def add_dependency(kernel, insn_match, dependency):
"""Add the instruction dependency *dependency* to the instructions matched
by *insn_match*.
*insn_match* may be any instruction id match understood by
:func:`loopy.match.parse_match`.
"""
if dependency not in kernel.id_to_insn:
raise LoopyError(
"cannot add dependency on non-existent instruction ID '%s'" %
dependency)
def add_dep(insn):
new_deps = insn.depends_on
added_deps = frozenset([dependency])
if new_deps is None:
new_deps = added_deps
else:
new_deps = new_deps | added_deps
return insn.copy(depends_on=new_deps)
return map_instructions(kernel, insn_match, add_dep)
def generate_unroll_loop(codegen_state, sched_index):
kernel = codegen_state.kernel
iname = kernel.schedule[sched_index].iname
bounds = kernel.get_iname_bounds(iname, constants_only=True)
from loopy.isl_helpers import (
static_max_of_pw_aff, static_value_of_pw_aff)
from loopy.symbolic import pw_aff_to_expr
length_aff = static_max_of_pw_aff(bounds.size, constants_only=True)
if not length_aff.is_cst():
raise LoopyError(
"length of unrolled loop '%s' is not a constant, "
"cannot unroll")
length = int(pw_aff_to_expr(length_aff))
try:
lower_bound_aff = static_value_of_pw_aff(
bounds.lower_bound_pw_aff.coalesce(),
constants_only=False)
except Exception as e:
raise type(e)("while finding lower bound of '%s': " % iname)
result = []
for i in range(length):
idx_aff = lower_bound_aff + i
new_codegen_state = codegen_state.fix(iname, idx_aff)
result.append(
build_loop_nest(new_codegen_state, sched_index+1))
return merge_codegen_results(codegen_state, result)
def _is_racing_iname_tag(tv, tag):
from loopy.kernel.data import (temp_var_scope,
LocalIndexTagBase, GroupIndexTag, ConcurrentTag, auto)
if tv.scope == temp_var_scope.PRIVATE:
return (
isinstance(tag, ConcurrentTag)
and not isinstance(tag, (LocalIndexTagBase, GroupIndexTag)))
elif tv.scope == temp_var_scope.LOCAL:
return (
isinstance(tag, ConcurrentTag)
and not isinstance(tag, GroupIndexTag))
elif tv.scope == temp_var_scope.GLOBAL:
return isinstance(tag, ConcurrentTag)
elif tv.scope == auto:
raise LoopyError("scope of temp var '%s' has not yet been"
"determined" % tv.name)
else:
raise ValueError("unexpected value of temp_var.scope for "
"temporary variable '%s'" % tv.name)
def map_call(self, expr, return_tuple=False):
from pymbolic.primitives import Variable
identifier = expr.function
if isinstance(identifier, Variable):
identifier = identifier.name
if identifier in ["indexof", "indexof_vec"]:
return [self.kernel.index_dtype]
def none_if_empty(d):
if d:
d, = d
return d
else:
return None
arg_dtypes = tuple(none_if_empty(self.rec(par)) for par in expr.parameters)
if None in arg_dtypes:
return []
mangle_result = self.kernel.mangle_function(identifier, arg_dtypes)
if return_tuple:
if mangle_result is not None:
return [mangle_result.result_dtypes]
else:
if mangle_result is not None:
if len(mangle_result.result_dtypes) != 1 and not return_tuple:
raise LoopyError("functions with more or fewer than one "
"return value may only be used in direct assignments")
return [mangle_result.result_dtypes[0]]
raise RuntimeError("unable to resolve "
"function '%s' with %d given arguments"
% (identifier, len(arg_dtypes)))
def map_reduction(self, expr, return_tuple=False):
rec_result = self.rec(expr.expr)
if rec_result:
rec_result, = rec_result
result = expr.operation.result_dtypes(self.kernel, rec_result,
expr.inames)
else:
result = expr.operation.result_dtypes(self.kernel, None,
expr.inames)
if result is None:
return []
if return_tuple:
return [result]
else:
if len(result) != 1 and not return_tuple:
raise LoopyError(
"reductions with more or fewer than one "
"return value may only be used in direct assignments")
return [result[0]]
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, ArrayArg, 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 ArrayArg 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)
alloc_size = sum(astrd * (alen - 1) if astrd != 0 else 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 %s not understood" % type(arg))
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, ArrayArg, 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 ArrayArg 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]
alloc_size = sum(astrd * (alen - 1) if astrd != 0 else 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 _check_for_unused_hw_axes_in_kernel_chunk(kernel, sched_index=None):
from loopy.schedule import (CallKernel, RunInstruction,
Barrier, EnterLoop, LeaveLoop, ReturnFromKernel,
get_insn_ids_for_block_at, gather_schedule_block)
if sched_index is None:
group_axes = set()
local_axes = set()
i = 0
loop_end_i = past_end_i = len(kernel.schedule)
else:
assert isinstance(kernel.schedule[sched_index], CallKernel)
_, past_end_i = gather_schedule_block(kernel.schedule, sched_index)
group_size, local_size = kernel.get_grid_sizes_for_insn_ids_as_exprs(
get_insn_ids_for_block_at(kernel.schedule, sched_index))
group_axes = set(ax for ax, length in enumerate(group_size))
local_axes = set(ax for ax, length in enumerate(local_size))
i = sched_index + 1
assert isinstance(kernel.schedule[past_end_i - 1], ReturnFromKernel)
loop_end_i = past_end_i - 1
# alternative: just disregard length-1 dimensions?
from loopy.kernel.data import (LocalIndexTag, AutoLocalIndexTagBase,
GroupIndexTag)
while i < loop_end_i:
sched_item = kernel.schedule[i]
if isinstance(sched_item, CallKernel):
i = _check_for_unused_hw_axes_in_kernel_chunk(kernel, i)
elif isinstance(sched_item, RunInstruction):
insn = kernel.id_to_insn[sched_item.insn_id]
i += 1
if insn.boostable:
continue
group_axes_used = set()
local_axes_used = set()
for iname in kernel.insn_inames(insn):
ltags = kernel.iname_tags_of_type(iname, LocalIndexTag, max_num=1)
gtags = kernel.iname_tags_of_type(iname, GroupIndexTag, max_num=1)
altags = kernel.iname_tags_of_type(
iname, AutoLocalIndexTagBase, max_num=1)
if ltags:
tag, = ltags
local_axes_used.add(tag.axis)
elif gtags:
tag, = gtags
group_axes_used.add(tag.axis)
elif altags:
raise LoopyError("auto local tag encountered")
if group_axes != group_axes_used:
raise LoopyError("instruction '%s' does not use all group hw axes "
"(available: %s used:%s)"
% (insn.id,
",".join(str(i) for i in group_axes),
",".join(str(i) for i in group_axes_used)))
if local_axes != local_axes_used:
raise LoopyError("instruction '%s' does not use all local hw axes "
"(available: %s used:%s)"
% (insn.id,
",".join(str(i) for i in local_axes),
",".join(str(i) for i in local_axes_used)))
elif isinstance(sched_item, (Barrier, EnterLoop, LeaveLoop)):
i += 1
continue
else:
raise TypeError(
"schedule item not understood: %s" % type(sched_item).__name__)
return past_end_i
def check_loop_priority_inames_known(kernel):
for prio in kernel.loop_priority:
for iname in prio:
if iname not in kernel.all_inames():
raise LoopyError("unknown iname '%s' in loop priorities" % iname)
def __init__(self,
assignees,
expression,
id=None,
depends_on=None,
depends_on_is_final=None,
groups=None,
conflicts_with_groups=None,
no_sync_with=None,
within_inames_is_final=None,
within_inames=None,
boostable=None,
boostable_into=None,
tags=None,
temp_var_types=None,
priority=0,
predicates=frozenset(),
insn_deps=None,
insn_deps_is_final=None,
forced_iname_deps=None,
forced_iname_deps_is_final=None):
super(CallInstruction, self).__init__(
id=id,
depends_on=depends_on,
depends_on_is_final=depends_on_is_final,
groups=groups,
conflicts_with_groups=conflicts_with_groups,
no_sync_with=no_sync_with,
within_inames_is_final=within_inames_is_final,
within_inames=within_inames,
boostable=boostable,
boostable_into=boostable_into,
priority=priority,
predicates=predicates,
tags=tags,
insn_deps=insn_deps,
insn_deps_is_final=insn_deps_is_final,
forced_iname_deps=forced_iname_deps,
forced_iname_deps_is_final=forced_iname_deps_is_final)
from pymbolic.primitives import Call
from loopy.symbolic import Reduction
if not isinstance(expression,
(Call, Reduction)) and expression is not None:
raise LoopyError("'expression' argument to CallInstruction "
"must be a function call")
from loopy.symbolic import parse
if isinstance(assignees, str):
assignees = parse(assignees)
if not isinstance(assignees, tuple):
raise LoopyError("'assignees' argument to CallInstruction "
"must be a tuple or a string parseable to a tuple"
"--got '%s'" % type(assignees).__name__)
if isinstance(expression, str):
expression = parse(expression)
from pymbolic.primitives import Variable, Subscript
from loopy.symbolic import LinearSubscript
for assignee in assignees:
if not isinstance(assignee,
(Variable, Subscript, LinearSubscript)):
raise LoopyError("invalid lvalue '%s'" % assignee)
self.assignees = assignees
self.expression = expression
if temp_var_types is None:
self.temp_var_types = (None, ) * len(self.assignees)
else:
self.temp_var_types = temp_var_types
def collect_common_factors_on_increment(kernel, var_name, vary_by_axes=()):
assert isinstance(kernel, LoopKernel)
# FIXME: Does not understand subst rules for now
if kernel.substitutions:
from loopy.transform.subst import expand_subst
kernel = expand_subst(kernel)
if var_name in kernel.temporary_variables:
var_descr = kernel.temporary_variables[var_name]
elif var_name in kernel.arg_dict:
var_descr = kernel.arg_dict[var_name]
else:
raise NameError("array '%s' was not found" % var_name)
# {{{ check/normalize vary_by_axes
if isinstance(vary_by_axes, str):
vary_by_axes = vary_by_axes.split(",")
from loopy.kernel.array import ArrayBase
if isinstance(var_descr, ArrayBase):
if var_descr.dim_names is not None:
name_to_index = {
name: idx
for idx, name in enumerate(var_descr.dim_names)
}
else:
name_to_index = {}
def map_ax_name_to_index(ax):
if isinstance(ax, str):
try:
return name_to_index[ax]
except KeyError:
raise LoopyError("axis name '%s' not understood " % ax)
else:
return ax
vary_by_axes = [map_ax_name_to_index(ax) for ax in vary_by_axes]
if (vary_by_axes
and (min(vary_by_axes) < 0
or max(vary_by_axes) > var_descr.num_user_axes())):
raise LoopyError("vary_by_axes refers to out-of-bounds axis index")
# }}}
from pymbolic.mapper.substitutor import make_subst_func
from pymbolic.primitives import (Sum, Product, is_zero, flattened_sum,
flattened_product, Subscript, Variable)
from loopy.symbolic import (get_dependencies, SubstitutionMapper,
UnidirectionalUnifier)
# {{{ common factor key list maintenance
# list of (index_key, common factors found)
common_factors = []
def find_unifiable_cf_index(index_key):
for i, (key, _val) in enumerate(common_factors):
unif = UnidirectionalUnifier(
lhs_mapping_candidates=get_dependencies(key))
unif_result = unif(key, index_key)
if unif_result:
assert len(unif_result) == 1
return i, unif_result[0]
return None, None
def extract_index_key(access_expr):
if isinstance(access_expr, Variable):
return ()
elif isinstance(access_expr, Subscript):
index = access_expr.index_tuple
return tuple(index[ax] for ax in vary_by_axes)
else:
raise ValueError("unexpected type of access_expr")
def is_assignee(insn):
return var_name in insn.assignee_var_names()
def iterate_as(cls, expr):
if isinstance(expr, cls):
yield from expr.children
else:
yield expr
# }}}
# {{{ find common factors
from loopy.kernel.data import Assignment
for insn in kernel.instructions:
if not is_assignee(insn):
continue
if not isinstance(insn, Assignment):
raise LoopyError("'%s' modified by non-single-assignment" %
var_name)
lhs = insn.assignee
rhs = insn.expression
if is_zero(rhs):
continue
index_key = extract_index_key(lhs)
cf_index, unif_result = find_unifiable_cf_index(index_key)
if cf_index is None:
# {{{ doesn't exist yet
assert unif_result is None
my_common_factors = None
for term in iterate_as(Sum, rhs):
if term == lhs:
continue
for part in iterate_as(Product, term):
if var_name in get_dependencies(part):
raise LoopyError("unexpected dependency on '%s' "
"in RHS of instruction '%s'" %
(var_name, insn.id))
product_parts = set(iterate_as(Product, term))
if my_common_factors is None:
my_common_factors = product_parts
else:
my_common_factors = my_common_factors & product_parts
if my_common_factors is not None:
common_factors.append((index_key, my_common_factors))
# }}}
else:
# {{{ match, filter existing common factors
_, my_common_factors = common_factors[cf_index]
unif_subst_map = SubstitutionMapper(
make_subst_func(unif_result.lmap))
for term in iterate_as(Sum, rhs):
if term == lhs:
continue
for part in iterate_as(Product, term):
if var_name in get_dependencies(part):
raise LoopyError("unexpected dependency on '%s' "
"in RHS of instruction '%s'" %
(var_name, insn.id))
product_parts = set(iterate_as(Product, term))
my_common_factors = {
cf
for cf in my_common_factors
if unif_subst_map(cf) in product_parts
}
common_factors[cf_index] = (index_key, my_common_factors)
# }}}
# }}}
common_factors = [(ik, cf) for ik, cf in common_factors if cf]
if not common_factors:
raise LoopyError("no common factors found")
# {{{ remove common factors
new_insns = []
for insn in kernel.instructions:
if not isinstance(insn, Assignment) or not is_assignee(insn):
new_insns.append(insn)
continue
index_key = extract_index_key(insn.assignee)
lhs = insn.assignee
rhs = insn.expression
if is_zero(rhs):
new_insns.append(insn)
continue
index_key = extract_index_key(lhs)
cf_index, unif_result = find_unifiable_cf_index(index_key)
if cf_index is None:
new_insns.append(insn)
continue
_, my_common_factors = common_factors[cf_index]
unif_subst_map = SubstitutionMapper(make_subst_func(unif_result.lmap))
mapped_my_common_factors = {
unif_subst_map(cf)
for cf in my_common_factors
}
new_sum_terms = []
for term in iterate_as(Sum, rhs):
if term == lhs:
new_sum_terms.append(term)
continue
new_sum_terms.append(
flattened_product([
part for part in iterate_as(Product, term)
if part not in mapped_my_common_factors
]))
new_insns.append(insn.copy(expression=flattened_sum(new_sum_terms)))
# }}}
# {{{ substitute common factors into usage sites
def find_substitution(expr):
if isinstance(expr, Subscript):
v = expr.aggregate.name
elif isinstance(expr, Variable):
v = expr.name
else:
return expr
if v != var_name:
return expr
index_key = extract_index_key(expr)
cf_index, unif_result = find_unifiable_cf_index(index_key)
unif_subst_map = SubstitutionMapper(make_subst_func(unif_result.lmap))
_, my_common_factors = common_factors[cf_index]
if my_common_factors is not None:
return flattened_product(
[unif_subst_map(cf) for cf in my_common_factors] + [expr])
else:
return expr
insns = new_insns
new_insns = []
subm = SubstitutionMapper(find_substitution)
for insn in insns:
if not isinstance(insn, Assignment) or is_assignee(insn):
new_insns.append(insn)
continue
new_insns.append(insn.with_transformed_expressions(subm))
# }}}
return kernel.copy(instructions=new_insns)
def gen_decls(name_suffix, shape, strides, unvec_shape, unvec_strides,
stride_arg_axes, dtype, user_index):
"""
:arg unvec_shape: shape tuple
that accounts for :class:`loopy.kernel.array.VectorArrayDimTag`
in a scalar manner
:arg unvec_strides: strides tuple
that accounts for :class:`loopy.kernel.array.VectorArrayDimTag`
in a scalar manner
:arg stride_arg_axes: a tuple *(user_axis, impl_axis, unvec_impl_axis)*
:arg user_index: A tuple representing a (user-facing)
multi-dimensional subscript. This is filled in with
concrete integers when known (such as for separate-array
dim tags), and with *None* where the index won't be
known until run time.
"""
if dtype is None:
dtype = self.dtype
user_axis = len(user_index)
num_user_axes = self.num_user_axes(require_answer=False)
if num_user_axes is None or user_axis >= num_user_axes:
# {{{ recursion base case
full_name = self.name + name_suffix
stride_args = []
strides = list(strides)
unvec_strides = list(unvec_strides)
# generate stride arguments, yielded later to keep array first
for stride_user_axis, stride_impl_axis, stride_unvec_impl_axis \
in stride_arg_axes:
stride_name = full_name + "_stride%d" % stride_user_axis
from pymbolic import var
strides[stride_impl_axis] = \
unvec_strides[stride_unvec_impl_axis] = \
var(stride_name)
stride_args.append(
ImplementedDataInfo(
target=target,
name=stride_name,
dtype=index_dtype,
arg_class=ValueArg,
stride_for_name_and_axis=(full_name,
stride_impl_axis),
is_written=False))
yield ImplementedDataInfo(target=target,
name=full_name,
base_name=self.name,
arg_class=type(self),
dtype=dtype,
shape=shape,
strides=tuple(strides),
unvec_shape=unvec_shape,
unvec_strides=tuple(unvec_strides),
allows_offset=bool(self.offset),
is_written=is_written)
import loopy as lp
if self.offset is lp.auto:
offset_name = full_name + "_offset"
yield ImplementedDataInfo(target=target,
name=offset_name,
dtype=index_dtype,
arg_class=ValueArg,
offset_for_name=full_name,
is_written=False)
yield from stride_args
# }}}
return
dim_tag = self.dim_tags[user_axis]
if isinstance(dim_tag, FixedStrideArrayDimTag):
if array_shape is None:
new_shape_axis = None
else:
new_shape_axis = array_shape[user_axis]
import loopy as lp
if dim_tag.stride is lp.auto:
new_stride_arg_axes = stride_arg_axes \
+ ((user_axis, len(strides), len(unvec_strides)),)
# repaired above when final array name is known
# (and stride argument is created)
new_stride_axis = None
else:
new_stride_arg_axes = stride_arg_axes
new_stride_axis = dim_tag.stride
yield from gen_decls(name_suffix, shape + (new_shape_axis, ),
strides + (new_stride_axis, ),
unvec_shape + (new_shape_axis, ),
unvec_strides + (new_stride_axis, ),
new_stride_arg_axes, dtype,
user_index + (None, ))
elif isinstance(dim_tag, SeparateArrayArrayDimTag):
shape_i = array_shape[user_axis]
if not is_integer(shape_i):
raise LoopyError("shape of '%s' has non-constant "
"integer axis %d (0-based)" %
(self.name, user_axis))
for i in range(shape_i):
yield from gen_decls(name_suffix + "_s%d" % i, shape,
strides, unvec_shape, unvec_strides,
stride_arg_axes, dtype,
user_index + (i, ))
elif isinstance(dim_tag, VectorArrayDimTag):
shape_i = array_shape[user_axis]
if not is_integer(shape_i):
raise LoopyError("shape of '%s' has non-constant "
"integer axis %d (0-based)" %
(self.name, user_axis))
yield from gen_decls(
name_suffix,
shape,
strides,
unvec_shape + (shape_i, ),
# vectors always have stride 1
unvec_strides + (1, ),
stride_arg_axes,
target.vector_dtype(dtype, shape_i),
user_index + (None, ))
else:
raise LoopyError(
"unsupported array dim implementation tag '%s' "
"in array '%s'" % (dim_tag, self.name))
def __init__(self, name, dtype=None, shape=(), address_space=None,
dim_tags=None, offset=0, dim_names=None, strides=None, order=None,
base_indices=None, storage_shape=None,
base_storage=None, initializer=None, read_only=False,
_base_storage_access_may_be_aliasing=False, **kwargs):
"""
:arg dtype: :class:`loopy.auto` or a :class:`numpy.dtype`
:arg shape: :class:`loopy.auto` or a shape tuple
:arg base_indices: :class:`loopy.auto` or a tuple of base indices
"""
scope = kwargs.pop("scope", None)
if scope is not None:
warn("Passing 'scope' is deprecated. Use 'address_space' instead.",
DeprecationWarning, stacklevel=2)
if address_space is not None:
raise ValueError("only one of 'scope' and 'address_space' "
"may be specified")
else:
address_space = scope
del scope
if address_space is None:
address_space = auto
if address_space is None:
raise LoopyError(
"temporary variable '%s': "
"address_space must not be None"
% name)
if initializer is None:
pass
elif isinstance(initializer, np.ndarray):
if offset != 0:
raise LoopyError(
"temporary variable '%s': "
"offset must be 0 if initializer specified"
% name)
from loopy.types import NumpyType, to_loopy_type
if dtype is auto or dtype is None:
dtype = NumpyType(initializer.dtype)
elif to_loopy_type(dtype) != to_loopy_type(initializer.dtype):
raise LoopyError(
"temporary variable '%s': "
"dtype of initializer does not match "
"dtype of array."
% name)
if shape is auto:
shape = initializer.shape
else:
raise LoopyError(
"temporary variable '%s': "
"initializer must be None or a numpy array"
% name)
if order is None:
order = "C"
if base_indices is None:
base_indices = (0,) * len(shape)
if not read_only and initializer is not None:
raise LoopyError(
"temporary variable '%s': "
"read-write variables with initializer "
"are not currently supported "
"(did you mean to set read_only=True?)"
% name)
if base_storage is not None and initializer is not None:
raise LoopyError(
"temporary variable '%s': "
"base_storage and initializer are "
"mutually exclusive"
% name)
if base_storage is None and _base_storage_access_may_be_aliasing:
raise LoopyError(
"temporary variable '%s': "
"_base_storage_access_may_be_aliasing option, but no "
"base_storage given!"
% name)
ArrayBase.__init__(self, name=intern(name),
dtype=dtype, shape=shape, strides=strides,
dim_tags=dim_tags, offset=offset, dim_names=dim_names,
order=order,
base_indices=base_indices,
address_space=address_space,
storage_shape=storage_shape,
base_storage=base_storage,
initializer=initializer,
read_only=read_only,
_base_storage_access_may_be_aliasing=(
_base_storage_access_may_be_aliasing),
**kwargs)
def map_local_hw_index(self, expr, enclosing_prec, type_context):
raise LoopyError("plain C does not have group hw axes")
def map_call(self, expr, enclosing_prec, type_context):
from pymbolic.primitives import Variable, Subscript
from pymbolic.mapper.stringifier import PREC_NONE
identifier = expr.function
# {{{ implement indexof, indexof_vec
if identifier.name in ["indexof", "indexof_vec"]:
if len(expr.parameters) != 1:
raise LoopyError("%s takes exactly one argument" %
identifier.name)
arg, = expr.parameters
if not isinstance(arg, Subscript):
raise LoopyError("argument to %s must be a subscript" %
identifier.name)
ary = self.find_array(arg)
from loopy.kernel.array import get_access_info
from pymbolic import evaluate
access_info = get_access_info(
self.kernel.target, ary, arg.index,
lambda expr: evaluate(expr, self.codegen_state.var_subst_map),
self.codegen_state.vectorization_info)
from loopy.kernel.data import ImageArg
if isinstance(ary, ImageArg):
raise LoopyError("%s does not support images" %
identifier.name)
if identifier.name == "indexof":
return access_info.subscripts[0]
elif identifier.name == "indexof_vec":
from loopy.kernel.array import VectorArrayDimTag
ivec = None
for iaxis, dim_tag in enumerate(ary.dim_tags):
if isinstance(dim_tag, VectorArrayDimTag):
ivec = iaxis
if ivec is None:
return access_info.subscripts[0]
else:
return (access_info.subscripts[0] * ary.shape[ivec] +
access_info.vector_index)
else:
raise RuntimeError("should not get here")
# }}}
if isinstance(identifier, Variable):
identifier = identifier.name
par_dtypes = tuple(self.infer_type(par) for par in expr.parameters)
str_parameters = None
mangle_result = self.kernel.mangle_function(
identifier, par_dtypes, ast_builder=self.codegen_state.ast_builder)
if mangle_result is None:
raise RuntimeError(
"function '%s' unknown--"
"maybe you need to register a function mangler?" % identifier)
if len(mangle_result.result_dtypes) != 1:
raise LoopyError(
"functions with more or fewer than one return value "
"may not be used in an expression")
if mangle_result.arg_dtypes is not None:
str_parameters = [
self.rec(par, PREC_NONE,
dtype_to_type_context(self.kernel.target, tgt_dtype),
tgt_dtype) for par, par_dtype, tgt_dtype in
zip(expr.parameters, par_dtypes, mangle_result.arg_dtypes)
]
else:
# /!\ FIXME For some functions (e.g. 'sin'), it makes sense to
# propagate the type context here. But for many others, it does
# not. Using the inferred type as a stopgap for now.
str_parameters = [
self.rec(par,
PREC_NONE,
type_context=dtype_to_type_context(
self.kernel.target, par_dtype))
for par, par_dtype in zip(expr.parameters, par_dtypes)
]
from warnings import warn
warn(
"Calling function '%s' with unknown C signature--"
"return CallMangleInfo.arg_dtypes" % identifier, LoopyWarning)
from loopy.codegen import SeenFunction
self.codegen_state.seen_functions.add(
SeenFunction(identifier, mangle_result.target_name,
mangle_result.arg_dtypes or par_dtypes))
return "%s(%s)" % (mangle_result.target_name,
", ".join(str_parameters))
def precompute(
kernel,
subst_use,
sweep_inames=[],
within=None,
storage_axes=None,
temporary_name=None,
precompute_inames=None,
precompute_outer_inames=None,
storage_axis_to_tag={},
# "None" is a valid value here, distinct from the default.
default_tag=_not_provided,
dtype=None,
fetch_bounding_box=False,
temporary_address_space=None,
compute_insn_id=None,
**kwargs):
"""Precompute the expression described in the substitution rule determined by
*subst_use* and store it in a temporary array. A precomputation needs two
things to operate, a list of *sweep_inames* (order irrelevant) and an
ordered list of *storage_axes* (whose order will describe the axis ordering
of the temporary array).
:arg subst_use: Describes what to prefetch.
The following objects may be given for *subst_use*:
* The name of the substitution rule.
* The tagged name ("name$tag") of the substitution rule.
* A list of invocations of the substitution rule.
This list of invocations, when swept across *sweep_inames*, then serves
to define the footprint of the precomputation.
Invocations may be tagged ("name$tag") to filter out a subset of the
usage sites of the substitution rule. (Namely those usage sites that
use the same tagged name.)
Invocations may be given as a string or as a
:class:`pymbolic.primitives.Expression` object.
If only one invocation is to be given, then the only entry of the list
may be given directly.
If the list of invocations generating the footprint is not given,
all (tag-matching, if desired) usage sites of the substitution rule
are used to determine the footprint.
The following cases can arise for each sweep axis:
* The axis is an iname that occurs within arguments specified at
usage sites of the substitution rule. This case is assumed covered
by the storage axes provided for the argument.
* The axis is an iname that occurs within the *value* of the rule, but not
within its arguments. A new, dedicated storage axis is allocated for
such an axis.
:arg sweep_inames: A :class:`list` of inames to be swept.
May also equivalently be a comma-separated string.
:arg within: a stack match as understood by
:func:`loopy.match.parse_stack_match`.
:arg storage_axes: A :class:`list` of inames and/or rule argument
names/indices to be used as storage axes.
May also equivalently be a comma-separated string.
:arg temporary_name:
The temporary variable name to use for storing the precomputed data.
If it does not exist, it will be created. If it does exist, its properties
(such as size, type) are checked (and updated, if possible) to match
its use.
:arg precompute_inames:
A tuple of inames to be used to carry out the precomputation.
If the specified inames do not already exist, they will be
created. If they do already exist, their loop domain is verified
against the one required for this precomputation. This tuple may
be shorter than the (provided or automatically found) *storage_axes*
tuple, in which case names will be automatically created.
May also equivalently be a comma-separated string.
:arg precompute_outer_inames: A :class:`frozenset` of inames within which
the compute instruction is nested. If *None*, make an educated guess.
May also be specified as a comma-separated string.
:arg default_tag: The :ref:`iname tag <iname-tags>` to be applied to the
inames created to perform the precomputation. The current default will
make them local axes and automatically split them to fit the work
group size, but this default will disappear in favor of simply leaving them
untagged in 2019. For 2018, a warning will be issued if no *default_tag* is
specified.
:arg compute_insn_id: The ID of the instruction generated to perform the
precomputation.
If `storage_axes` is not specified, it defaults to the arrangement
`<direct sweep axes><arguments>` with the direct sweep axes being the
slower-varying indices.
Trivial storage axes (i.e. axes of length 1 with respect to the sweep) are
eliminated.
"""
# {{{ unify temporary_address_space / temporary_scope
temporary_scope = kwargs.pop("temporary_scope", None)
from loopy.kernel.data import AddressSpace
if temporary_scope is not None:
from warnings import warn
warn(
"temporary_scope is deprecated. Use temporary_address_space instead",
DeprecationWarning,
stacklevel=2)
if temporary_address_space is not None:
raise LoopyError(
"may not specify both temporary_address_space and "
"temporary_scope")
temporary_address_space = temporary_scope
del temporary_scope
# }}}
if kwargs:
raise TypeError("unrecognized keyword arguments: %s" %
", ".join(kwargs.keys()))
# {{{ check, standardize arguments
if isinstance(sweep_inames, str):
sweep_inames = [iname.strip() for iname in sweep_inames.split(",")]
for iname in sweep_inames:
if iname not in kernel.all_inames():
raise RuntimeError("sweep iname '%s' is not a known iname" % iname)
sweep_inames = list(sweep_inames)
sweep_inames_set = frozenset(sweep_inames)
if isinstance(storage_axes, str):
storage_axes = [ax.strip() for ax in storage_axes.split(",")]
if isinstance(precompute_inames, str):
precompute_inames = [
iname.strip() for iname in precompute_inames.split(",")
]
if isinstance(precompute_outer_inames, str):
precompute_outer_inames = frozenset(
iname.strip() for iname in precompute_outer_inames.split(","))
if isinstance(subst_use, str):
subst_use = [subst_use]
footprint_generators = None
subst_name = None
subst_tag = None
from pymbolic.primitives import Variable, Call
from loopy.symbolic import parse, TaggedVariable
for use in subst_use:
if isinstance(use, str):
use = parse(use)
if isinstance(use, Call):
if footprint_generators is None:
footprint_generators = []
footprint_generators.append(use)
subst_name_as_expr = use.function
else:
subst_name_as_expr = use
if isinstance(subst_name_as_expr, TaggedVariable):
new_subst_name = subst_name_as_expr.name
new_subst_tag = subst_name_as_expr.tag
elif isinstance(subst_name_as_expr, Variable):
new_subst_name = subst_name_as_expr.name
new_subst_tag = None
else:
raise ValueError("unexpected type of subst_name")
if (subst_name, subst_tag) == (None, None):
subst_name, subst_tag = new_subst_name, new_subst_tag
else:
if (subst_name, subst_tag) != (new_subst_name, new_subst_tag):
raise ValueError("not all uses in subst_use agree "
"on rule name and tag")
from loopy.match import parse_stack_match
within = parse_stack_match(within)
try:
subst = kernel.substitutions[subst_name]
except KeyError:
raise LoopyError("substitution rule '%s' not found" % subst_name)
c_subst_name = subst_name.replace(".", "_")
# {{{ handle default_tag
from loopy.transform.data import _not_provided \
as transform_data_not_provided
if default_tag is _not_provided or default_tag is transform_data_not_provided:
# no need to warn for scalar precomputes
if sweep_inames:
from warnings import warn
warn(
"Not specifying default_tag is deprecated, and default_tag "
"will become mandatory in 2019.x. "
"Pass 'default_tag=\"l.auto\" to match the current default, "
"or Pass 'default_tag=None to leave the loops untagged, which "
"is the recommended behavior.",
DeprecationWarning,
stacklevel=(
# In this case, we came here through add_prefetch. Increase
# the stacklevel.
3 if default_tag is transform_data_not_provided else 2))
default_tag = "l.auto"
from loopy.kernel.data import parse_tag
default_tag = parse_tag(default_tag)
# }}}
# }}}
# {{{ process invocations in footprint generators, start access_descriptors
if footprint_generators:
from pymbolic.primitives import Variable, Call
access_descriptors = []
for fpg in footprint_generators:
if isinstance(fpg, Variable):
args = ()
elif isinstance(fpg, Call):
args = fpg.parameters
else:
raise ValueError("footprint generator must "
"be substitution rule invocation")
access_descriptors.append(
RuleAccessDescriptor(identifier=access_descriptor_id(
args, None),
args=args))
# }}}
# {{{ gather up invocations in kernel code, finish access_descriptors
if not footprint_generators:
rule_mapping_context = SubstitutionRuleMappingContext(
kernel.substitutions, kernel.get_var_name_generator())
invg = RuleInvocationGatherer(rule_mapping_context, kernel, subst_name,
subst_tag, within)
del rule_mapping_context
import loopy as lp
for insn in kernel.instructions:
if isinstance(insn, lp.MultiAssignmentBase):
for assignee in insn.assignees:
invg(assignee, kernel, insn)
invg(insn.expression, kernel, insn)
access_descriptors = invg.access_descriptors
if not access_descriptors:
raise RuntimeError("no invocations of '%s' found" % subst_name)
# }}}
# {{{ find inames used in arguments
expanding_usage_arg_deps = set()
for accdesc in access_descriptors:
for arg in accdesc.args:
expanding_usage_arg_deps.update(
get_dependencies(arg) & kernel.all_inames())
# }}}
var_name_gen = kernel.get_var_name_generator()
# {{{ use given / find new storage_axes
# extra axes made necessary because they don't occur in the arguments
extra_storage_axes = set(sweep_inames_set - expanding_usage_arg_deps)
from loopy.symbolic import SubstitutionRuleExpander
submap = SubstitutionRuleExpander(kernel.substitutions)
value_inames = (get_dependencies(submap(subst.expression)) -
frozenset(subst.arguments)) & kernel.all_inames()
if value_inames - expanding_usage_arg_deps < extra_storage_axes:
raise RuntimeError("unreferenced sweep inames specified: " +
", ".join(extra_storage_axes - value_inames -
expanding_usage_arg_deps))
new_iname_to_tag = {}
if storage_axes is None:
storage_axes = []
# Add sweep_inames (in given--rather than arbitrary--order) to
# storage_axes *if* they are part of extra_storage_axes.
for iname in sweep_inames:
if iname in extra_storage_axes:
extra_storage_axes.remove(iname)
storage_axes.append(iname)
if extra_storage_axes:
if (precompute_inames is not None
and len(storage_axes) < len(precompute_inames)):
raise LoopyError(
"must specify a sufficient number of "
"storage_axes to uniquely determine the meaning "
"of the given precompute_inames. (%d storage_axes "
"needed)" % len(precompute_inames))
storage_axes.extend(sorted(extra_storage_axes))
storage_axes.extend(range(len(subst.arguments)))
del extra_storage_axes
prior_storage_axis_name_dict = {}
storage_axis_names = []
storage_axis_sources = [] # number for arg#, or iname
# {{{ check for pre-existing precompute_inames
if precompute_inames is not None:
preexisting_precompute_inames = (set(precompute_inames)
& kernel.all_inames())
else:
preexisting_precompute_inames = set()
# }}}
for i, saxis in enumerate(storage_axes):
tag_lookup_saxis = saxis
if saxis in subst.arguments:
saxis = subst.arguments.index(saxis)
storage_axis_sources.append(saxis)
if isinstance(saxis, int):
# argument index
name = old_name = subst.arguments[saxis]
else:
old_name = saxis
name = "%s_%s" % (c_subst_name, old_name)
if (precompute_inames is not None and i < len(precompute_inames)
and precompute_inames[i]):
name = precompute_inames[i]
tag_lookup_saxis = name
if (name not in preexisting_precompute_inames
and var_name_gen.is_name_conflicting(name)):
raise RuntimeError("new storage axis name '%s' "
"conflicts with existing name" % name)
else:
name = var_name_gen(name)
storage_axis_names.append(name)
if name not in preexisting_precompute_inames:
new_iname_to_tag[name] = storage_axis_to_tag.get(
tag_lookup_saxis, default_tag)
prior_storage_axis_name_dict[name] = old_name
del storage_axis_to_tag
del storage_axes
del precompute_inames
# }}}
# {{{ fill out access_descriptors[...].storage_axis_exprs
access_descriptors = [
accdesc.copy(storage_axis_exprs=storage_axis_exprs(
storage_axis_sources, accdesc.args))
for accdesc in access_descriptors
]
# }}}
expanding_inames = sweep_inames_set | frozenset(expanding_usage_arg_deps)
assert expanding_inames <= kernel.all_inames()
if storage_axis_names:
# {{{ find domain to be changed
change_inames = expanding_inames | preexisting_precompute_inames
from loopy.kernel.tools import DomainChanger
domch = DomainChanger(kernel, change_inames)
if domch.leaf_domain_index is not None:
# If the sweep inames are at home in parent domains, then we'll add
# fetches with loops over copies of these parent inames that will end
# up being scheduled *within* loops over these parents.
for iname in sweep_inames_set:
if kernel.get_home_domain_index(
iname) != domch.leaf_domain_index:
raise RuntimeError(
"sweep iname '%s' is not 'at home' in the "
"sweep's leaf domain" % iname)
# }}}
abm = ArrayToBufferMap(kernel, domch.domain, sweep_inames,
access_descriptors, len(storage_axis_names))
non1_storage_axis_names = []
for i, saxis in enumerate(storage_axis_names):
if abm.non1_storage_axis_flags[i]:
non1_storage_axis_names.append(saxis)
else:
del new_iname_to_tag[saxis]
if saxis in preexisting_precompute_inames:
raise LoopyError(
"precompute axis %d (1-based) was "
"eliminated as "
"having length 1 but also mapped to existing "
"iname '%s'" % (i + 1, saxis))
mod_domain = domch.domain
# {{{ modify the domain, taking into account preexisting inames
# inames may already exist in mod_domain, add them primed to start
primed_non1_saxis_names = [
iname + "'" for iname in non1_storage_axis_names
]
mod_domain = abm.augment_domain_with_sweep(
domch.domain,
primed_non1_saxis_names,
boxify_sweep=fetch_bounding_box)
check_domain = mod_domain
for i, saxis in enumerate(non1_storage_axis_names):
var_dict = mod_domain.get_var_dict(isl.dim_type.set)
if saxis in preexisting_precompute_inames:
# add equality constraint between existing and new variable
dt, dim_idx = var_dict[saxis]
saxis_aff = isl.Aff.var_on_domain(mod_domain.space, dt,
dim_idx)
dt, dim_idx = var_dict[primed_non1_saxis_names[i]]
new_var_aff = isl.Aff.var_on_domain(mod_domain.space, dt,
dim_idx)
mod_domain = mod_domain.add_constraint(
isl.Constraint.equality_from_aff(new_var_aff - saxis_aff))
# project out the new one
mod_domain = mod_domain.project_out(dt, dim_idx, 1)
else:
# remove the prime from the new variable
dt, dim_idx = var_dict[primed_non1_saxis_names[i]]
mod_domain = mod_domain.set_dim_name(dt, dim_idx, saxis)
def add_assumptions(d):
assumption_non_param = isl.BasicSet.from_params(kernel.assumptions)
assumptions, domain = isl.align_two(assumption_non_param, d)
return assumptions & domain
# {{{ check that we got the desired domain
check_domain = add_assumptions(
check_domain.project_out_except(primed_non1_saxis_names,
[isl.dim_type.set]))
mod_check_domain = add_assumptions(mod_domain)
# re-add the prime from the new variable
var_dict = mod_check_domain.get_var_dict(isl.dim_type.set)
for saxis in non1_storage_axis_names:
dt, dim_idx = var_dict[saxis]
mod_check_domain = mod_check_domain.set_dim_name(
dt, dim_idx, saxis + "'")
mod_check_domain = mod_check_domain.project_out_except(
primed_non1_saxis_names, [isl.dim_type.set])
mod_check_domain, check_domain = isl.align_two(mod_check_domain,
check_domain)
# The modified domain can't get bigger by adding constraints
assert mod_check_domain <= check_domain
if not check_domain <= mod_check_domain:
print(check_domain)
print(mod_check_domain)
raise LoopyError("domain of preexisting inames does not match "
"domain needed for precompute")
# }}}
# {{{ check that we didn't shrink the original domain
# project out the new names from the modified domain
orig_domain_inames = list(domch.domain.get_var_dict(isl.dim_type.set))
mod_check_domain = add_assumptions(
mod_domain.project_out_except(orig_domain_inames,
[isl.dim_type.set]))
check_domain = add_assumptions(domch.domain)
mod_check_domain, check_domain = isl.align_two(mod_check_domain,
check_domain)
# The modified domain can't get bigger by adding constraints
assert mod_check_domain <= check_domain
if not check_domain <= mod_check_domain:
print(check_domain)
print(mod_check_domain)
raise LoopyError(
"original domain got shrunk by applying the precompute")
# }}}
# }}}
new_kernel_domains = domch.get_domains_with(mod_domain)
else:
# leave kernel domains unchanged
new_kernel_domains = kernel.domains
non1_storage_axis_names = []
abm = NoOpArrayToBufferMap()
kernel = kernel.copy(domains=new_kernel_domains)
# {{{ set up compute insn
if temporary_name is None:
temporary_name = var_name_gen(based_on=c_subst_name)
assignee = var(temporary_name)
if non1_storage_axis_names:
assignee = assignee[tuple(
var(iname) for iname in non1_storage_axis_names)]
# {{{ process substitutions on compute instruction
storage_axis_subst_dict = {}
for arg_name, bi in zip(storage_axis_names, abm.storage_base_indices):
if arg_name in non1_storage_axis_names:
arg = var(arg_name)
else:
arg = 0
storage_axis_subst_dict[prior_storage_axis_name_dict.get(
arg_name, arg_name)] = arg + bi
rule_mapping_context = SubstitutionRuleMappingContext(
kernel.substitutions, kernel.get_var_name_generator())
from loopy.match import parse_stack_match
expr_subst_map = RuleAwareSubstitutionMapper(
rule_mapping_context,
make_subst_func(storage_axis_subst_dict),
within=parse_stack_match(None))
compute_expression = expr_subst_map(subst.expression, kernel, None)
# }}}
from loopy.kernel.data import Assignment
if compute_insn_id is None:
compute_insn_id = kernel.make_unique_instruction_id(
based_on=c_subst_name)
compute_insn = Assignment(
id=compute_insn_id,
assignee=assignee,
expression=compute_expression,
# within_inames determined below
)
compute_dep_id = compute_insn_id
added_compute_insns = [compute_insn]
if temporary_address_space == AddressSpace.GLOBAL:
barrier_insn_id = kernel.make_unique_instruction_id(
based_on=c_subst_name + "_barrier")
from loopy.kernel.instruction import BarrierInstruction
barrier_insn = BarrierInstruction(id=barrier_insn_id,
depends_on=frozenset(
[compute_insn_id]),
synchronization_kind="global",
mem_kind="global")
compute_dep_id = barrier_insn_id
added_compute_insns.append(barrier_insn)
# }}}
# {{{ substitute rule into expressions in kernel (if within footprint)
from loopy.symbolic import SubstitutionRuleExpander
expander = SubstitutionRuleExpander(kernel.substitutions)
invr = RuleInvocationReplacer(rule_mapping_context,
subst_name,
subst_tag,
within,
access_descriptors,
abm,
storage_axis_names,
storage_axis_sources,
non1_storage_axis_names,
temporary_name,
compute_insn_id,
compute_dep_id,
compute_read_variables=get_dependencies(
expander(compute_expression)))
kernel = invr.map_kernel(kernel)
kernel = kernel.copy(instructions=added_compute_insns +
kernel.instructions)
kernel = rule_mapping_context.finish_kernel(kernel)
# }}}
# {{{ add dependencies to compute insn
kernel = kernel.copy(instructions=[
insn.copy(depends_on=frozenset(invr.compute_insn_depends_on)) if insn.
id == compute_insn_id else insn for insn in kernel.instructions
])
# }}}
# {{{ propagate storage iname subst to dependencies of compute instructions
from loopy.kernel.tools import find_recursive_dependencies
compute_deps = find_recursive_dependencies(kernel,
frozenset([compute_insn_id]))
# FIXME: Need to verify that there are no outside dependencies
# on compute_deps
prior_storage_axis_names = frozenset(storage_axis_subst_dict)
new_insns = []
for insn in kernel.instructions:
if (insn.id in compute_deps
and insn.within_inames & prior_storage_axis_names):
insn = (insn.with_transformed_expressions(
lambda expr: expr_subst_map(expr, kernel, insn)).copy(
within_inames=frozenset(
storage_axis_subst_dict.get(iname, var(iname)).name
for iname in insn.within_inames)))
new_insns.append(insn)
else:
new_insns.append(insn)
kernel = kernel.copy(instructions=new_insns)
# }}}
# {{{ determine inames for compute insn
if precompute_outer_inames is None:
from loopy.kernel.tools import guess_iname_deps_based_on_var_use
precompute_outer_inames = (
frozenset(non1_storage_axis_names)
| frozenset((expanding_usage_arg_deps | value_inames) -
sweep_inames_set)
| guess_iname_deps_based_on_var_use(kernel, compute_insn))
else:
if not isinstance(precompute_outer_inames, frozenset):
raise TypeError("precompute_outer_inames must be a frozenset")
precompute_outer_inames = precompute_outer_inames \
| frozenset(non1_storage_axis_names)
kernel = kernel.copy(instructions=[
insn.copy(within_inames=precompute_outer_inames) if insn.id ==
compute_insn_id else insn for insn in kernel.instructions
])
# }}}
# {{{ set up temp variable
import loopy as lp
if dtype is not None:
dtype = np.dtype(dtype)
if temporary_address_space is None:
temporary_address_space = lp.auto
new_temp_shape = tuple(abm.non1_storage_shape)
new_temporary_variables = kernel.temporary_variables.copy()
if temporary_name not in new_temporary_variables:
temp_var = lp.TemporaryVariable(
name=temporary_name,
dtype=dtype,
base_indices=(0, ) * len(new_temp_shape),
shape=tuple(abm.non1_storage_shape),
address_space=temporary_address_space,
dim_names=tuple(non1_storage_axis_names))
else:
temp_var = new_temporary_variables[temporary_name]
# {{{ check and adapt existing temporary
if temp_var.dtype is lp.auto:
pass
elif temp_var.dtype is not lp.auto and dtype is lp.auto:
dtype = temp_var.dtype
elif temp_var.dtype is not lp.auto and dtype is not lp.auto:
if temp_var.dtype != dtype:
raise LoopyError("Existing and new dtype of temporary '%s' "
"do not match (existing: %s, new: %s)" %
(temporary_name, temp_var.dtype, dtype))
temp_var = temp_var.copy(dtype=dtype)
if len(temp_var.shape) != len(new_temp_shape):
raise LoopyError(
"Existing and new temporary '%s' do not "
"have matching number of dimensions ('%d' vs. '%d') " %
(temporary_name, len(temp_var.shape), len(new_temp_shape)))
if temp_var.base_indices != (0, ) * len(new_temp_shape):
raise LoopyError(
"Existing and new temporary '%s' do not "
"have matching number of dimensions ('%d' vs. '%d') " %
(temporary_name, len(temp_var.shape), len(new_temp_shape)))
new_temp_shape = tuple(
max(i, ex_i) for i, ex_i in zip(new_temp_shape, temp_var.shape))
temp_var = temp_var.copy(shape=new_temp_shape)
if temporary_address_space == temp_var.address_space:
pass
elif temporary_address_space is lp.auto:
temporary_address_space = temp_var.address_space
elif temp_var.address_space is lp.auto:
pass
else:
raise LoopyError("Existing and new temporary '%s' do not "
"have matching scopes (existing: %s, new: %s)" %
(temporary_name,
AddressSpace.stringify(temp_var.address_space),
AddressSpace.stringify(temporary_address_space)))
temp_var = temp_var.copy(address_space=temporary_address_space)
# }}}
new_temporary_variables[temporary_name] = temp_var
kernel = kernel.copy(temporary_variables=new_temporary_variables)
# }}}
from loopy import tag_inames
kernel = tag_inames(kernel, new_iname_to_tag)
from loopy.kernel.data import AutoFitLocalIndexTag, filter_iname_tags_by_type
if filter_iname_tags_by_type(new_iname_to_tag.values(),
AutoFitLocalIndexTag):
from loopy.kernel.tools import assign_automatic_axes
kernel = assign_automatic_axes(kernel)
return kernel
def emit_multiple_assignment(self, codegen_state, insn):
ecm = codegen_state.expression_to_code_mapper
from pymbolic.primitives import Variable
from pymbolic.mapper.stringifier import PREC_NONE
func_id = insn.expression.function
parameters = insn.expression.parameters
if isinstance(func_id, Variable):
func_id = func_id.name
assignee_var_descriptors = [
codegen_state.kernel.get_var_descriptor(a)
for a in insn.assignee_var_names()
]
par_dtypes = tuple(ecm.infer_type(par) for par in parameters)
mangle_result = codegen_state.kernel.mangle_function(
func_id, par_dtypes)
if mangle_result is None:
raise RuntimeError(
"function '%s' unknown--"
"maybe you need to register a function mangler?" % func_id)
assert mangle_result.arg_dtypes is not None
from loopy.expression import dtype_to_type_context
c_parameters = [
ecm(par, PREC_NONE, dtype_to_type_context(self.target, tgt_dtype),
tgt_dtype).expr for par, par_dtype, tgt_dtype in zip(
parameters, par_dtypes, mangle_result.arg_dtypes)
]
from loopy.codegen import SeenFunction
codegen_state.seen_functions.add(
SeenFunction(func_id, mangle_result.target_name,
mangle_result.arg_dtypes))
from pymbolic import var
for i, (a, tgt_dtype) in enumerate(
zip(insn.assignees[1:], mangle_result.result_dtypes[1:])):
if tgt_dtype != ecm.infer_type(a):
raise LoopyError("type mismatch in %d'th (1-based) left-hand "
"side of instruction '%s'" % (i + 1, insn.id))
c_parameters.append(
# TODO Yuck: The "where-at function": &(...)
var("&")(ecm(a, PREC_NONE,
dtype_to_type_context(self.target, tgt_dtype),
tgt_dtype).expr))
from pymbolic import var
result = var(mangle_result.target_name)(*c_parameters)
# In case of no assignees, we are done
if len(mangle_result.result_dtypes) == 0:
from cgen import ExpressionStatement
return ExpressionStatement(
CExpression(self.get_c_expression_to_code_mapper(), result))
result = ecm.wrap_in_typecast(mangle_result.result_dtypes[0],
assignee_var_descriptors[0].dtype,
result)
lhs_code = ecm(insn.assignees[0], prec=PREC_NONE, type_context=None)
from cgen import Assign
return Assign(
lhs_code,
CExpression(self.get_c_expression_to_code_mapper(), result))
def get_access_info(target, ary, index, eval_expr, vectorization_info):
"""
:arg ary: an object of type :class:`ArrayBase`
:arg index: a tuple of indices representing a subscript into ary
:arg vectorization_info: an instance of :class:`loopy.codegen.VectorizationInfo`,
or *None*.
"""
import loopy as lp
from pymbolic import var
def eval_expr_assert_integer_constant(i, expr):
from pymbolic.mapper.evaluator import UnknownVariableError
try:
result = eval_expr(expr)
except UnknownVariableError as e:
raise LoopyError(
"When trying to index the array '%s' along axis "
"%d (tagged '%s'), the index was not a compile-time "
"constant (but it has to be in order for code to be "
"generated). You likely want to unroll the iname(s) '%s'." %
(ary.name, i, ary.dim_tags[i], str(e)))
if not is_integer(result):
raise LoopyError("subscript '%s[%s]' has non-constant "
"index for separate-array axis %d (0-based)" %
(ary.name, index, i))
return result
def apply_offset(sub):
import loopy as lp
if ary.offset:
if ary.offset is lp.auto:
return var(array_name + "_offset") + sub
elif isinstance(ary.offset, str):
return var(ary.offset) + sub
else:
# assume it's an expression
return ary.offset + sub
else:
return sub
if not isinstance(index, tuple):
index = (index, )
array_name = ary.name
if ary.dim_tags is None:
if len(index) != 1:
raise LoopyError(
"Array '%s' has no known axis implementation "
"tags and therefore only supports one-dimensional "
"indexing. (Did you mean 'shape=loopy.auto' instead of "
"'shape=None'?)" % ary.name)
return AccessInfo(array_name=array_name,
subscripts=(apply_offset(index[0]), ),
vector_index=None)
if len(ary.dim_tags) != len(index):
raise LoopyError("subscript to '%s[%s]' has the wrong "
"number of indices (got: %d, expected: %d)" %
(ary.name, index, len(index), len(ary.dim_tags)))
num_target_axes = ary.num_target_axes()
vector_index = None
subscripts = [0] * num_target_axes
vector_size = ary.vector_size(target)
# {{{ process separate-array dim tags first, to find array name
for i, (idx, dim_tag) in enumerate(zip(index, ary.dim_tags)):
if isinstance(dim_tag, SeparateArrayArrayDimTag):
idx = eval_expr_assert_integer_constant(i, idx)
array_name += "_s%d" % idx
# }}}
# {{{ process remaining dim tags
for i, (idx, dim_tag) in enumerate(zip(index, ary.dim_tags)):
if isinstance(dim_tag, FixedStrideArrayDimTag):
stride = dim_tag.stride
if is_integer(stride):
if not dim_tag.stride % vector_size == 0:
raise LoopyError(
"array '%s' has axis %d stride of "
"%d, which is not divisible by the size of the "
"vector (%d)" %
(ary.name, i, dim_tag.stride, vector_size))
elif stride is lp.auto:
stride = var(array_name + "_stride%d" % i)
subscripts[dim_tag.target_axis] += (stride // vector_size) * idx
elif isinstance(dim_tag, SeparateArrayArrayDimTag):
pass
elif isinstance(dim_tag, VectorArrayDimTag):
from pymbolic.primitives import Variable
if (vectorization_info is not None
and isinstance(index[i], Variable)
and index[i].name == vectorization_info.iname):
# We'll do absolutely nothing here, which will result
# in the vector being returned.
pass
else:
idx = eval_expr_assert_integer_constant(i, idx)
assert vector_index is None
vector_index = idx
else:
raise LoopyError("unsupported array dim implementation tag '%s' "
"in array '%s'" % (dim_tag, ary.name))
# }}}
from pymbolic import var
import loopy as lp
if ary.offset:
if num_target_axes > 1:
raise NotImplementedError("offsets for multiple image axes")
subscripts[0] = apply_offset(subscripts[0])
return AccessInfo(array_name=array_name,
vector_index=vector_index,
subscripts=subscripts)
def privatize_temporaries_with_inames(kernel,
privatizing_inames,
only_var_names=None):
"""This function provides each loop iteration of the *privatizing_inames*
with its own private entry in the temporaries it accesses (possibly
restricted to *only_var_names*).
This is accomplished implicitly as part of generating instruction-level
parallelism by the "ILP" tag and accessible separately through this
transformation.
Example::
for imatrix, i
acc = 0
for k
acc = acc + a[imatrix, i, k] * vec[k]
end
end
might become::
for imatrix, i
acc[imatrix] = 0
for k
acc[imatrix] = acc[imatrix] + a[imatrix, i, k] * vec[k]
end
end
facilitating loop interchange of the *imatrix* loop.
.. versionadded:: 2018.1
"""
if isinstance(privatizing_inames, str):
privatizing_inames = frozenset(s.strip()
for s in privatizing_inames.split(","))
if isinstance(only_var_names, str):
only_var_names = frozenset(s.strip()
for s in only_var_names.split(","))
wmap = kernel.writer_map()
var_to_new_priv_axis_iname = {}
# {{{ find variables that need extra indices
for tv in kernel.temporary_variables.values():
if only_var_names is not None and tv.name not in only_var_names:
continue
for writer_insn_id in wmap.get(tv.name, []):
writer_insn = kernel.id_to_insn[writer_insn_id]
priv_axis_inames = writer_insn.within_inames & privatizing_inames
referenced_priv_axis_inames = (
priv_axis_inames
& writer_insn.write_dependency_names())
new_priv_axis_inames = priv_axis_inames - referenced_priv_axis_inames
if not new_priv_axis_inames:
break
if tv.name in var_to_new_priv_axis_iname:
if new_priv_axis_inames != set(
var_to_new_priv_axis_iname[tv.name]):
raise LoopyError(
"instruction '%s' requires adding "
"indices for privatizing var '%s' on iname(s) '%s', "
"but previous instructions required inames '%s'" %
(writer_insn_id, tv.name,
", ".join(new_priv_axis_inames), ", ".join(
var_to_new_priv_axis_iname[tv.name])))
continue
var_to_new_priv_axis_iname[tv.name] = set(new_priv_axis_inames)
# }}}
# {{{ find ilp iname lengths
from loopy.isl_helpers import static_max_of_pw_aff
from loopy.symbolic import pw_aff_to_expr
priv_axis_iname_to_length = {}
iname_to_lbound = {}
for priv_axis_inames in var_to_new_priv_axis_iname.values():
for iname in priv_axis_inames:
if iname in priv_axis_iname_to_length:
continue
bounds = kernel.get_iname_bounds(iname, constants_only=False)
priv_axis_iname_to_length[iname] = pw_aff_to_expr(
static_max_of_pw_aff(bounds.size, constants_only=False))
iname_to_lbound[iname] = pw_aff_to_expr(bounds.lower_bound_pw_aff)
# }}}
# {{{ change temporary variables
from loopy.kernel.data import VectorizeTag
new_temp_vars = kernel.temporary_variables.copy()
for tv_name, inames in var_to_new_priv_axis_iname.items():
tv = new_temp_vars[tv_name]
extra_shape = tuple(priv_axis_iname_to_length[iname]
for iname in inames)
shape = tv.shape
if shape is None:
shape = ()
dim_tags = ["c"] * (len(shape) + len(extra_shape))
for i, iname in enumerate(inames):
if kernel.iname_tags_of_type(iname, VectorizeTag):
dim_tags[len(shape) + i] = "vec"
new_temp_vars[tv.name] = tv.copy(
shape=shape + extra_shape,
# Forget what you knew about data layout,
# create from scratch.
dim_tags=dim_tags,
dim_names=None)
# }}}
from pymbolic import var
var_to_extra_iname = {
var_name: tuple(var(iname) for iname in inames)
for var_name, inames in var_to_new_priv_axis_iname.items()
}
new_insns = []
for insn in kernel.instructions:
eiii = ExtraInameIndexInserter(var_to_extra_iname, iname_to_lbound)
new_insn = insn.with_transformed_expressions(eiii)
if not eiii.seen_priv_axis_inames <= insn.within_inames:
raise LoopyError(
"Kernel '%s': Instruction '%s': touched variable that "
"(for privatization, e.g. as performed for ILP) "
"required iname(s) '%s', but that the instruction was not "
"previously within the iname(s). To remedy this, first promote"
"the instruction into the iname." %
(kernel.name, insn.id,
", ".join(eiii.seen_priv_axis_inames - insn.within_inames)))
new_insns.append(new_insn)
return kernel.copy(temporary_variables=new_temp_vars,
instructions=new_insns)
def _parse_array_dim_tag(tag, default_target_axis, nesting_levels):
if isinstance(tag, ArrayDimImplementationTag):
return False, False, tag
if not isinstance(tag, str):
raise TypeError("arg dimension implementation tag must be "
"string or tag object")
tag = tag.strip()
is_optional = False
if tag.endswith("?"):
tag = tag[:-1]
is_optional = True
orig_tag = tag
if tag == "sep":
return False, is_optional, SeparateArrayArrayDimTag()
elif tag == "vec":
return False, is_optional, VectorArrayDimTag()
nesting_level_match = NESTING_LEVEL_RE.match(tag)
if nesting_level_match is not None:
nesting_level = int(nesting_level_match.group(1))
tag = nesting_level_match.group(2)
if tag is None:
tag = ""
else:
nesting_level = None
has_explicit_nesting_level = nesting_level is not None
target_axis_match = TARGET_AXIS_RE.search(tag)
if target_axis_match is not None:
target_axis = int(target_axis_match.group(1))
tag = tag[:target_axis_match.start()]
else:
target_axis = default_target_axis
ta_nesting_levels = nesting_levels.get(target_axis, [])
if tag.startswith("stride:"):
fixed_stride_descr = tag[7:]
if fixed_stride_descr.strip() == "auto":
import loopy as lp
return (has_explicit_nesting_level, is_optional,
FixedStrideArrayDimTag(lp.auto,
target_axis,
layout_nesting_level=nesting_level))
else:
from loopy.symbolic import parse
return (has_explicit_nesting_level, is_optional,
FixedStrideArrayDimTag(parse(fixed_stride_descr),
target_axis,
layout_nesting_level=nesting_level))
else:
padded_stride_match = PADDED_STRIDE_TAG_RE.match(tag)
if padded_stride_match is not None:
tag = padded_stride_match.group(1)
from loopy.symbolic import parse
pad_to = parse(padded_stride_match.group(2))
else:
pad_to = None
if tag in ["c", "C"]:
if nesting_level is not None:
raise LoopyError(
"may not specify 'C' array order with explicit "
"layout nesting level")
if ta_nesting_levels:
nesting_level = min(ta_nesting_levels) - 1
else:
nesting_level = 0
elif tag in ["f", "F"]:
if nesting_level is not None:
raise LoopyError(
"may not specify 'C' array order with explicit "
"layout nesting level")
if ta_nesting_levels:
nesting_level = max(ta_nesting_levels) + 1
else:
nesting_level = 0
elif tag == "":
if nesting_level is None:
raise LoopyError("invalid dim tag: '%s'" % orig_tag)
else:
raise LoopyError("invalid dim tag: '%s'" % orig_tag)
return (has_explicit_nesting_level, is_optional,
ComputedStrideArrayDimTag(nesting_level,
pad_to=pad_to,
target_axis=target_axis))
def __init__(self,
id,
depends_on,
depends_on_is_final,
groups,
conflicts_with_groups,
no_sync_with,
within_inames_is_final,
within_inames,
priority,
boostable,
boostable_into,
predicates,
tags,
insn_deps=None,
insn_deps_is_final=None,
forced_iname_deps=None,
forced_iname_deps_is_final=None):
# {{{ backwards compatibility goop
if depends_on is not None and insn_deps is not None:
raise LoopyError("may not specify both insn_deps and depends_on")
elif insn_deps is not None:
warn("insn_deps is deprecated, use depends_on",
DeprecationWarning,
stacklevel=2)
depends_on = insn_deps
depends_on_is_final = insn_deps_is_final
if forced_iname_deps is not None and within_inames is not None:
raise LoopyError("may not specify both forced_iname_deps "
"and within_inames")
elif forced_iname_deps is not None:
warn("forced_iname_deps is deprecated, use within_inames",
DeprecationWarning,
stacklevel=2)
within_inames = forced_iname_deps
within_inames_is_final = forced_iname_deps_is_final
if predicates is None:
predicates = frozenset()
new_predicates = set()
for pred in predicates:
if isinstance(pred, str):
from pymbolic.primitives import LogicalNot
from loopy.symbolic import parse
if pred.startswith("!"):
warn("predicates starting with '!' are deprecated. "
"Simply use 'not' instead")
pred = LogicalNot(parse(pred[1:]))
else:
pred = parse(pred)
new_predicates.add(pred)
predicates = frozenset(new_predicates)
del new_predicates
# }}}
if depends_on is None:
depends_on = frozenset()
if groups is None:
groups = frozenset()
if conflicts_with_groups is None:
conflicts_with_groups = frozenset()
if no_sync_with is None:
no_sync_with = frozenset()
if within_inames is None:
within_inames = frozenset()
if within_inames_is_final is None:
within_inames_is_final = False
if isinstance(depends_on, str):
depends_on = frozenset(s.strip() for s in depends_on.split(",")
if s.strip())
if depends_on_is_final is None:
depends_on_is_final = False
if depends_on_is_final and not isinstance(depends_on, frozenset):
raise LoopyError("Setting depends_on_is_final to True requires "
"actually specifying depends_on")
if tags is None:
tags = frozenset()
if priority is None:
priority = 0
if not isinstance(tags, frozenset):
# was previously allowed to be tuple
tags = frozenset(tags)
# Periodically reenable these and run the tests to ensure all
# performance-relevant identifiers are interned.
#
# from loopy.tools import is_interned
# assert is_interned(id)
# assert all(is_interned(dep) for dep in depends_on)
# assert all(is_interned(grp) for grp in groups)
# assert all(is_interned(grp) for grp in conflicts_with_groups)
# assert all(is_interned(iname) for iname in within_inames)
# assert all(is_interned(pred) for pred in predicates)
assert isinstance(within_inames, frozenset)
assert isinstance(depends_on, frozenset) or depends_on is None
assert isinstance(groups, frozenset)
assert isinstance(conflicts_with_groups, frozenset)
ImmutableRecord.__init__(self,
id=id,
depends_on=depends_on,
depends_on_is_final=depends_on_is_final,
no_sync_with=no_sync_with,
groups=groups,
conflicts_with_groups=conflicts_with_groups,
within_inames_is_final=within_inames_is_final,
within_inames=within_inames,
priority=priority,
boostable=boostable,
boostable_into=boostable_into,
predicates=predicates,
tags=tags)
def parse_array_dim_tags(dim_tags,
n_axes=None,
use_increasing_target_axes=False,
dim_names=None):
if isinstance(dim_tags, str):
dim_tags = dim_tags.split(",")
if isinstance(dim_tags, dict):
dim_tags_dict = dim_tags
if dim_names is None:
raise LoopyError("dim_tags may only be given as a dictionary if "
"dim_names is available")
assert n_axes == len(dim_names)
dim_tags = [None] * n_axes
for dim_name, val in dim_tags_dict.items():
try:
dim_idx = dim_names.index(dim_name)
except ValueError:
raise LoopyError("'%s' does not name an array axis" % dim_name)
dim_tags[dim_idx] = val
for idim, dim_tag in enumerate(dim_tags):
if dim_tag is None:
raise LoopyError(
"array axis tag for axis %d (1-based) was not "
"set by passed dictionary" % (idim + 1))
default_target_axis = 0
result = []
# a mapping from target axes to used nesting levels
nesting_levels = {}
target_axis_to_has_explicit_nesting_level = {}
for iaxis, dim_tag in enumerate(dim_tags):
has_explicit_nesting_level, is_optional, parsed_dim_tag = (
_parse_array_dim_tag(dim_tag, default_target_axis, nesting_levels))
if (is_optional and n_axes is not None
and len(result) + (len(dim_tags) - iaxis) > n_axes):
continue
if isinstance(parsed_dim_tag, _StrideArrayDimTagBase):
# {{{ check for C/F mixed with explicit layout nesting level specs
if (parsed_dim_tag.target_axis
in target_axis_to_has_explicit_nesting_level):
if (has_explicit_nesting_level !=
target_axis_to_has_explicit_nesting_level[
parsed_dim_tag.target_axis]):
raise LoopyError(
"may not mix C/F dim_tag specifications with "
"explicit specification of layout nesting levels")
else:
target_axis_to_has_explicit_nesting_level[
parsed_dim_tag.target_axis] = has_explicit_nesting_level
# }}}
lnl = parsed_dim_tag.layout_nesting_level
target_axis = parsed_dim_tag.target_axis
if lnl is not None:
if lnl in nesting_levels.get(target_axis, []):
raise LoopyError("layout nesting level %d is not unique"
" in target axis %d" % (lnl, target_axis))
nesting_levels.setdefault(target_axis, []) \
.append(parsed_dim_tag.layout_nesting_level)
result.append(parsed_dim_tag)
if use_increasing_target_axes:
default_target_axis += 1
# {{{ check contiguity of nesting levels
for target_axis, ta_nesting_levels in nesting_levels.items():
if sorted(ta_nesting_levels) != list(
range(min(ta_nesting_levels),
min(ta_nesting_levels) + len(ta_nesting_levels))):
raise LoopyError(
"layout nesting levels '%s' "
"for target axis %d not contiguous" %
(",".join(str(nl) for nl in ta_nesting_levels), target_axis))
ta_nesting_level_increment = -min(ta_nesting_levels)
for i in range(len(result)):
if (isinstance(result[i], _StrideArrayDimTagBase)
and result[i].target_axis == target_axis
and result[i].layout_nesting_level is not None):
result[i] = result[i].copy(
layout_nesting_level=result[i].layout_nesting_level +
ta_nesting_level_increment)
# }}}
return result
def map_subscript(self, expr):
WalkMapper.map_subscript(self, expr)
from pymbolic.primitives import Variable
assert isinstance(expr.aggregate, Variable)
shape = None
var_name = expr.aggregate.name
if var_name in self.kernel.arg_dict:
arg = self.kernel.arg_dict[var_name]
shape = arg.shape
elif var_name in self.kernel.temporary_variables:
tv = self.kernel.temporary_variables[var_name]
shape = tv.shape
if shape is not None:
subscript = expr.index
if not isinstance(subscript, tuple):
subscript = (subscript,)
from loopy.symbolic import (get_dependencies, get_access_range,
UnableToDetermineAccessRange)
available_vars = set(self.domain.get_var_dict())
shape_deps = set()
for shape_axis in shape:
if shape_axis is not None:
shape_deps.update(get_dependencies(shape_axis))
if not (get_dependencies(subscript) <= available_vars
and shape_deps <= available_vars):
return
if len(subscript) != len(shape):
raise LoopyError("subscript to '%s' in '%s' has the wrong "
"number of indices (got: %d, expected: %d)" % (
expr.aggregate.name, expr,
len(subscript), len(shape)))
try:
access_range = get_access_range(self.domain, subscript,
self.kernel.assumptions)
except UnableToDetermineAccessRange:
# Likely: index was non-affine, nothing we can do.
return
shape_domain = isl.BasicSet.universe(access_range.get_space())
for idim in range(len(subscript)):
shape_axis = shape[idim]
if shape_axis is not None:
from loopy.isl_helpers import make_slab
slab = make_slab(
shape_domain.get_space(), (dim_type.in_, idim),
0, shape_axis)
shape_domain = shape_domain.intersect(slab)
if not access_range.is_subset(shape_domain):
raise LoopyError("'%s' in instruction '%s' "
"accesses out-of-bounds array element (could not"
" establish '%s' is a subset of '%s')."
% (expr, self.insn_id, access_range, shape_domain))
def convert_computed_to_fixed_dim_tags(name, num_user_axes, num_target_axes,
shape, dim_tags):
# Just to clarify:
#
# - user axes are user-facing--what the user actually uses for indexing.
#
# - target axes are implementation facing. Normal in-memory arrays have one.
# 3D images have three.
import loopy as lp
# {{{ pick apart arg dim tags into computed, fixed and vec
vector_dim = None
# a mapping from target axes to {layout_nesting_level: dim_tag_index}
target_axis_to_nesting_level_map = {}
for i, dim_tag in enumerate(dim_tags):
if isinstance(dim_tag, VectorArrayDimTag):
if vector_dim is not None:
raise LoopyError("arg '%s' may only have one vector-tagged "
"argument dimension" % name)
vector_dim = i
elif isinstance(dim_tag, _StrideArrayDimTagBase):
if dim_tag.layout_nesting_level is None:
continue
nl_map = target_axis_to_nesting_level_map \
.setdefault(dim_tag.target_axis, {})
assert dim_tag.layout_nesting_level not in nl_map
nl_map[dim_tag.layout_nesting_level] = i
elif isinstance(dim_tag, SeparateArrayArrayDimTag):
pass
else:
raise LoopyError("invalid array dim tag")
# }}}
# {{{ convert computed to fixed stride dim tags
new_dim_tags = dim_tags[:]
for target_axis in range(num_target_axes):
if vector_dim is None:
stride_so_far = 1
else:
if shape is None or shape is lp.auto:
# unable to normalize without known shape
return None
if not is_integer(shape[vector_dim]):
raise TypeError(
"shape along vector axis %d of array '%s' "
"must be an integer, not an expression ('%s')" %
(vector_dim, name, shape[vector_dim]))
stride_so_far = shape[vector_dim]
# FIXME: OpenCL-specific
if stride_so_far == 3:
stride_so_far = 4
nesting_level_map = target_axis_to_nesting_level_map.get(
target_axis, {})
nl_keys = sorted(nesting_level_map.keys())
if not nl_keys:
continue
for key in nl_keys:
dim_tag_index = nesting_level_map[key]
dim_tag = dim_tags[dim_tag_index]
if isinstance(dim_tag, ComputedStrideArrayDimTag):
if stride_so_far is None:
raise LoopyError(
"unable to determine fixed stride "
"for axis %d because it is nested outside of "
"an 'auto' stride axis" % dim_tag_index)
new_dim_tags[dim_tag_index] = FixedStrideArrayDimTag(
stride_so_far,
target_axis=dim_tag.target_axis,
layout_nesting_level=dim_tag.layout_nesting_level)
if shape is None or shape is lp.auto:
# unable to normalize without known shape
return None
shape_axis = shape[dim_tag_index]
if shape_axis is None:
stride_so_far = None
else:
stride_so_far *= shape_axis
if dim_tag.pad_to is not None:
from pytools import div_ceil
stride_so_far = (div_ceil(stride_so_far, dim_tag.pad_to) *
stride_so_far)
elif isinstance(dim_tag, FixedStrideArrayDimTag):
stride_so_far = dim_tag.stride
if stride_so_far is lp.auto:
stride_so_far = None
else:
raise TypeError("internal error in dim_tag conversion")
# }}}
return new_dim_tags
def check_implemented_domains(kernel, implemented_domains, code=None):
from islpy import dim_type
from islpy import align_two
last_idomains = None
last_insn_inames = None
for insn_id, idomains in six.iteritems(implemented_domains):
insn = kernel.id_to_insn[insn_id]
assert idomains
insn_inames = kernel.insn_inames(insn)
# {{{ if we've checked the same thing before, no need to check it again
if last_idomains is not None and last_insn_inames is not None:
if idomains == last_idomains and insn_inames == last_insn_inames:
continue
last_idomains = idomains
last_insn_inames = insn_inames
# }}}
insn_impl_domain = idomains[0]
for idomain in idomains[1:]:
insn_impl_domain = insn_impl_domain | idomain
assumption_non_param = isl.BasicSet.from_params(kernel.assumptions)
assumptions, insn_impl_domain = align_two(
assumption_non_param, insn_impl_domain)
insn_impl_domain = (
(insn_impl_domain & assumptions)
.project_out_except(insn_inames, [dim_type.set]))
from loopy.kernel.instruction import BarrierInstruction
from loopy.kernel.data import LocalIndexTag
if isinstance(insn, BarrierInstruction):
# project out local-id-mapped inames, solves #94 on gitlab
non_lid_inames = frozenset(iname for iname in insn_inames
if not kernel.iname_tags_of_type(iname, LocalIndexTag))
insn_impl_domain = insn_impl_domain.project_out_except(
non_lid_inames, [dim_type.set])
insn_domain = kernel.get_inames_domain(insn_inames)
insn_parameters = frozenset(insn_domain.get_var_names(dim_type.param))
assumptions, insn_domain = align_two(assumption_non_param, insn_domain)
desired_domain = ((insn_domain & assumptions)
.project_out_except(insn_inames, [dim_type.set])
.project_out_except(insn_parameters, [dim_type.param]))
if isinstance(insn, BarrierInstruction):
# project out local-id-mapped inames, solves #94 on gitlab
desired_domain = desired_domain.project_out_except(
non_lid_inames, [dim_type.set])
insn_impl_domain = (insn_impl_domain
.project_out_except(insn_parameters, [dim_type.param]))
insn_impl_domain, desired_domain = align_two(
insn_impl_domain, desired_domain)
if insn_impl_domain != desired_domain:
i_minus_d = insn_impl_domain - desired_domain
d_minus_i = desired_domain - insn_impl_domain
parameter_inames = set(
insn_domain.get_dim_name(dim_type.param, i)
for i in range(insn_impl_domain.dim(dim_type.param)))
lines = []
for bigger, smaller, diff_set, gist_domain in [
("implemented", "desired", i_minus_d,
desired_domain.gist(insn_impl_domain)),
("desired", "implemented", d_minus_i,
insn_impl_domain.gist(desired_domain))]:
if diff_set.is_empty():
continue
diff_set = diff_set.coalesce()
pt = diff_set.sample_point()
assert not pt.is_void()
#pt_set = isl.Set.from_point(pt)
#lines.append("point implemented: %s" % (pt_set <= insn_impl_domain))
#lines.append("point desired: %s" % (pt_set <= desired_domain))
iname_to_dim = pt.get_space().get_var_dict()
point_axes = []
for iname in kernel.insn_inames(insn) | parameter_inames:
tp, dim = iname_to_dim[iname]
point_axes.append("%s=%d" % (
iname, pt.get_coordinate_val(tp, dim).to_python()))
lines.append(
"sample point in %s but not %s: %s" % (
bigger, smaller, ", ".join(point_axes)))
lines.append(
"gist of constraints in %s but not %s: %s" % (
smaller, bigger, gist_domain))
if code is not None:
print(79*"-")
print("CODE:")
print(79*"-")
from loopy.target.execution import get_highlighted_code
print(get_highlighted_code(code))
print(79*"-")
raise LoopyError("sanity check failed--implemented and desired "
"domain for instruction '%s' do not match\n\n"
"implemented: %s\n\n"
"desired:%s\n\n%s"
% (insn_id, insn_impl_domain, desired_domain, "\n".join(lines)))
# placate the assert at the call site
return True
def __init__(self,
name,
dtype=None,
shape=None,
dim_tags=None,
offset=0,
dim_names=None,
strides=None,
order=None,
for_atomic=False,
target=None,
alignment=None,
**kwargs):
"""
All of the following (except *name*) are optional.
Specify either strides or shape.
:arg name: When passed to :class:`loopy.make_kernel`, this may contain
multiple names separated by commas, in which case multiple arguments,
each with identical properties, are created for each name.
:arg shape: May be any of the things specified under :attr:`shape`,
or a string which can be parsed into the previous form.
:arg dim_tags: A comma-separated list of tags as understood by
:func:`loopy.kernel.array.parse_array_dim_tags`.
:arg strides: May be one of the following:
* None
* :class:`loopy.auto`. The strides will be determined by *order*
and the access footprint.
* a tuple like like :attr:`numpy.ndarray.shape`.
Each entry of the tuple is also allowed to be a :mod:`pymbolic`
expression involving kernel parameters, or a (potentially-comma
separated) or a string that can be parsed to such an expression.
* A string which can be parsed into the previous form.
:arg order: "F" or "C" for C (row major) or Fortran
(column major). Defaults to the *default_order* argument
passed to :func:`loopy.make_kernel`.
:arg for_atomic:
Whether the array is declared for atomic access, and, if necessary,
using atomic-capable data types.
:arg offset: (See :attr:`offset`)
:arg alignment: memory alignment in bytes
"""
for kwarg_name in kwargs:
if kwarg_name not in self.allowed_extra_kwargs:
raise TypeError("invalid kwarg: %s" % kwarg_name)
import loopy as lp
from loopy.types import to_loopy_type
dtype = to_loopy_type(dtype,
allow_auto=True,
allow_none=True,
for_atomic=for_atomic,
target=target)
if dtype is lp.auto:
from warnings import warn
warn(
"Argument/temporary data type for '%s' should be None if "
"unspecified, not auto. This usage will be disallowed in 2018."
% name,
DeprecationWarning,
stacklevel=2)
dtype = None
strides_known = strides is not None and strides is not lp.auto
shape_known = shape is not None and shape is not lp.auto
if strides_known:
strides = _parse_shape_or_strides(strides)
if shape_known:
shape = _parse_shape_or_strides(shape)
# {{{ check dim_names
if dim_names is not None:
if len(dim_names) != len(set(dim_names)):
raise LoopyError("dim_names are not unique")
for n in dim_names:
if not isinstance(n, str):
raise LoopyError("found non-string '%s' in dim_names" %
type(n).__name__)
# }}}
# {{{ convert strides to dim_tags (Note: strides override order)
if dim_tags is not None and strides_known:
raise TypeError("may not specify both strides and dim_tags")
if dim_tags is None and strides_known:
dim_tags = [FixedStrideArrayDimTag(s) for s in strides]
strides = None
# }}}
if dim_tags is not None:
dim_tags = parse_array_dim_tags(
dim_tags,
n_axes=(len(shape) if shape_known else None),
use_increasing_target_axes=self.max_target_axes > 1,
dim_names=dim_names)
# {{{ determine number of user axes
num_user_axes = None
if shape_known:
num_user_axes = len(shape)
for dim_iterable in [dim_tags, dim_names]:
if dim_iterable is not None:
new_num_user_axes = len(dim_iterable)
if num_user_axes is None:
num_user_axes = new_num_user_axes
else:
if new_num_user_axes != num_user_axes:
raise LoopyError(
"contradictory values for number of "
"dimensions of array '%s' from shape, strides, "
"dim_tags, or dim_names" % name)
del new_num_user_axes
# }}}
# {{{ convert order to dim_tags
if order is None and self.max_target_axes > 1:
# FIXME: Hackety hack. ImageArgs need to generate dim_tags even
# if no order is specified. Plus they don't care that much.
order = "C"
if dim_tags is None and num_user_axes is not None and order is not None:
dim_tags = parse_array_dim_tags(
num_user_axes * [order],
n_axes=num_user_axes,
use_increasing_target_axes=self.max_target_axes > 1,
dim_names=dim_names)
order = None
# }}}
if dim_tags is not None:
# {{{ find number of target axes
target_axes = set()
for dim_tag in dim_tags:
if isinstance(dim_tag, _StrideArrayDimTagBase):
target_axes.add(dim_tag.target_axis)
if target_axes != set(range(len(target_axes))):
raise LoopyError("target axes for variable '%s' are non-"
"contiguous" % self.name)
num_target_axes = len(target_axes)
del target_axes
# }}}
if not (self.min_target_axes <= num_target_axes <=
self.max_target_axes):
raise LoopyError(
"%s only supports between %d and %d target axes "
"('%s' has %d)" %
(type(self).__name__, self.min_target_axes,
self.max_target_axes, self.name, num_target_axes))
new_dim_tags = convert_computed_to_fixed_dim_tags(
name, num_user_axes, num_target_axes, shape, dim_tags)
if new_dim_tags is not None:
# successfully normalized
dim_tags = new_dim_tags
del new_dim_tags
if dim_tags is not None:
# for hashability
dim_tags = tuple(dim_tags)
order = None
if strides is not None:
# Preserve strides if we weren't able to process them yet.
# That only happens if they're set to loopy.auto (and 'guessed'
# in loopy.kernel.creation).
kwargs["strides"] = strides
if dim_names is not None and not isinstance(dim_names, tuple):
from warnings import warn
warn("dim_names is not a tuple when calling ArrayBase constructor",
DeprecationWarning,
stacklevel=2)
ImmutableRecord.__init__(self,
name=name,
dtype=dtype,
shape=shape,
dim_tags=dim_tags,
offset=offset,
dim_names=dim_names,
order=order,
alignment=alignment,
for_atomic=for_atomic,
**kwargs)
def auto_test_vs_ref(ref_knl,
ctx,
test_knl=None,
op_count=[],
op_label=[],
parameters={},
print_ref_code=False,
print_code=True,
warmup_rounds=2,
dump_binary=False,
fills_entire_output=None,
do_check=True,
check_result=None,
max_test_kernel_count=1,
quiet=False,
blacklist_ref_vendors=[]):
"""Compare results of `ref_knl` to the kernels generated by
scheduling *test_knl*.
:arg check_result: a callable with :class:`numpy.ndarray` arguments
*(result, reference_result)* returning a a tuple (class:`bool`,
message) indicating correctness/acceptability of the result
:arg max_test_kernel_count: Stop testing after this many *test_knl*
"""
import pyopencl as cl
if test_knl is None:
test_knl = ref_knl
do_check = False
if len(ref_knl.args) != len(test_knl.args):
raise LoopyError("ref_knl and test_knl do not have the same number "
"of arguments")
for i, (ref_arg, test_arg) in enumerate(zip(ref_knl.args, test_knl.args)):
if ref_arg.name != test_arg.name:
raise LoopyError(
"ref_knl and test_knl argument lists disagree at index "
"%d (1-based)" % (i + 1))
if ref_arg.dtype != test_arg.dtype:
raise LoopyError(
"ref_knl and test_knl argument lists disagree at index "
"%d (1-based)" % (i + 1))
from loopy.compiled import CompiledKernel
from loopy.target.execution import get_highlighted_code
if isinstance(op_count, (int, float)):
warn("op_count should be a list", stacklevel=2)
op_count = [op_count]
if isinstance(op_label, str):
warn("op_label should be a list", stacklevel=2)
op_label = [op_label]
from time import time
if check_result is None:
check_result = _default_check_result
if fills_entire_output is not None:
warn("fills_entire_output is deprecated",
DeprecationWarning,
stacklevel=2)
# {{{ compile and run reference code
from loopy.type_inference import infer_unknown_types
ref_knl = infer_unknown_types(ref_knl, expect_completion=True)
found_ref_device = False
ref_errors = []
from loopy.kernel.data import ImageArg
need_ref_image_support = any(
isinstance(arg, ImageArg) for arg in ref_knl.args)
for dev in _enumerate_cl_devices_for_ref_test(blacklist_ref_vendors,
need_ref_image_support):
ref_ctx = cl.Context([dev])
ref_queue = cl.CommandQueue(
ref_ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
pp_ref_knl = lp.preprocess_kernel(ref_knl)
for knl in lp.generate_loop_schedules(pp_ref_knl):
ref_sched_kernel = knl
break
logger.info("{} (ref): trying {} for the reference calculation".format(
ref_knl.name, dev))
ref_compiled = CompiledKernel(ref_ctx, ref_sched_kernel)
if not quiet and print_ref_code:
print(75 * "-")
print("Reference Code:")
print(75 * "-")
print(get_highlighted_code(ref_compiled.get_code()))
print(75 * "-")
ref_kernel_info = ref_compiled.kernel_info(frozenset())
try:
ref_args, ref_arg_data = \
make_ref_args(ref_sched_kernel,
ref_kernel_info.implemented_data_info,
ref_queue, parameters)
ref_args["out_host"] = False
except cl.RuntimeError as e:
if e.code == cl.status_code.IMAGE_FORMAT_NOT_SUPPORTED:
import traceback
ref_errors.append("\n".join([
75 * "-",
"On %s:" % dev, 75 * "-",
traceback.format_exc(), 75 * "-"
]))
continue
else:
raise
found_ref_device = True
if not do_check:
break
ref_queue.finish()
logger.info("{} (ref): using {} for the reference calculation".format(
ref_knl.name, dev))
logger.info("%s (ref): run" % ref_knl.name)
ref_start = time()
if not AUTO_TEST_SKIP_RUN:
ref_evt, _ = ref_compiled(ref_queue, **ref_args)
else:
ref_evt = cl.enqueue_marker(ref_queue)
ref_queue.finish()
ref_stop = time()
ref_elapsed_wall = ref_stop - ref_start
logger.info("%s (ref): run done" % ref_knl.name)
ref_evt.wait()
ref_elapsed_event = 1e-9 * (ref_evt.profile.END -
ref_evt.profile.START)
break
if not found_ref_device:
raise LoopyError("could not find a suitable device for the "
"reference computation.\n"
"These errors were encountered:\n" +
"\n".join(ref_errors))
# }}}
# {{{ compile and run parallel code
need_check = do_check
queue = cl.CommandQueue(
ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
from loopy.kernel import KernelState
from loopy.target.pyopencl import PyOpenCLTarget
if test_knl.state not in [
KernelState.PREPROCESSED, KernelState.LINEARIZED
]:
if isinstance(test_knl.target, PyOpenCLTarget):
test_knl = test_knl.copy(target=PyOpenCLTarget(ctx.devices[0]))
test_knl = lp.preprocess_kernel(test_knl)
if not test_knl.schedule:
test_kernels = lp.generate_loop_schedules(test_knl)
else:
test_kernels = [test_knl]
test_kernel_count = 0
from loopy.type_inference import infer_unknown_types
for i, kernel in enumerate(test_kernels):
test_kernel_count += 1
if test_kernel_count > max_test_kernel_count:
break
kernel = infer_unknown_types(kernel, expect_completion=True)
compiled = CompiledKernel(ctx, kernel)
kernel_info = compiled.kernel_info(frozenset())
args = make_args(kernel, kernel_info.implemented_data_info, queue,
ref_arg_data, parameters)
args["out_host"] = False
if not quiet:
print(75 * "-")
print("Kernel #%d:" % i)
print(75 * "-")
if print_code:
print(compiled.get_highlighted_code())
print(75 * "-")
if dump_binary:
# {{{ find cl program
for name in dir(kernel_info.cl_kernels):
if name.startswith("__"):
continue
cl_kernel = getattr(kernel_info.cl_kernels, name)
cl_program = cl_kernel.get_info(cl.kernel_info.PROGRAM)
break
else:
assert False, "could not find cl_program"
# }}}
print(type(cl_program))
if hasattr(cl_program, "binaries"):
print(cl_program.binaries[0])
print(75 * "-")
logger.info("%s: run warmup" % (knl.name))
for i in range(warmup_rounds):
if not AUTO_TEST_SKIP_RUN:
compiled(queue, **args)
if need_check and not AUTO_TEST_SKIP_RUN:
for arg_desc in ref_arg_data:
if arg_desc is None:
continue
if not arg_desc.needs_checking:
continue
from pyopencl.compyte.array import as_strided
ref_ary = as_strided(
arg_desc.ref_storage_array.get(),
shape=arg_desc.ref_shape,
strides=arg_desc.ref_numpy_strides).flatten()
test_ary = as_strided(
arg_desc.test_storage_array.get(),
shape=arg_desc.test_shape,
strides=arg_desc.test_numpy_strides).flatten()
common_len = min(len(ref_ary), len(test_ary))
ref_ary = ref_ary[:common_len]
test_ary = test_ary[:common_len]
error_is_small, error = check_result(test_ary, ref_ary)
if not error_is_small:
raise AutomaticTestFailure(error)
need_check = False
events = []
queue.finish()
logger.info("%s: warmup done" % (knl.name))
logger.info("%s: timing run" % (knl.name))
timing_rounds = max(warmup_rounds, 1)
while True:
from time import time
start_time = time()
evt_start = cl.enqueue_marker(queue)
for i in range(timing_rounds):
if not AUTO_TEST_SKIP_RUN:
evt, _ = compiled(queue, **args)
events.append(evt)
else:
events.append(cl.enqueue_marker(queue))
evt_end = cl.enqueue_marker(queue)
queue.finish()
stop_time = time()
for evt in events:
evt.wait()
evt_start.wait()
evt_end.wait()
elapsed_event = (1e-9*events[-1].profile.END
- 1e-9*events[0].profile.START) \
/ timing_rounds
try:
elapsed_event_marker = ((1e-9 * evt_end.profile.START -
1e-9 * evt_start.profile.START) /
timing_rounds)
except cl.RuntimeError:
elapsed_event_marker = None
elapsed_wall = (stop_time - start_time) / timing_rounds
if elapsed_wall * timing_rounds < 0.3:
timing_rounds *= 4
else:
break
logger.info("%s: timing run done" % (knl.name))
rates = ""
for cnt, lbl in zip(op_count, op_label):
rates += " {:g} {}/s".format(cnt / elapsed_wall, lbl)
if not quiet:
def format_float_or_none(v):
if v is None:
return "<unavailable>"
else:
return "%g" % v
print("elapsed: %s s event, %s s marker-event %s s wall "
"(%d rounds)%s" %
(format_float_or_none(elapsed_event),
format_float_or_none(elapsed_event_marker),
format_float_or_none(elapsed_wall), timing_rounds, rates))
if do_check:
ref_rates = ""
for cnt, lbl in zip(op_count, op_label):
ref_rates += " {:g} {}/s".format(cnt / ref_elapsed_event, lbl)
if not quiet:
print("ref: elapsed: {:g} s event, {:g} s wall{}".format(
ref_elapsed_event, ref_elapsed_wall, ref_rates))
# }}}
result_dict = {}
result_dict["elapsed_event"] = elapsed_event
result_dict["elapsed_event_marker"] = elapsed_event_marker
result_dict["elapsed_wall"] = elapsed_wall
result_dict["timing_rounds"] = timing_rounds
if do_check:
result_dict["ref_elapsed_event"] = ref_elapsed_event
result_dict["ref_elapsed_wall"] = ref_elapsed_wall
return result_dict
def alias_temporaries(knl, names, base_name_prefix=None,
synchronize_for_exclusive_use=True):
"""Sets all temporaries given by *names* to be backed by a single piece of
storage.
:arg synchronize_for_exclusive_use: A :class:`bool`. If ``True``, this also
introduces ordering structures ("groups") to prevent the usage to ensure
that the live ranges (i.e. the regions of code where each of the
temporaries is used) do not overlap. This will allow two (or more)
temporaries to share the same storage space as long as their live
ranges do not need to be concurrent.
:arg base_name_prefix: an identifier to be used for the common storage
area
.. versionchanged:: 2016.3
Added *synchronize_for_exclusive_use* flag.
``synchronize_for_exclusive_use=True`` was the previous default
behavior.
"""
gng = knl.get_group_name_generator()
group_names = [gng("tmpgrp_"+name) for name in names]
if base_name_prefix is None:
base_name_prefix = "temp_storage"
vng = knl.get_var_name_generator()
base_name = vng(base_name_prefix)
names_set = set(names)
if synchronize_for_exclusive_use:
new_insns = []
for insn in knl.instructions:
temp_deps = insn.dependency_names() & names_set
if not temp_deps:
new_insns.append(insn)
continue
if len(temp_deps) > 1:
raise LoopyError("Instruction {insn} refers to multiple of the "
"temporaries being aliased, namely '{temps}'. Cannot alias."
.format(
insn=insn.id,
temps=", ".join(temp_deps)))
temp_name, = temp_deps
temp_idx = names.index(temp_name)
group_name = group_names[temp_idx]
other_group_names = (
frozenset(group_names[:temp_idx])
| frozenset(group_names[temp_idx+1:]))
new_insns.append(
insn.copy(
groups=insn.groups | frozenset([group_name]),
conflicts_with_groups=(
insn.conflicts_with_groups | other_group_names)))
else:
new_insns = knl.instructions
new_temporary_variables = {}
for tv in six.itervalues(knl.temporary_variables):
if tv.name in names_set:
if tv.base_storage is not None:
raise LoopyError("temporary variable '{tv}' already has "
"a defined storage array -- cannot alias"
.format(tv=tv.name))
new_temporary_variables[tv.name] = \
tv.copy(base_storage=base_name)
else:
new_temporary_variables[tv.name] = tv
return knl.copy(
instructions=new_insns,
temporary_variables=new_temporary_variables)
def get_hw_axis_sizes_and_tags_for_save_slot(self, temporary):
"""
This is used for determining the amount of global storage needed for saving
and restoring the temporary across kernel calls, due to hardware
parallel inames (the inferred axes get prefixed to the number of
dimensions in the temporary).
In the case of local temporaries, inames that are tagged
hw-local do not contribute to the global storage shape.
"""
accessor_insn_ids = frozenset(
self.kernel.reader_map()[temporary.name]
| self.kernel.writer_map()[temporary.name])
group_tags = None
local_tags = None
def _sortedtags(tags):
return sorted(tags, key=lambda tag: tag.axis)
for insn_id in accessor_insn_ids:
insn = self.kernel.id_to_insn[insn_id]
my_group_tags = []
my_local_tags = []
for iname in insn.within_inames:
tag = self.kernel.iname_to_tag.get(iname)
if tag is None:
continue
from loopy.kernel.data import (GroupIndexTag, LocalIndexTag,
ParallelTag)
if isinstance(tag, GroupIndexTag):
my_group_tags.append(tag)
elif isinstance(tag, LocalIndexTag):
my_local_tags.append(tag)
elif isinstance(tag, ParallelTag):
raise LoopyError("iname '%s' is tagged with '%s' - only "
"group and local tags are supported for "
"auto save/reload of temporaries" %
(iname, tag))
if group_tags is None:
group_tags = _sortedtags(my_group_tags)
local_tags = _sortedtags(my_local_tags)
group_tags_originating_insn_id = insn_id
if (group_tags != _sortedtags(my_group_tags)
or local_tags != _sortedtags(my_local_tags)):
raise LoopyError(
"inconsistent parallel tags across instructions that access "
"'%s' (specifically, instruction '%s' has tags '%s' but "
"instruction '%s' has tags '%s')" %
(temporary.name, group_tags_originating_insn_id, group_tags
+ local_tags, insn_id, my_group_tags + my_local_tags))
if group_tags is None:
assert local_tags is None
return (), ()
group_sizes, local_sizes = (
self.kernel.get_grid_sizes_for_insn_ids_as_exprs(accessor_insn_ids)
)
if temporary.scope == lp.temp_var_scope.LOCAL:
# Elide local axes in the save slot for local temporaries.
del local_tags[:]
local_sizes = ()
# We set hw_dims to be arranged according to the order:
# g.0 < g.1 < ... < l.0 < l.1 < ...
return (group_sizes + local_sizes), tuple(group_tags + local_tags)
def generate_code_v2(kernel):
"""
:returns: a :class:`CodeGenerationResult`
"""
from loopy.kernel import kernel_state
if kernel.state == kernel_state.INITIAL:
from loopy.preprocess import preprocess_kernel
kernel = preprocess_kernel(kernel)
if kernel.schedule is None:
from loopy.schedule import get_one_scheduled_kernel
kernel = get_one_scheduled_kernel(kernel)
if kernel.state != kernel_state.SCHEDULED:
raise LoopyError("cannot generate code for a kernel that has not been "
"scheduled")
# {{{ cache retrieval
from loopy import CACHING_ENABLED
if CACHING_ENABLED:
input_kernel = kernel
try:
result = code_gen_cache[input_kernel]
logger.debug("%s: code generation cache hit" % kernel.name)
return result
except KeyError:
pass
# }}}
from loopy.type_inference import infer_unknown_types
kernel = infer_unknown_types(kernel, expect_completion=True)
from loopy.check import pre_codegen_checks
pre_codegen_checks(kernel)
logger.info("%s: generate code: start" % kernel.name)
# {{{ examine arg list
from loopy.kernel.data import ValueArg
from loopy.kernel.array import ArrayBase
implemented_data_info = []
for arg in kernel.args:
is_written = arg.name in kernel.get_written_variables()
if isinstance(arg, ArrayBase):
implemented_data_info.extend(
arg.decl_info(
kernel.target,
is_written=is_written,
index_dtype=kernel.index_dtype))
elif isinstance(arg, ValueArg):
implemented_data_info.append(ImplementedDataInfo(
target=kernel.target,
name=arg.name,
dtype=arg.dtype,
arg_class=ValueArg,
is_written=is_written))
else:
raise ValueError("argument type not understood: '%s'" % type(arg))
allow_complex = False
for var in kernel.args + list(six.itervalues(kernel.temporary_variables)):
if var.dtype.involves_complex():
allow_complex = True
# }}}
seen_dtypes = set()
seen_functions = set()
seen_atomic_dtypes = set()
initial_implemented_domain = isl.BasicSet.from_params(kernel.assumptions)
codegen_state = CodeGenerationState(
kernel=kernel,
implemented_data_info=implemented_data_info,
implemented_domain=initial_implemented_domain,
implemented_predicates=frozenset(),
seen_dtypes=seen_dtypes,
seen_functions=seen_functions,
seen_atomic_dtypes=seen_atomic_dtypes,
var_subst_map={},
allow_complex=allow_complex,
var_name_generator=kernel.get_var_name_generator(),
is_generating_device_code=False,
gen_program_name=(
kernel.target.host_program_name_prefix
+ kernel.name
+ kernel.target.host_program_name_suffix),
schedule_index_end=len(kernel.schedule))
from loopy.codegen.result import generate_host_or_device_program
codegen_result = generate_host_or_device_program(
codegen_state,
schedule_index=0)
device_code_str = codegen_result.device_code()
from loopy.check import check_implemented_domains
assert check_implemented_domains(kernel, codegen_result.implemented_domains,
device_code_str)
# {{{ handle preambles
for arg in kernel.args:
seen_dtypes.add(arg.dtype)
for tv in six.itervalues(kernel.temporary_variables):
seen_dtypes.add(tv.dtype)
preambles = kernel.preambles[:]
preamble_info = PreambleInfo(
kernel=kernel,
seen_dtypes=seen_dtypes,
seen_functions=seen_functions,
# a set of LoopyTypes (!)
seen_atomic_dtypes=seen_atomic_dtypes)
preamble_generators = (kernel.preamble_generators
+ kernel.target.get_device_ast_builder().preamble_generators())
for prea_gen in preamble_generators:
preambles.extend(prea_gen(preamble_info))
codegen_result = codegen_result.copy(device_preambles=preambles)
# }}}
logger.info("%s: generate code: done" % kernel.name)
if CACHING_ENABLED:
code_gen_cache[input_kernel] = codegen_result
return codegen_result