=self.scan_wg_seq_batches,
**kw_values))
scan_intervals_prg = cl.Program(ctx,
scan_intervals_src).build(options)
self.scan_intervals_knl = getattr(scan_intervals_prg,
name_prefix + "_scan_intervals")
self.scan_intervals_knl.set_scalar_arg_dtypes(
(None, np.uint32, np.uint32, None, None))
final_update_src = str(
self.final_update_tp.render(wg_size=self.update_wg_size,
**kw_values))
final_update_prg = cl.Program(self.context,
final_update_src).build(options)
self.final_update_knl = getattr(final_update_prg,
name_prefix + "_final_update")
self.final_update_knl.set_scalar_arg_dtypes(
(None, np.uint32, np.uint32, None))
def find_temporary_scope(kernel):
logger.debug("%s: mark local temporaries" % kernel.name)
new_temp_vars = {}
from loopy.kernel.data import (LocalIndexTagBase, GroupIndexTag,
temp_var_scope)
import loopy as lp
writers = kernel.writer_map()
for temp_var in six.itervalues(kernel.temporary_variables):
# Only fill out for variables that do not yet know if they're
# local. (I.e. those generated by implicit temporary generation.)
if temp_var.scope is not lp.auto:
new_temp_vars[temp_var.name] = temp_var
continue
my_writers = writers.get(temp_var.name, [])
desired_scope_per_insn = []
for insn_id in my_writers:
insn = kernel.id_to_insn[insn_id]
# A write race will emerge if:
#
# - the variable is local
# and
# - the instruction is run across more inames (locally) parallel
# than are reflected in the assignee indices.
locparallel_compute_inames = _get_compute_inames_tagged(
kernel, insn, LocalIndexTagBase)
locparallel_assignee_inames = _get_assignee_inames_tagged(
kernel, insn, LocalIndexTagBase, temp_var.name)
grpparallel_compute_inames = _get_compute_inames_tagged(
kernel, insn, GroupIndexTag)
grpparallel_assignee_inames = _get_assignee_inames_tagged(
kernel, insn, GroupIndexTag, temp_var.name)
assert locparallel_assignee_inames <= locparallel_compute_inames
assert grpparallel_assignee_inames <= grpparallel_compute_inames
desired_scope = temp_var_scope.PRIVATE
for iname_descr, scope_descr, apin, cpin, scope in [
("local", "local", locparallel_assignee_inames,
locparallel_compute_inames, temp_var_scope.LOCAL),
("group", "global", grpparallel_assignee_inames,
grpparallel_compute_inames, temp_var_scope.GLOBAL),
]:
if (apin != cpin and bool(locparallel_assignee_inames)):
warn_with_kernel(kernel, "write_race_local(%s)" % insn_id,
"instruction '%s' looks invalid: "
"it assigns to indices based on %s IDs, but "
"its temporary '%s' cannot be made %s because "
"a write race across the iname(s) '%s' would emerge. "
"(Do you need to add an extra iname to your prefetch?)"
% (insn_id, iname_descr, temp_var.name, scope_descr,
", ".join(cpin - apin)),
WriteRaceConditionWarning)
if (apin == cpin
# doesn't want to be in this scope if there aren't any
# parallel inames of that kind:
and bool(cpin)):
desired_scope = max(desired_scope, scope)
break
desired_scope_per_insn.append(desired_scope)
if not desired_scope_per_insn:
if temp_var.initializer is None:
warn_with_kernel(kernel, "temp_to_write(%s)" % temp_var.name,
"temporary variable '%s' never written, eliminating"
% temp_var.name, LoopyAdvisory)
else:
raise LoopyError("temporary variable '%s': never written, "
"cannot automatically determine scope"
% temp_var.name)
continue
overall_scope = max(desired_scope_per_insn)
from pytools import all
if not all(iscope == overall_scope for iscope in desired_scope_per_insn):
raise LoopyError("not all instructions agree on the "
"the desired scope (private/local/global) of the "
"temporary '%s'" % temp_var.name)
new_temp_vars[temp_var.name] = temp_var.copy(scope=overall_scope)
return kernel.copy(temporary_variables=new_temp_vars)
def map_logical_and(self, expr):
from pytools import all
return all(self.rec(ch) for ch in expr.children)
def map_logical_and(self, expr):
from pytools import all
return all(self.rec(ch) for ch in expr.children)
def get_reduction_source(
ctx, out_type, out_type_size,
neutral, reduce_expr, map_expr, arguments,
name="reduce_kernel", preamble="",
device=None, max_group_size=None):
if device is not None:
devices = [device]
else:
devices = ctx.devices
# {{{ compute group size
def get_dev_group_size(device):
return min(
device.max_work_group_size,
(device.local_mem_size + out_type_size - 1)
// out_type_size)
group_size = min(
get_dev_group_size(dev) for dev in devices)
if max_group_size is not None:
group_size = min(max_group_size, group_size)
# }}}
# {{{ compute synchronization-less group size
def get_dev_no_sync_size(device):
try:
return device.warp_size_nv
except:
if "nvidia" in device.vendor.lower():
from warnings import warn
warn("Reduction might be unnecessarily slow: "
"can't query warp size on Nvidia device")
return 1
no_sync_size = min(get_dev_no_sync_size(dev) for dev in devices)
# }}}
from mako.template import Template
from pytools import all
from pyopencl.characterize import has_double_support
src = str(Template(KERNEL).render(
out_type=out_type,
arguments=arguments,
group_size=group_size,
no_sync_size=no_sync_size,
neutral=neutral,
reduce_expr=reduce_expr,
map_expr=map_expr,
name=name,
preamble=preamble,
double_support=all(
has_double_support(dev) for dev in devices)
))
from pytools import Record
class ReductionInfo(Record):
pass
return ReductionInfo(
context=ctx,
source=src,
group_size=group_size)
def make_ref_args(kernel, impl_arg_info, queue, parameters):
import pyopencl as cl
import pyopencl.array as cl_array
from loopy.kernel.data import ValueArg, GlobalArg, ImageArg, TemporaryVariable
from pymbolic import evaluate
ref_args = {}
ref_arg_data = []
for arg in impl_arg_info:
kernel_arg = kernel.impl_arg_to_arg.get(arg.name)
if arg.arg_class is ValueArg:
if arg.offset_for_name:
continue
arg_value = parameters[arg.name]
try:
argv_dtype = arg_value.dtype
except AttributeError:
argv_dtype = None
if argv_dtype != arg.dtype:
arg_value = arg.dtype.numpy_dtype.type(arg_value)
ref_args[arg.name] = arg_value
ref_arg_data.append(None)
elif arg.arg_class is GlobalArg or arg.arg_class is ImageArg:
if arg.shape is None or any(saxis is None for saxis in arg.shape):
raise LoopyError("array '%s' needs known shape to use automatic "
"testing" % arg.name)
shape = evaluate_shape(arg.unvec_shape, parameters)
dtype = kernel_arg.dtype
is_output = arg.base_name in kernel.get_written_variables()
if arg.arg_class is ImageArg:
storage_array = ary = cl_array.empty(
queue, shape, dtype, order="C")
numpy_strides = None
alloc_size = None
strides = None
else:
strides = evaluate(arg.unvec_strides, parameters)
from pytools import all
assert all(s > 0 for s in strides)
alloc_size = sum(astrd*(alen-1)
for alen, astrd in zip(shape, strides)) + 1
if dtype is None:
raise LoopyError("dtype for argument '%s' is not yet "
"known. Perhaps you want to use "
"loopy.add_dtypes "
"or loopy.infer_argument_dtypes?"
% arg.name)
itemsize = dtype.itemsize
numpy_strides = [itemsize*s for s in strides]
storage_array = cl_array.empty(queue, alloc_size, dtype)
if is_output and arg.arg_class is ImageArg:
raise LoopyError("write-mode images not supported in "
"automatic testing")
fill_rand(storage_array)
if arg.arg_class is ImageArg:
# must be contiguous
pre_run_ary = pre_run_storage_array = storage_array.copy()
ref_args[arg.name] = cl.image_from_array(
queue.context, ary.get())
else:
pre_run_storage_array = storage_array.copy()
ary = cl_array.as_strided(storage_array, shape, numpy_strides)
pre_run_ary = cl_array.as_strided(
pre_run_storage_array, shape, numpy_strides)
ref_args[arg.name] = ary
ref_arg_data.append(
TestArgInfo(
name=arg.name,
ref_array=ary,
ref_storage_array=storage_array,
ref_pre_run_array=pre_run_ary,
ref_pre_run_storage_array=pre_run_storage_array,
ref_shape=shape,
ref_strides=strides,
ref_alloc_size=alloc_size,
ref_numpy_strides=numpy_strides,
needs_checking=is_output))
elif arg.arg_class is TemporaryVariable:
# global temporary, handled by invocation logic
pass
else:
raise LoopyError("arg type not understood")
return ref_args, ref_arg_data
def make_args(kernel, impl_arg_info, queue, ref_arg_data, parameters):
import pyopencl as cl
import pyopencl.array as cl_array
from loopy.kernel.data import ValueArg, GlobalArg, ImageArg, TemporaryVariable
from pymbolic import evaluate
args = {}
for arg, arg_desc in zip(impl_arg_info, ref_arg_data):
kernel_arg = kernel.impl_arg_to_arg.get(arg.name)
if arg.arg_class is ValueArg:
arg_value = parameters[arg.name]
try:
argv_dtype = arg_value.dtype
except AttributeError:
argv_dtype = None
if argv_dtype != arg.dtype:
arg_value = arg.dtype.numpy_dtype.type(arg_value)
args[arg.name] = arg_value
elif arg.arg_class is ImageArg:
if arg.name in kernel.get_written_variables():
raise NotImplementedError("write-mode images not supported in "
"automatic testing")
shape = evaluate_shape(arg.unvec_shape, parameters)
assert shape == arg_desc.ref_shape
# must be contiguous
args[arg.name] = cl.image_from_array(
queue.context, arg_desc.ref_pre_run_array.get())
elif arg.arg_class is GlobalArg:
shape = evaluate(arg.unvec_shape, parameters)
strides = evaluate(arg.unvec_strides, parameters)
dtype = kernel_arg.dtype
itemsize = dtype.itemsize
numpy_strides = [itemsize*s for s in strides]
assert all(s > 0 for s in strides)
alloc_size = sum(astrd*(alen-1)
for alen, astrd in zip(shape, strides)) + 1
# use contiguous array to transfer to host
host_ref_contig_array = arg_desc.ref_pre_run_storage_array.get()
# use device shape/strides
from pyopencl.compyte.array import as_strided
host_ref_array = as_strided(host_ref_contig_array,
arg_desc.ref_shape, arg_desc.ref_numpy_strides)
# flatten the thing
host_ref_flat_array = host_ref_array.flatten()
# create host array with test shape (but not strides)
host_contig_array = np.empty(shape, dtype=dtype)
common_len = min(
len(host_ref_flat_array),
len(host_contig_array.ravel()))
host_contig_array.ravel()[:common_len] = \
host_ref_flat_array[:common_len]
# create host array with test shape and storage layout
host_storage_array = np.empty(alloc_size, dtype)
host_array = as_strided(
host_storage_array, shape, numpy_strides)
host_array[...] = host_contig_array
host_contig_array = arg_desc.ref_storage_array.get()
storage_array = cl_array.to_device(queue, host_storage_array)
ary = cl_array.as_strided(storage_array, shape, numpy_strides)
args[arg.name] = ary
arg_desc.test_storage_array = storage_array
arg_desc.test_array = ary
arg_desc.test_shape = shape
arg_desc.test_strides = strides
arg_desc.test_numpy_strides = numpy_strides
arg_desc.test_alloc_size = alloc_size
elif arg.arg_class is TemporaryVariable:
# global temporary, handled by invocation logic
pass
else:
raise LoopyError("arg type not understood")
return args
def mark_local_temporaries(kernel):
logger.debug("%s: mark local temporaries" % kernel.name)
new_temp_vars = {}
from loopy.kernel.data import LocalIndexTagBase
import loopy as lp
writers = kernel.writer_map()
from loopy.symbolic import get_dependencies
for temp_var in six.itervalues(kernel.temporary_variables):
# Only fill out for variables that do not yet know if they're
# local. (I.e. those generated by implicit temporary generation.)
if temp_var.is_local is not lp.auto:
new_temp_vars[temp_var.name] = temp_var
continue
my_writers = writers.get(temp_var.name, [])
wants_to_be_local_per_insn = []
for insn_id in my_writers:
insn = kernel.id_to_insn[insn_id]
# A write race will emerge if:
#
# - the variable is local
# and
# - the instruction is run across more inames (locally) parallel
# than are reflected in the assignee indices.
locparallel_compute_inames = set(iname
for iname in kernel.insn_inames(insn_id)
if isinstance(kernel.iname_to_tag.get(iname), LocalIndexTagBase))
locparallel_assignee_inames = set(iname
for _, assignee_indices in insn.assignees_and_indices()
for iname in get_dependencies(assignee_indices)
& kernel.all_inames()
if isinstance(kernel.iname_to_tag.get(iname), LocalIndexTagBase))
assert locparallel_assignee_inames <= locparallel_compute_inames
if (locparallel_assignee_inames != locparallel_compute_inames
and bool(locparallel_assignee_inames)):
warn(kernel, "write_race_local(%s)" % insn_id,
"instruction '%s' looks invalid: "
"it assigns to indices based on local IDs, but "
"its temporary '%s' cannot be made local because "
"a write race across the iname(s) '%s' would emerge. "
"(Do you need to add an extra iname to your prefetch?)"
% (insn_id, temp_var.name, ", ".join(
locparallel_compute_inames
- locparallel_assignee_inames)),
WriteRaceConditionWarning)
wants_to_be_local_per_insn.append(
locparallel_assignee_inames == locparallel_compute_inames
# doesn't want to be local if there aren't any
# parallel inames:
and bool(locparallel_compute_inames))
if not wants_to_be_local_per_insn:
warn(kernel, "temp_to_write(%s)" % temp_var.name,
"temporary variable '%s' never written, eliminating"
% temp_var.name, LoopyAdvisory)
continue
is_local = any(wants_to_be_local_per_insn)
from pytools import all
if not all(wtbl == is_local for wtbl in wants_to_be_local_per_insn):
raise LoopyError("not all instructions agree on whether "
"temporary '%s' should be in local memory" % temp_var.name)
new_temp_vars[temp_var.name] = temp_var.copy(is_local=is_local)
return kernel.copy(temporary_variables=new_temp_vars)
def get_reduction_source(ctx,
out_type,
out_type_size,
neutral,
reduce_expr,
map_expr,
arguments,
name="reduce_kernel",
preamble="",
device=None,
max_group_size=None):
if device is not None:
devices = [device]
else:
devices = ctx.devices
# {{{ compute group size
def get_dev_group_size(device):
# dirty fix for the RV770 boards
max_work_group_size = device.max_work_group_size
if "RV770" in device.name:
max_work_group_size = 64
return min(max_work_group_size,
(device.local_mem_size + out_type_size - 1) //
out_type_size)
group_size = min(get_dev_group_size(dev) for dev in devices)
if max_group_size is not None:
group_size = min(max_group_size, group_size)
# }}}
# {{{ compute synchronization-less group size
def get_dev_no_sync_size(device):
from pyopencl.characterize import get_simd_group_size
result = get_simd_group_size(device, out_type_size)
if result is None:
from warnings import warn
warn("Reduction might be unnecessarily slow: "
"can't query SIMD group size")
return 1
return result
no_sync_size = min(get_dev_no_sync_size(dev) for dev in devices)
# }}}
from mako.template import Template
from pytools import all
from pyopencl.characterize import has_double_support, has_amd_double_support
src = str(
Template(KERNEL).render(
out_type=out_type,
arguments=arguments,
group_size=group_size,
no_sync_size=no_sync_size,
neutral=neutral,
reduce_expr=reduce_expr,
map_expr=map_expr,
name=name,
preamble=preamble,
double_support=all(has_double_support(dev) for dev in devices),
amd_double_support=all(
has_amd_double_support(dev) for dev in devices)))
from pytools import Record
class ReductionInfo(Record):
pass
return ReductionInfo(context=ctx, source=src, group_size=group_size)
def get_reduction_source(
ctx, out_type, out_type_size,
neutral, reduce_expr, map_expr, arguments,
name="reduce_kernel", preamble="",
device=None, max_group_size=None):
if device is not None:
devices = [device]
else:
devices = ctx.devices
# {{{ compute group size
def get_dev_group_size(device):
# dirty fix for the RV770 boards
max_work_group_size = device.max_work_group_size
if "RV770" in device.name:
max_work_group_size = 64
# compute lmem limit
from pytools import div_ceil
lmem_wg_size = div_ceil(max_work_group_size, out_type_size)
result = min(max_work_group_size, lmem_wg_size)
# round down to power of 2
from pyopencl.tools import bitlog2
return 2**bitlog2(result)
group_size = min(get_dev_group_size(dev) for dev in devices)
if max_group_size is not None:
group_size = min(max_group_size, group_size)
# }}}
# {{{ compute synchronization-less group size
def get_dev_no_sync_size(device):
from pyopencl.characterize import get_simd_group_size
result = get_simd_group_size(device, out_type_size)
if result is None:
from warnings import warn
warn("Reduction might be unnecessarily slow: "
"can't query SIMD group size")
return 1
return result
no_sync_size = min(get_dev_no_sync_size(dev) for dev in devices)
# }}}
from mako.template import Template
from pytools import all
from pyopencl.characterize import has_double_support, has_amd_double_support
src = str(Template(KERNEL).render(
out_type=out_type,
arguments=arguments,
group_size=group_size,
no_sync_size=no_sync_size,
neutral=neutral,
reduce_expr=reduce_expr,
map_expr=map_expr,
name=name,
preamble=preamble,
double_support=all(
has_double_support(dev) for dev in devices),
amd_double_support=all(
has_amd_double_support(dev) for dev in devices)
))
from pytools import Record
class ReductionInfo(Record):
pass
return ReductionInfo(
context=ctx,
source=src,
group_size=group_size)
def make_ref_args(kernel, impl_arg_info, queue, parameters):
import pyopencl as cl
import pyopencl.array as cl_array
from loopy.kernel.data import ValueArg, GlobalArg, ImageArg, \
TemporaryVariable, ConstantArg
from pymbolic import evaluate
ref_args = {}
ref_arg_data = []
for arg in impl_arg_info:
kernel_arg = kernel.impl_arg_to_arg.get(arg.name)
if arg.arg_class is ValueArg:
if arg.offset_for_name:
continue
arg_value = parameters[arg.name]
try:
argv_dtype = arg_value.dtype
except AttributeError:
argv_dtype = None
if argv_dtype != arg.dtype:
arg_value = arg.dtype.numpy_dtype.type(arg_value)
ref_args[arg.name] = arg_value
ref_arg_data.append(None)
elif arg.arg_class is GlobalArg or arg.arg_class is ImageArg \
or arg.arg_class is ConstantArg:
if arg.shape is None or any(saxis is None for saxis in arg.shape):
raise LoopyError(
"array '%s' needs known shape to use automatic "
"testing" % arg.name)
shape = evaluate_shape(arg.unvec_shape, parameters)
dtype = kernel_arg.dtype
is_output = arg.base_name in kernel.get_written_variables()
if arg.arg_class is ImageArg:
storage_array = ary = cl_array.empty(queue,
shape,
dtype,
order="C")
numpy_strides = None
alloc_size = None
strides = None
else:
strides = evaluate(arg.unvec_strides, parameters)
from pytools import all
assert all(s > 0 for s in strides)
alloc_size = sum(astrd * (alen - 1)
for alen, astrd in zip(shape, strides)) + 1
if dtype is None:
raise LoopyError("dtype for argument '%s' is not yet "
"known. Perhaps you want to use "
"loopy.add_dtypes "
"or loopy.infer_argument_dtypes?" %
arg.name)
itemsize = dtype.itemsize
numpy_strides = [itemsize * s for s in strides]
storage_array = cl_array.empty(queue, alloc_size, dtype)
if is_output and arg.arg_class is ImageArg:
raise LoopyError("write-mode images not supported in "
"automatic testing")
fill_rand(storage_array)
if arg.arg_class is ImageArg:
# must be contiguous
pre_run_ary = pre_run_storage_array = storage_array.copy()
ref_args[arg.name] = cl.image_from_array(
queue.context, ary.get())
else:
pre_run_storage_array = storage_array.copy()
ary = cl_array.as_strided(storage_array, shape, numpy_strides)
pre_run_ary = cl_array.as_strided(pre_run_storage_array, shape,
numpy_strides)
ref_args[arg.name] = ary
ref_arg_data.append(
TestArgInfo(name=arg.name,
ref_array=ary,
ref_storage_array=storage_array,
ref_pre_run_array=pre_run_ary,
ref_pre_run_storage_array=pre_run_storage_array,
ref_shape=shape,
ref_strides=strides,
ref_alloc_size=alloc_size,
ref_numpy_strides=numpy_strides,
needs_checking=is_output))
elif arg.arg_class is TemporaryVariable:
# global temporary, handled by invocation logic
pass
else:
raise LoopyError("arg type not understood")
return ref_args, ref_arg_data
def make_args(kernel, impl_arg_info, queue, ref_arg_data, parameters):
import pyopencl as cl
import pyopencl.array as cl_array
from loopy.kernel.data import ValueArg, GlobalArg, ImageArg,\
TemporaryVariable, ConstantArg
from pymbolic import evaluate
args = {}
for arg, arg_desc in zip(impl_arg_info, ref_arg_data):
kernel_arg = kernel.impl_arg_to_arg.get(arg.name)
if arg.arg_class is ValueArg:
arg_value = parameters[arg.name]
try:
argv_dtype = arg_value.dtype
except AttributeError:
argv_dtype = None
if argv_dtype != arg.dtype:
arg_value = arg.dtype.numpy_dtype.type(arg_value)
args[arg.name] = arg_value
elif arg.arg_class is ImageArg:
if arg.name in kernel.get_written_variables():
raise NotImplementedError("write-mode images not supported in "
"automatic testing")
shape = evaluate_shape(arg.unvec_shape, parameters)
assert shape == arg_desc.ref_shape
# must be contiguous
args[arg.name] = cl.image_from_array(
queue.context, arg_desc.ref_pre_run_array.get())
elif arg.arg_class is GlobalArg or\
arg.arg_class is ConstantArg:
shape = evaluate(arg.unvec_shape, parameters)
strides = evaluate(arg.unvec_strides, parameters)
dtype = kernel_arg.dtype
itemsize = dtype.itemsize
numpy_strides = [itemsize * s for s in strides]
assert all(s > 0 for s in strides)
alloc_size = sum(astrd * (alen - 1)
for alen, astrd in zip(shape, strides)) + 1
# use contiguous array to transfer to host
host_ref_contig_array = arg_desc.ref_pre_run_storage_array.get()
# use device shape/strides
from pyopencl.compyte.array import as_strided
host_ref_array = as_strided(host_ref_contig_array,
arg_desc.ref_shape,
arg_desc.ref_numpy_strides)
# flatten the thing
host_ref_flat_array = host_ref_array.flatten()
# create host array with test shape (but not strides)
host_contig_array = np.empty(shape, dtype=dtype)
common_len = min(len(host_ref_flat_array),
len(host_contig_array.ravel()))
host_contig_array.ravel()[:common_len] = \
host_ref_flat_array[:common_len]
# create host array with test shape and storage layout
host_storage_array = np.empty(alloc_size, dtype)
host_array = as_strided(host_storage_array, shape, numpy_strides)
host_array[...] = host_contig_array
host_contig_array = arg_desc.ref_storage_array.get()
storage_array = cl_array.to_device(queue, host_storage_array)
ary = cl_array.as_strided(storage_array, shape, numpy_strides)
args[arg.name] = ary
arg_desc.test_storage_array = storage_array
arg_desc.test_array = ary
arg_desc.test_shape = shape
arg_desc.test_strides = strides
arg_desc.test_numpy_strides = numpy_strides
arg_desc.test_alloc_size = alloc_size
elif arg.arg_class is TemporaryVariable:
# global temporary, handled by invocation logic
pass
else:
raise LoopyError("arg type not understood")
return args
def _get_reduction_source(ctx,
out_type,
out_type_size,
neutral,
reduce_expr,
map_expr,
parsed_args,
name="reduce_kernel",
preamble="",
device=None,
max_group_size=None):
if device is not None:
devices = [device]
else:
devices = ctx.devices
# {{{ compute group size
def get_dev_group_size(device):
# dirty fix for the RV770 boards
max_work_group_size = device.max_work_group_size
if "RV770" in device.name:
max_work_group_size = 64
# compute lmem limit
from pytools import div_ceil
lmem_wg_size = div_ceil(max_work_group_size, out_type_size)
result = min(max_work_group_size, lmem_wg_size)
# round down to power of 2
from pyopencl.tools import bitlog2
return 2**bitlog2(result)
group_size = min(get_dev_group_size(dev) for dev in devices)
if max_group_size is not None:
group_size = min(max_group_size, group_size)
# }}}
# {{{ compute synchronization-less group size
def get_dev_no_sync_size(device):
from pyopencl.characterize import get_simd_group_size
result = get_simd_group_size(device, out_type_size)
if result is None:
from warnings import warn
warn("Reduction might be unnecessarily slow: "
"can't query SIMD group size")
return 1
return result
no_sync_size = min(get_dev_no_sync_size(dev) for dev in devices)
# }}}
from mako.template import Template
from pytools import all
from pyopencl.characterize import has_double_support
src = str(
Template(KERNEL).render(
out_type=out_type,
arguments=", ".join(arg.declarator() for arg in parsed_args),
group_size=group_size,
no_sync_size=no_sync_size,
neutral=neutral,
reduce_expr=reduce_expr,
map_expr=map_expr,
name=name,
preamble=preamble,
double_support=all(has_double_support(dev) for dev in devices),
))
from pytools import Record
class ReductionInfo(Record):
pass
return ReductionInfo(context=ctx, source=src, group_size=group_size)
def find_temporary_scope(kernel):
logger.debug("%s: mark local temporaries" % kernel.name)
new_temp_vars = {}
from loopy.kernel.data import (LocalIndexTagBase, GroupIndexTag,
temp_var_scope)
import loopy as lp
writers = kernel.writer_map()
for temp_var in six.itervalues(kernel.temporary_variables):
# Only fill out for variables that do not yet know if they're
# local. (I.e. those generated by implicit temporary generation.)
if temp_var.scope is not lp.auto:
new_temp_vars[temp_var.name] = temp_var
continue
my_writers = writers.get(temp_var.name, [])
desired_scope_per_insn = []
for insn_id in my_writers:
insn = kernel.id_to_insn[insn_id]
# A write race will emerge if:
#
# - the variable is local
# and
# - the instruction is run across more inames (locally) parallel
# than are reflected in the assignee indices.
locparallel_compute_inames = _get_compute_inames_tagged(
kernel, insn, LocalIndexTagBase)
locparallel_assignee_inames = _get_assignee_inames_tagged(
kernel, insn, LocalIndexTagBase, temp_var.name)
grpparallel_compute_inames = _get_compute_inames_tagged(
kernel, insn, GroupIndexTag)
grpparallel_assignee_inames = _get_assignee_inames_tagged(
kernel, insn, GroupIndexTag, temp_var.name)
assert locparallel_assignee_inames <= locparallel_compute_inames
assert grpparallel_assignee_inames <= grpparallel_compute_inames
desired_scope = temp_var_scope.PRIVATE
for iname_descr, scope_descr, apin, cpin, scope in [
("local", "local", locparallel_assignee_inames,
locparallel_compute_inames, temp_var_scope.LOCAL),
("group", "global", grpparallel_assignee_inames,
grpparallel_compute_inames, temp_var_scope.GLOBAL),
]:
if (apin != cpin and bool(locparallel_assignee_inames)):
warn_with_kernel(kernel, "write_race_local(%s)" % insn_id,
"instruction '%s' looks invalid: "
"it assigns to indices based on %s IDs, but "
"its temporary '%s' cannot be made %s because "
"a write race across the iname(s) '%s' would emerge. "
"(Do you need to add an extra iname to your prefetch?)"
% (insn_id, iname_descr, temp_var.name, scope_descr,
", ".join(cpin - apin)),
WriteRaceConditionWarning)
if (apin == cpin
# doesn't want to be in this scope if there aren't any
# parallel inames of that kind:
and bool(cpin)):
desired_scope = max(desired_scope, scope)
break
desired_scope_per_insn.append(desired_scope)
if not desired_scope_per_insn:
if temp_var.initializer is None:
warn_with_kernel(kernel, "temp_to_write(%s)" % temp_var.name,
"temporary variable '%s' never written, eliminating"
% temp_var.name, LoopyAdvisory)
else:
raise LoopyError("temporary variable '%s': never written, "
"cannot automatically determine scope"
% temp_var.name)
continue
overall_scope = max(desired_scope_per_insn)
from pytools import all
if not all(iscope == overall_scope for iscope in desired_scope_per_insn):
raise LoopyError("not all instructions agree on the "
"the desired scope (private/local/global) of the "
"temporary '%s'" % temp_var.name)
new_temp_vars[temp_var.name] = temp_var.copy(scope=overall_scope)
return kernel.copy(temporary_variables=new_temp_vars)
def __init__(self, ctx, dtype,
scan_expr, neutral=None,
name_prefix="scan", options=[], preamble="", devices=None):
if isinstance(self, ExclusiveScanKernel) and neutral is None:
raise ValueError("neutral element is required for exclusive scan")
self.context = ctx
dtype = self.dtype = np.dtype(dtype)
self.neutral = neutral
if devices is None:
devices = ctx.devices
self.devices = devices
max_wg_size = min(dev.max_work_group_size for dev in self.devices)
# loop to find suitable workgrouop size
trip_count = 0
while True:
# Thrust says these are good for GT200
self.scan_wg_size = min(max_wg_size, 128)
self.update_wg_size = min(max_wg_size, 256)
if self.scan_wg_size < 16:
# Hello, Apple CPU. Nice to see you.
self.scan_wg_seq_batches = 128 # FIXME: guesswork
else:
self.scan_wg_seq_batches = 6
from pytools import all
from pyopencl.characterize import has_double_support
kw_values = dict(
preamble=preamble,
name_prefix=name_prefix,
scan_type=dtype_to_ctype(dtype),
scan_expr=scan_expr,
neutral=neutral,
double_support=all(
has_double_support(dev) for dev in devices)
)
scan_intervals_src = str(SCAN_INTERVALS_SOURCE.render(
wg_size=self.scan_wg_size,
wg_seq_batches=self.scan_wg_seq_batches,
**kw_values))
scan_intervals_prg = cl.Program(ctx, scan_intervals_src).build(options)
self.scan_intervals_knl = getattr(
scan_intervals_prg,
name_prefix+"_scan_intervals")
self.scan_intervals_knl.set_scalar_arg_dtypes(
(None, np.uint32, np.uint32, None, None))
kernel_max_wg_size = self.scan_intervals_knl.get_work_group_info(
cl.kernel_work_group_info.WORK_GROUP_SIZE,
ctx.devices[0])
if self.scan_wg_size <= kernel_max_wg_size:
break
else:
max_wg_size = kernel_max_wg_size
trip_count += 1
assert trip_count <= 2
final_update_src = str(self.final_update_tp.render(
wg_size=self.update_wg_size,
**kw_values))
final_update_prg = cl.Program(self.context, final_update_src).build(options)
self.final_update_knl = getattr(
final_update_prg,
name_prefix+"_final_update")
self.final_update_knl.set_scalar_arg_dtypes(
(None, np.uint32, np.uint32, None))
