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import ast
import copy
from collections import OrderedDict
import linecache
import os
import sys
import operator
import numpy as np
import types as pytypes
import operator
import warnings
import llvmlite.llvmpy.core as lc
import as liv
import numba
from numba.parfors import parfor
from numba.core import types, ir, config, compiler, lowering, sigutils, cgutils
from numba.core.ir_utils import add_offset_to_labels, replace_var_names, remove_dels, legalize_names, mk_unique_var, rename_labels, get_name_var_table, visit_vars_inner, get_definition, guard, find_callname, get_call_table, is_pure, get_np_ufunc_typ, get_unused_var_name, find_potential_aliases, is_const_call
from numba.core.analysis import compute_use_defs, compute_live_map, compute_dead_maps, compute_cfg_from_blocks
from numba.core.typing import signature
from numba.parfors.parfor import print_wrapped, ensure_parallel_support
from numba.core.errors import NumbaParallelSafetyWarning, NotDefinedError, CompilerError
from numba.parfors.parfor_lowering_utils import ParforLoweringBuilder
def _lower_parfor_parallel(lowerer, parfor):
"""Lowerer that handles LLVM code generation for parfor.
This function lowers a parfor IR node to LLVM.
The general approach is as follows:
1) The code from the parfor's init block is lowered normally
in the context of the current function.
2) The body of the parfor is transformed into a gufunc function.
3) Code is inserted into the main function that calls do_scheduling
to divide the iteration space for each thread, allocates
reduction arrays, calls the gufunc function, and then invokes
the reduction function across the reduction arrays to produce
the final reduction values.
from import get_thread_count
typingctx = lowerer.context.typing_context
targetctx = lowerer.context
# We copy the typemap here because for race condition variable we'll
# update their type to array so they can be updated by the gufunc.
orig_typemap = lowerer.fndesc.typemap
# replace original typemap with copy and restore the original at the end.
lowerer.fndesc.typemap = copy.copy(orig_typemap)
if config.DEBUG_ARRAY_OPT:
print("lowerer.fndesc", lowerer.fndesc, type(lowerer.fndesc))
typemap = lowerer.fndesc.typemap
varmap = lowerer.varmap
if config.DEBUG_ARRAY_OPT:
loc = parfor.init_block.loc
scope = parfor.init_block.scope
# produce instructions for init_block
if config.DEBUG_ARRAY_OPT:
print("init_block = ", parfor.init_block, " ", type(parfor.init_block))
for instr in parfor.init_block.body:
if config.DEBUG_ARRAY_OPT:
print("lower init_block instr = ", instr)
for racevar in parfor.races:
if racevar not in varmap:
rvtyp = typemap[racevar]
rv = ir.Var(scope, racevar, loc)
lowerer._alloca_var(, rvtyp)
alias_map = {}
arg_aliases = {}
numba.parfors.parfor.find_potential_aliases_parfor(parfor, parfor.params, typemap,
lowerer.func_ir, alias_map, arg_aliases)
if config.DEBUG_ARRAY_OPT:
print("alias_map", alias_map)
print("arg_aliases", arg_aliases)
# run get_parfor_outputs() and get_parfor_reductions() before gufunc creation
# since Jumps are modified so CFG of loop_body dict will become invalid
assert parfor.params is not None
parfor_output_arrays = numba.parfors.parfor.get_parfor_outputs(
parfor, parfor.params)
parfor_redvars, parfor_reddict = numba.parfors.parfor.get_parfor_reductions(
lowerer.func_ir, parfor, parfor.params, lowerer.fndesc.calltypes)
if config.DEBUG_ARRAY_OPT:
print("parfor_redvars:", parfor_redvars)
print("parfor_reddict:", parfor_reddict)
# init reduction array allocation here.
nredvars = len(parfor_redvars)
redarrs = {}
if nredvars > 0:
# reduction arrays outer dimension equal to thread count
thread_count = get_thread_count()
scope = parfor.init_block.scope
loc = parfor.init_block.loc
pfbdr = ParforLoweringBuilder(lowerer=lowerer, scope=scope, loc=loc)
# For each reduction variable...
for i in range(nredvars):
redvar_typ = lowerer.fndesc.typemap[parfor_redvars[i]]
redvar = ir.Var(scope, parfor_redvars[i], loc)
redarrvar_typ = redtyp_to_redarraytype(redvar_typ)
reddtype = redarrvar_typ.dtype
if config.DEBUG_ARRAY_OPT:
print("redvar_typ", redvar_typ, redarrvar_typ, reddtype, types.DType(reddtype))
# If this is reduction over an array,
# the reduction array has just one added per-worker dimension.
if isinstance(redvar_typ, types.npytypes.Array):
redarrdim = redvar_typ.ndim + 1
redarrdim = 1
# Reduction array is created and initialized to the initial reduction value.
# First create a var for the numpy empty ufunc.
glbl_np_empty = pfbdr.bind_global_function(
types.UniTuple(types.intp, redarrdim),
# Create var for outer dimension size of reduction array equal to number of threads.
num_threads_var = pfbdr.make_const_variable(
size_var_list = [num_threads_var]
# If this is a reduction over an array...
if isinstance(redvar_typ, types.npytypes.Array):
# Add code to get the shape of the array being reduced over.
redshape_var = pfbdr.assign(
rhs=ir.Expr.getattr(redvar, "shape", loc),
typ=types.UniTuple(types.intp, redvar_typ.ndim),
# Add the dimension sizes of the array being reduced over to the tuple of sizes pass to empty.
for j in range(redvar_typ.ndim):
onedimvar = pfbdr.assign(
rhs=ir.Expr.static_getitem(redshape_var, j, None, loc),
# Empty call takes tuple of sizes. Create here and fill in outer dimension (num threads).
size_var = pfbdr.make_tuple_variable(
size_var_list, name='tuple_size_var',
# Add call to empty passing the size var tuple.
empty_call =, args=[size_var])
redarr_var = pfbdr.assign(
rhs=empty_call, typ=redarrvar_typ, name="redarr",
# Remember mapping of original reduction array to the newly created per-worker reduction array.
redarrs[] = redarr_var
init_val = parfor_reddict[parfor_redvars[i]][0]
if init_val is not None:
if isinstance(redvar_typ, types.npytypes.Array):
# Create an array of identity values for the reduction.
# First, create a variable for np.full.
full_func_node = pfbdr.bind_global_function(
types.UniTuple(types.intp, redvar_typ.ndim),
# Then create a var with the identify value.
init_val_var = pfbdr.make_const_variable(
# Then, call np.full with the shape of the reduction array and the identity value.
full_call =
full_func_node, args=[redshape_var, init_val_var],
redtoset = pfbdr.assign(
redtoset = pfbdr.make_const_variable(
redtoset = redvar
res_print_str = "res_print1 for redvar " + str(redvar) + ":"
strconsttyp = types.StringLiteral(res_print_str)
lhs = pfbdr.make_const_variable(
res_print = ir.Print(args=[lhs, redvar],
vararg=None, loc=loc)
lowerer.fndesc.calltypes[res_print] = signature(types.none,
print("res_print_redvar", res_print)
# For each thread, initialize the per-worker reduction array to the current reduction array value.
for j in range(thread_count):
index_var = pfbdr.make_const_variable(
cval=j, typ=types.uintp, name="index_var",
pfbdr.setitem(obj=redarr_var, index=index_var, val=redtoset)
# compile parfor body as a separate function to be used with GUFuncWrapper
flags = copy.copy(parfor.flags)
flags.set('error_model', 'numpy')
# Can't get here unless flags.set('auto_parallel', ParallelOptions(True))
index_var_typ = typemap[parfor.loop_nests[0]]
# index variables should have the same type, check rest of indices
for l in parfor.loop_nests[1:]:
assert typemap[] == index_var_typ
numba.parfors.parfor.sequential_parfor_lowering = True
exp_name_to_tuple_var) = _create_gufunc_for_parfor_body(
lowerer, parfor, typemap, typingctx, targetctx, flags, {},
bool(alias_map), index_var_typ, parfor.races)
numba.parfors.parfor.sequential_parfor_lowering = False
# get the shape signature
func_args = ['sched'] + func_args
num_reductions = len(parfor_redvars)
num_inputs = len(func_args) - len(parfor_output_arrays) - num_reductions
if config.DEBUG_ARRAY_OPT:
print("func_args = ", func_args)
print("num_inputs = ", num_inputs)
print("parfor_outputs = ", parfor_output_arrays)
print("parfor_redvars = ", parfor_redvars)
print("num_reductions = ", num_reductions)
gu_signature = _create_shape_signature(
if config.DEBUG_ARRAY_OPT:
print("gu_signature = ", gu_signature)
# call the func in parallel by wrapping it with ParallelGUFuncBuilder
loop_ranges = [(l.start, l.stop, l.step) for l in parfor.loop_nests]
if config.DEBUG_ARRAY_OPT:
print("loop_nests = ", parfor.loop_nests)
print("loop_ranges = ", loop_ranges)
if config.DEBUG_ARRAY_OPT:
if nredvars > 0:
# Perform the final reduction across the reduction array created above.
thread_count = get_thread_count()
scope = parfor.init_block.scope
loc = parfor.init_block.loc
# For each reduction variable...
for i in range(nredvars):
name = parfor_redvars[i]
redarr = redarrs[name]
redvar_typ = lowerer.fndesc.typemap[name]
if config.DEBUG_ARRAY_OPT:
print("post-gufunc reduction:", name, redarr, redvar_typ)
res_print_str = "res_print"
strconsttyp = types.StringLiteral(res_print_str)
lhs = pfbdr.make_const_variable(
res_print = ir.Print(args=[lhs, redarr], vararg=None, loc=loc)
lowerer.fndesc.calltypes[res_print] = signature(types.none,
print("res_print", res_print)
# For each element in the reduction array created above.
for j in range(thread_count):
# Create index var to access that element.
index_var = pfbdr.make_const_variable(
cval=j, typ=types.uintp, name="index_var",
# Read that element from the array into oneelem.
oneelemgetitem = pfbdr.getitem(
obj=redarr, index=index_var, typ=redvar_typ,
oneelem = pfbdr.assign(
init_var = pfbdr.assign_inplace(
rhs=oneelem, typ=redvar_typ, name=name + "#init",
res_print_str = "res_print1 for thread " + str(j) + ":"
strconsttyp = types.StringLiteral(res_print_str)
lhs = pfbdr.make_const_variable(
res_print = ir.Print(args=[lhs, index_var, oneelem, init_var, ir.Var(scope, name, loc)],
vararg=None, loc=loc)
lowerer.fndesc.calltypes[res_print] = signature(types.none,
print("res_print1", res_print)
# generate code for combining reduction variable with thread output
for inst in parfor_reddict[name][1]:
# If we have a case where a parfor body has an array reduction like A += B
# and A and B have different data types then the reduction in the parallel
# region will operate on those differeing types. However, here, after the
# parallel region, we are summing across the reduction array and that is
# guaranteed to have the same data type so we need to change the reduction
# nodes so that the right-hand sides have a type equal to the reduction-type
# and therefore the left-hand side.
if isinstance(inst, ir.Assign):
rhs = inst.value
# We probably need to generalize this since it only does substitutions in
# inplace_binops.
if (isinstance(rhs, ir.Expr) and rhs.op == 'inplace_binop' and ==
if config.DEBUG_ARRAY_OPT:
print("Adding call to reduction", rhs)
if rhs.fn == operator.isub:
rhs.fn = operator.iadd
rhs.immutable_fn = operator.add
if rhs.fn == operator.itruediv or rhs.fn == operator.ifloordiv:
rhs.fn = operator.imul
rhs.immutable_fn = operator.mul
if config.DEBUG_ARRAY_OPT:
print("After changing sub to add or div to mul", rhs)
# Get calltype of rhs.
ct = lowerer.fndesc.calltypes[rhs]
assert(len(ct.args) == 2)
# Create new arg types replace the second arg type with the reduction var type.
ctargs = (ct.args[0], redvar_typ)
# Update the signature of the call.
ct = ct.replace(args=ctargs)
# Remove so we can re-insert since calltypes is unique dict.
# Add calltype back in for the expr with updated signature.
lowerer.fndesc.calltypes[rhs] = ct
# Only process reduction statements post-gufunc execution
# until we see an assignment with a left-hand side to the
# reduction variable's name. This fixes problems with
# cases where there are multiple assignments to the
# reduction variable in the parfor.
if isinstance(inst, ir.Assign):
reduction_var = scope.get_exact(name)
except NotDefinedError:
# Ideally, this shouldn't happen. The redvar name
# missing from scope indicates an error from
# other rewrite passes.
is_same_source_var = name ==
# Because of SSA, the redvar and target var of
# the current assignment would be different even
# though they refer to the same source-level var.
redvar_unver_name = reduction_var.unversioned_name
target_unver_name =
is_same_source_var = redvar_unver_name == target_unver_name
if is_same_source_var:
# If redvar is different from target var, add an
# assignment to put target var into redvar.
if name !=
pfbdr.assign_inplace(, typ=redvar_typ,
res_print_str = "res_print2 for thread " + str(j) + ":"
strconsttyp = types.StringLiteral(res_print_str)
lhs = pfbdr.make_const_variable(
res_print = ir.Print(args=[lhs, index_var, oneelem, init_var, ir.Var(scope, name, loc)],
vararg=None, loc=loc)
lowerer.fndesc.calltypes[res_print] = signature(types.none,
print("res_print2", res_print)
# Cleanup reduction variable
for v in redarrs.values():
lowerer.lower_inst(ir.Del(, loc=loc))
# Restore the original typemap of the function that was replaced temporarily at the
# Beginning of this function.
lowerer.fndesc.typemap = orig_typemap
if config.DEBUG_ARRAY_OPT:
print("_lower_parfor_parallel done")
# A work-around to prevent circular imports
lowering.lower_extensions[parfor.Parfor] = _lower_parfor_parallel
def _create_shape_signature(
'''Create shape signature for GUFunc
if config.DEBUG_ARRAY_OPT:
print("_create_shape_signature", num_inputs, num_reductions, args, redargstartdim)
for i in args[1:]:
print("argument", i, type(i), get_shape_classes(i, typemap=typemap))
num_inouts = len(args) - num_reductions
# maximum class number for array shapes
classes = [get_shape_classes(var, typemap=typemap) if var not in races else (-1,) for var in args[1:]]
class_set = set()
for _class in classes:
if _class:
for i in _class:
max_class = max(class_set) + 1 if class_set else 0
classes.insert(0, (max_class,)) # force set the class of 'sched' argument
class_map = {}
# TODO: use prefix + class number instead of single char
alphabet = ord('a')
for n in class_set:
if n >= 0:
class_map[n] = chr(alphabet)
alphabet += 1
alpha_dict = {'latest_alpha' : alphabet}
def bump_alpha(c, class_map):
if c >= 0:
return class_map[c]
alpha_dict['latest_alpha'] += 1
return chr(alpha_dict['latest_alpha'])
gu_sin = []
gu_sout = []
count = 0
syms_sin = ()
if config.DEBUG_ARRAY_OPT:
print("args", args)
print("classes", classes)
for cls, arg in zip(classes, args):
count = count + 1
if cls:
dim_syms = tuple(bump_alpha(c, class_map) for c in cls)
dim_syms = ()
if (count > num_inouts):
# Strip the first symbol corresponding to the number of workers
# so that guvectorize will parallelize across the reduction.
syms_sin += dim_syms
return (gu_sin, gu_sout)
def _print_block(block):
for i, inst in enumerate(block.body):
print(" ", i, " ", inst)
def _print_body(body_dict):
'''Pretty-print a set of IR blocks.
for label, block in body_dict.items():
print("label: ", label)
def wrap_loop_body(loop_body):
blocks = loop_body.copy() # shallow copy is enough
first_label = min(blocks.keys())
last_label = max(blocks.keys())
loc = blocks[last_label].loc
blocks[last_label].body.append(ir.Jump(first_label, loc))
return blocks
def unwrap_loop_body(loop_body):
last_label = max(loop_body.keys())
loop_body[last_label].body = loop_body[last_label].body[:-1]
def add_to_def_once_sets(a_def, def_once, def_more):
'''If the variable is already defined more than once, do nothing.
Else if defined exactly once previously then transition this
variable to the defined more than once set (remove it from
def_once set and add to def_more set).
Else this must be the first time we've seen this variable defined
so add to def_once set.
if a_def in def_more:
elif a_def in def_once:
def compute_def_once_block(block, def_once, def_more, getattr_taken, typemap, module_assigns):
'''Effect changes to the set of variables defined once or more than once
for a single block.
block - the block to process
def_once - set of variable names known to be defined exactly once
def_more - set of variable names known to be defined more than once
getattr_taken - dict mapping variable name to tuple of object and attribute taken
module_assigns - dict mapping variable name to the Global that they came from
# The only "defs" occur in assignments, so find such instructions.
assignments = block.find_insts(ir.Assign)
# For each assignment...
for one_assign in assignments:
# Get the LHS/target of the assignment.
a_def =
# Add variable to def sets.
add_to_def_once_sets(a_def, def_once, def_more)
rhs = one_assign.value
if isinstance(rhs, ir.Global):
# Remember assignments of the form "a = Global(...)"
# Is this a module?
if isinstance(rhs.value, pytypes.ModuleType):
module_assigns[a_def] = rhs.value.__name__
if isinstance(rhs, ir.Expr) and rhs.op == 'getattr' and in def_once:
# Remember assignments of the form "a = b.c"
getattr_taken[a_def] = (, rhs.attr)
if isinstance(rhs, ir.Expr) and rhs.op == 'call' and in getattr_taken:
# If "a" is being called then lookup the getattr definition of "a"
# as above, getting the module variable "b" (base_obj)
# and the attribute "c" (base_attr).
base_obj, base_attr = getattr_taken[]
if base_obj in module_assigns:
# If we know the definition of the module variable then get the module
# name from module_assigns.
base_mod_name = module_assigns[base_obj]
if not is_const_call(base_mod_name, base_attr):
# Calling a method on an object could modify the object and is thus
# like a def of that object. We call is_const_call to see if this module/attribute
# combination is known to not modify the module state. If we don't know that
# the combination is safe then we have to assume there could be a modification to
# the module and thus add the module variable as defined more than once.
add_to_def_once_sets(base_obj, def_once, def_more)
# Assume the worst and say that base_obj could be modified by the call.
add_to_def_once_sets(base_obj, def_once, def_more)
if isinstance(rhs, ir.Expr) and rhs.op == 'call':
# If a mutable object is passed to a function, then it may be changed and
# therefore can't be hoisted.
# For each argument to the function...
for argvar in rhs.args:
# Get the argument's type.
if isinstance(argvar, ir.Var):
argvar =
avtype = typemap[argvar]
# If that type doesn't have a mutable attribute or it does and it's set to
# not mutable then this usage is safe for hoisting.
if getattr(avtype, 'mutable', False):
# Here we have a mutable variable passed to a function so add this variable
# to the def lists.
add_to_def_once_sets(argvar, def_once, def_more)
def compute_def_once_internal(loop_body, def_once, def_more, getattr_taken, typemap, module_assigns):
'''Compute the set of variables defined exactly once in the given set of blocks
and use the given sets for storing which variables are defined once, more than
once and which have had a getattr call on them.
# For each block...
for label, block in loop_body.items():
# Scan this block and effect changes to def_once, def_more, and getattr_taken
# based on the instructions in that block.
compute_def_once_block(block, def_once, def_more, getattr_taken, typemap, module_assigns)
# Have to recursively process parfors manually here.
for inst in block.body:
if isinstance(inst, parfor.Parfor):
# Recursively compute for the parfor's init block.
compute_def_once_block(inst.init_block, def_once, def_more, getattr_taken, typemap, module_assigns)
# Recursively compute for the parfor's loop body.
compute_def_once_internal(inst.loop_body, def_once, def_more, getattr_taken, typemap, module_assigns)
def compute_def_once(loop_body, typemap):
'''Compute the set of variables defined exactly once in the given set of blocks.
def_once = set() # set to hold variables defined exactly once
def_more = set() # set to hold variables defined more than once
getattr_taken = {}
module_assigns = {}
compute_def_once_internal(loop_body, def_once, def_more, getattr_taken, typemap, module_assigns)
return def_once, def_more
def find_vars(var, varset):
assert isinstance(var, ir.Var)
return var
def _hoist_internal(inst, dep_on_param, call_table, hoisted, not_hoisted,
typemap, stored_arrays):
if in stored_arrays:
not_hoisted.append((inst, "stored array"))
if config.DEBUG_ARRAY_OPT >= 1:
print("Instruction", inst, " could not be hoisted because the created array is stored.")
return False
uses = set()
visit_vars_inner(inst.value, find_vars, uses)
diff = uses.difference(dep_on_param)
if config.DEBUG_ARRAY_OPT >= 1:
print("_hoist_internal:", inst, "uses:", uses, "diff:", diff)
if len(diff) == 0 and is_pure(inst.value, None, call_table):
if config.DEBUG_ARRAY_OPT >= 1:
print("Will hoist instruction", inst, typemap[])
if not isinstance(typemap[], types.npytypes.Array):
dep_on_param += []
return True
if len(diff) > 0:
not_hoisted.append((inst, "dependency"))
if config.DEBUG_ARRAY_OPT >= 1:
print("Instruction", inst, " could not be hoisted because of a dependency.")
not_hoisted.append((inst, "not pure"))
if config.DEBUG_ARRAY_OPT >= 1:
print("Instruction", inst, " could not be hoisted because it isn't pure.")
return False
def find_setitems_block(setitems, itemsset, block, typemap):
for inst in block.body:
if isinstance(inst, ir.StaticSetItem) or isinstance(inst, ir.SetItem):
# If we store a non-mutable object into an array then that is safe to hoist.
# If the stored object is mutable and you hoist then multiple entries in the
# outer array could reference the same object and changing one index would then
# change other indices.
if getattr(typemap[], "mutable", False):
elif isinstance(inst, parfor.Parfor):
find_setitems_block(setitems, itemsset, inst.init_block, typemap)
find_setitems_body(setitems, itemsset, inst.loop_body, typemap)
def find_setitems_body(setitems, itemsset, loop_body, typemap):
Find the arrays that are written into (goes into setitems) and the
mutable objects (mostly arrays) that are written into other arrays
(goes into itemsset).
for label, block in loop_body.items():
find_setitems_block(setitems, itemsset, block, typemap)
def empty_container_allocator_hoist(inst, dep_on_param, call_table, hoisted,
not_hoisted, typemap, stored_arrays):
if (isinstance(inst, ir.Assign) and
isinstance(inst.value, ir.Expr) and
inst.value.op == 'call' and in call_table):
call_list = call_table[]
if call_list == ['empty', np]:
return _hoist_internal(inst, dep_on_param, call_table, hoisted,
not_hoisted, typemap, stored_arrays)
return False
def hoist(parfor_params, loop_body, typemap, wrapped_blocks):
dep_on_param = copy.copy(parfor_params)
hoisted = []
not_hoisted = []
# Compute the set of variable defined exactly once in the loop body.
def_once, def_more = compute_def_once(loop_body, typemap)
(call_table, reverse_call_table) = get_call_table(wrapped_blocks)
setitems = set()
itemsset = set()
find_setitems_body(setitems, itemsset, loop_body, typemap)
dep_on_param = list(set(dep_on_param).difference(setitems))
if config.DEBUG_ARRAY_OPT >= 1:
print("hoist - def_once:", def_once, "setitems:", setitems, "itemsset:", itemsset, "dep_on_param:", dep_on_param, "parfor_params:", parfor_params)
for si in setitems:
add_to_def_once_sets(si, def_once, def_more)
for label, block in loop_body.items():
new_block = []
for inst in block.body:
if empty_container_allocator_hoist(inst, dep_on_param, call_table,
hoisted, not_hoisted, typemap, itemsset):
elif isinstance(inst, ir.Assign) and in def_once:
if _hoist_internal(inst, dep_on_param, call_table,
hoisted, not_hoisted, typemap, itemsset):
# don't add this instruction to the block since it is
# hoisted
elif isinstance(inst, parfor.Parfor):
new_init_block = []
if config.DEBUG_ARRAY_OPT >= 1:
for ib_inst in inst.init_block.body:
if empty_container_allocator_hoist(ib_inst, dep_on_param,
call_table, hoisted, not_hoisted, typemap, itemsset):
elif (isinstance(ib_inst, ir.Assign) and in def_once):
if _hoist_internal(ib_inst, dep_on_param, call_table,
hoisted, not_hoisted, typemap, itemsset):
# don't add this instruction to the block since it is hoisted
inst.init_block.body = new_init_block
block.body = new_block
return hoisted, not_hoisted
def redtyp_is_scalar(redtype):
return not isinstance(redtype, types.npytypes.Array)
def redtyp_to_redarraytype(redtyp):
"""Go from a reducation variable type to a reduction array type used to hold
per-worker results.
redarrdim = 1
# If the reduction type is an array then allocate reduction array with ndim+1 dimensions.
if isinstance(redtyp, types.npytypes.Array):
redarrdim += redtyp.ndim
# We don't create array of array but multi-dimensional reduciton array with same dtype.
redtyp = redtyp.dtype
return types.npytypes.Array(redtyp, redarrdim, "C")
def redarraytype_to_sig(redarraytyp):
"""Given a reduction array type, find the type of the reduction argument to the gufunc.
Scalar and 1D array reduction both end up with 1D gufunc param type since scalars have to
be passed as arrays.
assert isinstance(redarraytyp, types.npytypes.Array)
return types.npytypes.Array(redarraytyp.dtype, max(1, redarraytyp.ndim - 1), redarraytyp.layout)
def legalize_names_with_typemap(names, typemap):
""" We use ir_utils.legalize_names to replace internal IR variable names
containing illegal characters (e.g. period) with a legal character
(underscore) so as to create legal variable names.
The original variable names are in the typemap so we also
need to add the legalized name to the typemap as well.
outdict = legalize_names(names)
# For each pair in the dict of legalized names...
for x, y in outdict.items():
# If the name had some legalization change to it...
if x != y:
# Set the type of the new name the same as the type of the old name.
typemap[y] = typemap[x]
return outdict
def to_scalar_from_0d(x):
if isinstance(x, types.ArrayCompatible):
if x.ndim == 0:
return x.dtype
return x
def _create_gufunc_for_parfor_body(
Takes a parfor and creates a gufunc function for its body.
There are two parts to this function.
1) Code to iterate across the iteration space as defined by the schedule.
2) The parfor body that does the work for a single point in the iteration space.
Part 1 is created as Python text for simplicity with a sentinel assignment to mark the point
in the IR where the parfor body should be added.
This Python text is 'exec'ed into existence and its IR retrieved with run_frontend.
The IR is scanned for the sentinel assignment where that basic block is split and the IR
for the parfor body inserted.
if config.DEBUG_ARRAY_OPT >= 1:
print("starting _create_gufunc_for_parfor_body")
loc = parfor.init_block.loc
# The parfor body and the main function body share ir.Var nodes.
# We have to do some replacements of Var names in the parfor body to make them
# legal parameter names. If we don't copy then the Vars in the main function also
# would incorrectly change their name.
loop_body = copy.copy(parfor.loop_body)
parfor_dim = len(parfor.loop_nests)
loop_indices = [ for l in parfor.loop_nests]
# Get all the parfor params.
parfor_params = parfor.params
# Get just the outputs of the parfor.
parfor_outputs = numba.parfors.parfor.get_parfor_outputs(parfor, parfor_params)
# Get all parfor reduction vars, and operators.
typemap = lowerer.fndesc.typemap
parfor_redvars, parfor_reddict = numba.parfors.parfor.get_parfor_reductions(
lowerer.func_ir, parfor, parfor_params, lowerer.fndesc.calltypes)
# Compute just the parfor inputs as a set difference.
parfor_inputs = sorted(
set(parfor_params) -
set(parfor_outputs) -
if config.DEBUG_ARRAY_OPT >= 1:
print("parfor_params = ", parfor_params, " ", type(parfor_params))
print("parfor_outputs = ", parfor_outputs, " ", type(parfor_outputs))
print("parfor_inputs = ", parfor_inputs, " ", type(parfor_inputs))
print("parfor_redvars = ", parfor_redvars, " ", type(parfor_redvars))
# -------------------------------------------------------------------------
# Convert tuples to individual parameters.
tuple_expanded_parfor_inputs = []
tuple_var_to_expanded_names = {}
expanded_name_to_tuple_var = {}
next_expanded_tuple_var = 0
parfor_tuple_params = []
# For each input to the parfor.
for pi in parfor_inputs:
# Get the type of the input.
pi_type = typemap[pi]
# If it is a UniTuple or Tuple we will do the conversion.
if isinstance(pi_type, types.UniTuple) or isinstance(pi_type, types.NamedUniTuple):
# Get the size and dtype of the tuple.
tuple_count = pi_type.count
tuple_dtype = pi_type.dtype
# Only do tuples up to config.PARFOR_MAX_TUPLE_SIZE length.
assert(tuple_count <= config.PARFOR_MAX_TUPLE_SIZE)
this_var_expansion = []
for i in range(tuple_count):
# Generate a new name for the individual part of the tuple var.
expanded_name = "expanded_tuple_var_" + str(next_expanded_tuple_var)
# Add that name to the new list of inputs to the gufunc.
# Remember a mapping from new param name to original tuple
# var and the index within the tuple.
expanded_name_to_tuple_var[expanded_name] = (pi, i)
next_expanded_tuple_var += 1
# Set the type of the new parameter.
typemap[expanded_name] = tuple_dtype
# Remember a mapping from the original tuple var to the
# individual parts.
tuple_var_to_expanded_names[pi] = this_var_expansion
elif isinstance(pi_type, types.Tuple) or isinstance(pi_type, types.NamedTuple):
# This is the same as above for UniTuple except that each part of
# the tuple can have a different type and we fetch that type with
# pi_type.types[offset].
tuple_count = pi_type.count
tuple_types = pi_type.types
# Only do tuples up to config.PARFOR_MAX_TUPLE_SIZE length.
assert(tuple_count <= config.PARFOR_MAX_TUPLE_SIZE)
this_var_expansion = []
for i in range(tuple_count):
expanded_name = "expanded_tuple_var_" + str(next_expanded_tuple_var)
expanded_name_to_tuple_var[expanded_name] = (pi, i)
next_expanded_tuple_var += 1
typemap[expanded_name] = tuple_types[i]
tuple_var_to_expanded_names[pi] = this_var_expansion
parfor_inputs = tuple_expanded_parfor_inputs
if config.DEBUG_ARRAY_OPT >= 1:
print("parfor_inputs post tuple handling = ", parfor_inputs, " ", type(parfor_inputs))
# -------------------------------------------------------------------------
races = races.difference(set(parfor_redvars))
for race in races:
msg = ("Variable %s used in parallel loop may be written "
"to simultaneously by multiple workers and may result "
"in non-deterministic or unintended results." % race)
warnings.warn(NumbaParallelSafetyWarning(msg, loc))
replace_var_with_array(races, loop_body, typemap, lowerer.fndesc.calltypes)
# Reduction variables are represented as arrays, so they go under
# different names.
parfor_redarrs = []
parfor_red_arg_types = []
for var in parfor_redvars:
arr = var + "_arr"
redarraytype = redtyp_to_redarraytype(typemap[var])
redarrsig = redarraytype_to_sig(redarraytype)
if arr in typemap:
assert(typemap[arr] == redarrsig)
typemap[arr] = redarrsig
# Reorder all the params so that inputs go first then outputs.
parfor_params = parfor_inputs + parfor_outputs + parfor_redarrs
if config.DEBUG_ARRAY_OPT >= 1:
print("parfor_params = ", parfor_params, " ", type(parfor_params))
print("loop_indices = ", loop_indices, " ", type(loop_indices))
print("loop_body = ", loop_body, " ", type(loop_body))
# Some Var are not legal parameter names so create a dict of potentially illegal
# param name to guaranteed legal name.
param_dict = legalize_names_with_typemap(parfor_params + parfor_redvars + parfor_tuple_params, typemap)
if config.DEBUG_ARRAY_OPT >= 1:
"param_dict = ",
" ",
# Some loop_indices are not legal parameter names so create a dict of potentially illegal
# loop index to guaranteed legal name.
ind_dict = legalize_names_with_typemap(loop_indices, typemap)
# Compute a new list of legal loop index names.
legal_loop_indices = [ind_dict[v] for v in loop_indices]
if config.DEBUG_ARRAY_OPT >= 1:
print("ind_dict = ", sorted(ind_dict.items()), " ", type(ind_dict))
"legal_loop_indices = ",
" ",
for pd in parfor_params:
print("pd = ", pd)
print("pd type = ", typemap[pd], " ", type(typemap[pd]))
# Get the types of each parameter.
param_types = [to_scalar_from_0d(typemap[v]) for v in parfor_params]
# Calculate types of args passed to gufunc.
func_arg_types = [typemap[v] for v in (parfor_inputs + parfor_outputs)] + parfor_red_arg_types
# Replace illegal parameter names in the loop body with legal ones.
replace_var_names(loop_body, param_dict)
# remember the name before legalizing as the actual arguments
parfor_args = parfor_params
# Change parfor_params to be legal names.
parfor_params = [param_dict[v] for v in parfor_params]
parfor_params_orig = parfor_params
parfor_params = []
ascontig = False
for pindex in range(len(parfor_params_orig)):
if (ascontig and
pindex < len(parfor_inputs) and
isinstance(param_types[pindex], types.npytypes.Array)):
# Change parfor body to replace illegal loop index vars with legal ones.
replace_var_names(loop_body, ind_dict)
loop_body_var_table = get_name_var_table(loop_body)
sentinel_name = get_unused_var_name("__sentinel__", loop_body_var_table)
if config.DEBUG_ARRAY_OPT >= 1:
"legal parfor_params = ",
" ",
# Determine the unique names of the scheduling and gufunc functions.
# sched_func_name = "__numba_parfor_sched_%s" % (hex(hash(parfor)).replace("-", "_"))
gufunc_name = "__numba_parfor_gufunc_%s" % (
hex(hash(parfor)).replace("-", "_"))
if config.DEBUG_ARRAY_OPT:
# print("sched_func_name ", type(sched_func_name), " ", sched_func_name)
print("gufunc_name ", type(gufunc_name), " ", gufunc_name)
gufunc_txt = ""
# Create the gufunc function.
gufunc_txt += "def " + gufunc_name + \
"(sched, " + (", ".join(parfor_params)) + "):\n"
globls = {"np": np}
# First thing in the gufunc, we reconstruct tuples from their
# individual parts, e.g., orig_tup_name = (part1, part2,).
# The rest of the code of the function will use the original tuple name.
for tup_var, exp_names in tuple_var_to_expanded_names.items():
tup_type = typemap[tup_var]
gufunc_txt += " " + param_dict[tup_var]
# Determine if the tuple is a named tuple.
if (isinstance(tup_type, types.NamedTuple) or
isinstance(tup_type, types.NamedUniTuple)):
named_tup = True
named_tup = False
if named_tup:
# It is a named tuple so try to find the global that defines the
# named tuple.
func_def = guard(get_definition, lowerer.func_ir, tup_var)
named_tuple_def = None
if config.DEBUG_ARRAY_OPT:
print("func_def:", func_def, type(func_def))
if func_def is not None:
if (isinstance(func_def, ir.Expr) and
func_def.op == 'call'):
named_tuple_def = guard(get_definition, lowerer.func_ir, func_def.func)
if config.DEBUG_ARRAY_OPT:
print("named_tuple_def:", named_tuple_def, type(named_tuple_def))
elif isinstance(func_def, ir.Arg):
named_tuple_def = typemap[]
if config.DEBUG_ARRAY_OPT:
print("named_tuple_def:", named_tuple_def,
if named_tuple_def is not None:
if (isinstance(named_tuple_def, ir.Global) or
isinstance(named_tuple_def, ir.FreeVar)):
gval = named_tuple_def.value
if config.DEBUG_ARRAY_OPT:
print("gval:", gval, type(gval))
globls[] = gval
elif isinstance(named_tuple_def, types.containers.BaseNamedTuple):
named_tuple_name ='(')[0]
if config.DEBUG_ARRAY_OPT:
print("name:", named_tuple_name,
globls[named_tuple_name] = named_tuple_def.instance_class
if config.DEBUG_ARRAY_OPT:
print("Didn't find definition of namedtuple for globls.")
raise CompilerError("Could not find definition of " + str(tup_var),
gufunc_txt += " = " + tup_type.instance_class.__name__ + "("
for name, field_name in zip(exp_names, tup_type.fields):
gufunc_txt += field_name + "=" + param_dict[name] + ","
# Just a regular tuple so use (part0, part1, ...)
gufunc_txt += " = (" + ", ".join([param_dict[x] for x in exp_names])
if len(exp_names) == 1:
# Add comma for tuples with singular values. We can't unilaterally
# add a comma alway because (,) isn't valid.
gufunc_txt += ","
gufunc_txt += ")\n"
for pindex in range(len(parfor_inputs)):
if ascontig and isinstance(param_types[pindex], types.npytypes.Array):
gufunc_txt += (" " + parfor_params_orig[pindex]
+ " = np.ascontiguousarray(" + parfor_params[pindex] + ")\n")
# Add initialization of reduction variables
for arr, var in zip(parfor_redarrs, parfor_redvars):
# If reduction variable is a scalar then save current value to
# temp and accumulate on that temp to prevent false sharing.
if redtyp_is_scalar(typemap[var]):
gufunc_txt += " " + param_dict[var] + \
"=" + param_dict[arr] + "[0]\n"
# The reduction variable is an array so np.copy it to a temp.
gufunc_txt += " " + param_dict[var] + \
"=np.copy(" + param_dict[arr] + ")\n"
# For each dimension of the parfor, create a for loop in the generated gufunc function.
# Iterate across the proper values extracted from the schedule.
# The form of the schedule is start_dim0, start_dim1, ..., start_dimN, end_dim0,
# end_dim1, ..., end_dimN
for eachdim in range(parfor_dim):
for indent in range(eachdim + 1):
gufunc_txt += " "
sched_dim = eachdim
gufunc_txt += ("for " +
legal_loop_indices[eachdim] +
" in range(sched[" +
str(sched_dim) +
"], sched[" +
str(sched_dim +
parfor_dim) +
"] + np.uint8(1)):\n")
for indent in range(parfor_dim + 1):
gufunc_txt += " "
gufunc_txt += "print("
for eachdim in range(parfor_dim):
gufunc_txt += "\"" + legal_loop_indices[eachdim] + "\"," + legal_loop_indices[eachdim] + ","
gufunc_txt += ")\n"
# Add the sentinel assignment so that we can find the loop body position
# in the IR.
for indent in range(parfor_dim + 1):
gufunc_txt += " "
gufunc_txt += sentinel_name + " = 0\n"
# Add assignments of reduction variables (for returning the value)
redargstartdim = {}
for arr, var in zip(parfor_redarrs, parfor_redvars):
# After the gufunc loops, copy the accumulated temp value back to reduction array.
if redtyp_is_scalar(typemap[var]):
gufunc_txt += " " + param_dict[arr] + \
"[0] = " + param_dict[var] + "\n"
redargstartdim[arr] = 1
# After the gufunc loops, copy the accumulated temp array back to reduction array with ":"
gufunc_txt += " " + param_dict[arr] + \
"[:] = " + param_dict[var] + "[:]\n"
redargstartdim[arr] = 0
gufunc_txt += " return None\n"
if config.DEBUG_ARRAY_OPT:
print("gufunc_txt = ", type(gufunc_txt), "\n", gufunc_txt)
# Force gufunc outline into existence.
locls = {}
exec(gufunc_txt, globls, locls)
gufunc_func = locls[gufunc_name]
if config.DEBUG_ARRAY_OPT:
print("gufunc_func = ", type(gufunc_func), "\n", gufunc_func)
# Get the IR for the gufunc outline.
gufunc_ir = compiler.run_frontend(gufunc_func)
if config.DEBUG_ARRAY_OPT:
print("gufunc_ir dump ", type(gufunc_ir))
print("loop_body dump ", type(loop_body))
# rename all variables in gufunc_ir afresh
var_table = get_name_var_table(gufunc_ir.blocks)
new_var_dict = {}
reserved_names = [sentinel_name] + \
list(param_dict.values()) + legal_loop_indices
for name, var in var_table.items():
if not (name in reserved_names):
new_var_dict[name] = mk_unique_var(name)
replace_var_names(gufunc_ir.blocks, new_var_dict)
if config.DEBUG_ARRAY_OPT:
print("gufunc_ir dump after renaming ")
gufunc_param_types = [types.npytypes.Array(
index_var_typ, 1, "C")] + param_types
if config.DEBUG_ARRAY_OPT:
"gufunc_param_types = ",
gufunc_stub_last_label = max(gufunc_ir.blocks.keys()) + 1
# Add gufunc stub last label to each parfor.loop_body label to prevent
# label conflicts.
loop_body = add_offset_to_labels(loop_body, gufunc_stub_last_label)
# new label for splitting sentinel block
new_label = max(loop_body.keys()) + 1
# If enabled, add a print statement after every assignment.
for label, block in loop_body.items():
new_block = block.copy()
loc = block.loc
scope = block.scope
for inst in block.body:
# Append print after assignment
if isinstance(inst, ir.Assign):
# Only apply to numbers
if typemap[] not in types.number_domain:
# Make constant string
strval = "{} =".format(
strconsttyp = types.StringLiteral(strval)
lhs = ir.Var(scope, mk_unique_var("str_const"), loc)
assign_lhs = ir.Assign(value=ir.Const(value=strval, loc=loc),
target=lhs, loc=loc)
typemap[] = strconsttyp
# Make print node
print_node = ir.Print(args=[lhs,], vararg=None, loc=loc)
sig = numba.core.typing.signature(types.none,
lowerer.fndesc.calltypes[print_node] = sig
loop_body[label] = new_block
if config.DEBUG_ARRAY_OPT:
print("parfor loop body")
wrapped_blocks = wrap_loop_body(loop_body)
hoisted, not_hoisted = hoist(parfor_params, loop_body, typemap, wrapped_blocks)
start_block = gufunc_ir.blocks[min(gufunc_ir.blocks.keys())]
start_block.body = start_block.body[:-1] + hoisted + [start_block.body[-1]]
# store hoisted into diagnostics
diagnostics = lowerer.metadata['parfor_diagnostics']
diagnostics.hoist_info[] = {'hoisted': hoisted,
'not_hoisted': not_hoisted}
if config.DEBUG_ARRAY_OPT:
print("After hoisting")
# Search all the block in the gufunc outline for the sentinel assignment.
for label, block in gufunc_ir.blocks.items():
for i, inst in enumerate(block.body):
if isinstance(
ir.Assign) and == sentinel_name:
# We found the sentinel assignment.
loc = inst.loc
scope = block.scope
# split block across __sentinel__
# A new block is allocated for the statements prior to the sentinel
# but the new block maintains the current block label.
prev_block = ir.Block(scope, loc)
prev_block.body = block.body[:i]
# The current block is used for statements after the sentinel.
block.body = block.body[i + 1:]
# But the current block gets a new label.
body_first_label = min(loop_body.keys())
# The previous block jumps to the minimum labelled block of the
# parfor body.
prev_block.append(ir.Jump(body_first_label, loc))
# Add all the parfor loop body blocks to the gufunc function's
# IR.
for (l, b) in loop_body.items():
gufunc_ir.blocks[l] = b
body_last_label = max(loop_body.keys())
gufunc_ir.blocks[new_label] = block
gufunc_ir.blocks[label] = prev_block
# Add a jump from the last parfor body block to the block containing
# statements after the sentinel.
ir.Jump(new_label, loc))
if config.DEBUG_ARRAY_OPT:
print("gufunc_ir last dump before renaming")
gufunc_ir.blocks = rename_labels(gufunc_ir.blocks)
if config.DEBUG_ARRAY_OPT:
print("gufunc_ir last dump")
print("flags", flags)
print("typemap", typemap)
old_alias = flags.noalias
if not has_aliases:
if config.DEBUG_ARRAY_OPT:
print("No aliases found so adding noalias flag.")
flags.noalias = True
kernel_func = compiler.compile_ir(
flags.noalias = old_alias
kernel_sig = signature(types.none, *gufunc_param_types)
if config.DEBUG_ARRAY_OPT:
print("finished create_gufunc_for_parfor_body. kernel_sig = ", kernel_sig)
return kernel_func, parfor_args, kernel_sig, redargstartdim, func_arg_types, expanded_name_to_tuple_var
def replace_var_with_array_in_block(vars, block, typemap, calltypes):
new_block = []
for inst in block.body:
if isinstance(inst, ir.Assign) and in vars:
const_node = ir.Const(0, inst.loc)
const_var = ir.Var(, mk_unique_var("$const_ind_0"), inst.loc)
typemap[] = types.uintp
const_assign = ir.Assign(const_node, const_var, inst.loc)
setitem_node = ir.SetItem(, const_var, inst.value, inst.loc)
calltypes[setitem_node] = signature(
types.none, types.npytypes.Array(typemap[], 1, "C"), types.intp, typemap[])
elif isinstance(inst, parfor.Parfor):
replace_var_with_array_internal(vars, {0: inst.init_block}, typemap, calltypes)
replace_var_with_array_internal(vars, inst.loop_body, typemap, calltypes)
return new_block
def replace_var_with_array_internal(vars, loop_body, typemap, calltypes):
for label, block in loop_body.items():
block.body = replace_var_with_array_in_block(vars, block, typemap, calltypes)
def replace_var_with_array(vars, loop_body, typemap, calltypes):
replace_var_with_array_internal(vars, loop_body, typemap, calltypes)
for v in vars:
el_typ = typemap[v]
typemap.pop(v, None)
typemap[v] = types.npytypes.Array(el_typ, 1, "C")
def call_parallel_gufunc(lowerer, cres, gu_signature, outer_sig, expr_args, expr_arg_types,
loop_ranges, redvars, reddict, redarrdict, init_block, index_var_typ, races,
Adds the call to the gufunc function from the main function.
context = lowerer.context
builder = lowerer.builder
from import (build_gufunc_wrapper,
if config.DEBUG_ARRAY_OPT:
print("outer_sig = ", outer_sig.args, outer_sig.return_type,
outer_sig.recvr, outer_sig.pysig)
print("loop_ranges = ", loop_ranges)
print("expr_args", expr_args)
print("expr_arg_types", expr_arg_types)
print("gu_signature", gu_signature)
# Build the wrapper for GUFunc
args, return_type = sigutils.normalize_signature(outer_sig)
llvm_func = cres.library.get_function(cres.fndesc.llvm_func_name)
sin, sout = gu_signature
# These are necessary for build_gufunc_wrapper to find external symbols
info = build_gufunc_wrapper(llvm_func, cres, sin, sout,
cache=False, is_parfors=True)
wrapper_name =
if config.DEBUG_ARRAY_OPT:
print("parallel function = ", wrapper_name, cres)
# loadvars for loop_ranges
def load_range(v):
if isinstance(v, ir.Var):
return lowerer.loadvar(
return context.get_constant(types.uintp, v)
num_dim = len(loop_ranges)
for i in range(num_dim):
start, stop, step = loop_ranges[i]
start = load_range(start)
stop = load_range(stop)
assert(step == 1) # We do not support loop steps other than 1
step = load_range(step)
loop_ranges[i] = (start, stop, step)
if config.DEBUG_ARRAY_OPT:
print("call_parallel_gufunc loop_ranges[{}] = ".format(i), start,
stop, step)
cgutils.printf(builder, "loop range[{}]: %d %d (%d)\n".format(i),
start, stop, step)
# Commonly used LLVM types and constants
byte_t =
byte_ptr_t = lc.Type.pointer(byte_t)
byte_ptr_ptr_t = lc.Type.pointer(byte_ptr_t)
intp_t = context.get_value_type(types.intp)
uintp_t = context.get_value_type(types.uintp)
intp_ptr_t = lc.Type.pointer(intp_t)
uintp_ptr_t = lc.Type.pointer(uintp_t)
zero = context.get_constant(types.uintp, 0)
one = context.get_constant(types.uintp, 1)
one_type = one.type
sizeof_intp = context.get_abi_sizeof(intp_t)
# Prepare sched, first pop it out of expr_args, outer_sig, and gu_signature
sched_sig = sin.pop(0)
if config.DEBUG_ARRAY_OPT:
print("Parfor has potentially negative start", index_var_typ.signed)
if index_var_typ.signed:
sched_type = intp_t
sched_ptr_type = intp_ptr_t
sched_type = uintp_t
sched_ptr_type = uintp_ptr_t
# Call do_scheduling with appropriate arguments
dim_starts = cgutils.alloca_once(
builder, sched_type, size=context.get_constant(
types.uintp, num_dim), name="dims")
dim_stops = cgutils.alloca_once(
builder, sched_type, size=context.get_constant(
types.uintp, num_dim), name="dims")
for i in range(num_dim):
start, stop, step = loop_ranges[i]
if start.type != one_type:
start = builder.sext(start, one_type)
if stop.type != one_type:
stop = builder.sext(stop, one_type)
if step.type != one_type:
step = builder.sext(step, one_type)
# substract 1 because do-scheduling takes inclusive ranges
stop = builder.sub(stop, one)
start, builder.gep(
dim_starts, [
types.uintp, i)])), builder.gep(dim_stops,
[context.get_constant(types.uintp, i)]))
sched_size = get_thread_count() * num_dim * 2
sched = cgutils.alloca_once(
builder, sched_type, size=context.get_constant(
types.uintp, sched_size), name="sched")
debug_flag = 1 if config.DEBUG_ARRAY_OPT else 0
scheduling_fnty = lc.Type.function(
intp_ptr_t, [uintp_t, sched_ptr_type, sched_ptr_type, uintp_t, sched_ptr_type, intp_t])
if index_var_typ.signed:
do_scheduling = builder.module.get_or_insert_function(scheduling_fnty,
do_scheduling = builder.module.get_or_insert_function(scheduling_fnty,
get_num_threads = builder.module.get_or_insert_function(
lc.Type.function(, []),
num_threads =, [])
with cgutils.if_unlikely(builder, builder.icmp_signed('<=', num_threads,
cgutils.printf(builder, "num_threads: %d\n", num_threads)
context.call_conv.return_user_exc(builder, RuntimeError,
("Invalid number of threads. "
"This likely indicates a bug in Numba.",))
do_scheduling, [
types.uintp, num_dim), dim_starts, dim_stops, num_threads,
sched, context.get_constant(
types.intp, debug_flag)])
# Get the LLVM vars for the Numba IR reduction array vars.
redarrs = [lowerer.loadvar(redarrdict[x].name) for x in redvars]
nredvars = len(redvars)
ninouts = len(expr_args) - nredvars
if config.DEBUG_ARRAY_OPT:
for i in range(get_thread_count()):
cgutils.printf(builder, "sched[" + str(i) + "] = ")
for j in range(num_dim * 2):
builder, "%d ", builder.load(
sched, [
types.intp, i * num_dim * 2 + j)])))
cgutils.printf(builder, "\n")
def load_potential_tuple_var(x):
"""Given a variable name, if that variable is not a new name
introduced as the extracted part of a tuple then just return
the variable loaded from its name. However, if the variable
does represent part of a tuple, as recognized by the name of
the variable being present in the exp_name_to_tuple_var dict,
then we load the original tuple var instead that we get from
the dict and then extract the corresponding element of the
tuple, also stored and returned to use in the dict (i.e., offset).
if x in exp_name_to_tuple_var:
orig_tup, offset = exp_name_to_tuple_var[x]
tup_var = lowerer.loadvar(orig_tup)
res = builder.extract_value(tup_var, offset)
return res
return lowerer.loadvar(x)
# ----------------------------------------------------------------------------
# Prepare arguments: args, shapes, steps, data
all_args = [load_potential_tuple_var(x) for x in expr_args[:ninouts]] + redarrs
num_args = len(all_args)
num_inps = len(sin) + 1
args = cgutils.alloca_once(
1 + num_args),
array_strides = []
# sched goes first, byte_ptr_t), args)
array_strides.append(context.get_constant(types.intp, sizeof_intp))
red_shapes = {}
rv_to_arg_dict = {}
# followed by other arguments
for i in range(num_args):
arg = all_args[i]
var = expr_args[i]
aty = expr_arg_types[i]
dst = builder.gep(args, [context.get_constant(types.intp, i + 1)])
if i >= ninouts: # reduction variables
ary = context.make_array(aty)(context, builder, arg)
strides = cgutils.unpack_tuple(builder, ary.strides, aty.ndim)
ary_shapes = cgutils.unpack_tuple(builder, ary.shape, aty.ndim)
# Start from 1 because we skip the first dimension of length num_threads just like sched.
for j in range(1, len(strides)):
red_shapes[i] = ary_shapes[1:], byte_ptr_t), dst)
elif isinstance(aty, types.ArrayCompatible):
if var in races:
typ = context.get_data_type(
aty.dtype) if aty.dtype != types.boolean else
rv_arg = cgutils.alloca_once(builder, typ), rv_arg), byte_ptr_t), dst)
rv_to_arg_dict[var] = (arg, rv_arg)
array_strides.append(context.get_constant(types.intp, context.get_abi_sizeof(typ)))
ary = context.make_array(aty)(context, builder, arg)
strides = cgutils.unpack_tuple(builder, ary.strides, aty.ndim)
for j in range(len(strides)):
array_strides.append(strides[j]), byte_ptr_t), dst)
if i < num_inps:
# Scalar input, need to store the value in an array of size 1
typ = context.get_data_type(
aty) if not isinstance(aty, types.Boolean) else
ptr = cgutils.alloca_once(builder, typ), ptr)
# Scalar output, must allocate
typ = context.get_data_type(
aty) if not isinstance(aty, types.Boolean) else
ptr = cgutils.alloca_once(builder, typ), byte_ptr_t), dst)
# ----------------------------------------------------------------------------
# Next, we prepare the individual dimension info recorded in gu_signature
sig_dim_dict = {}
occurances = []
occurances = [sched_sig[0]]
sig_dim_dict[sched_sig[0]] = context.get_constant(types.intp, 2 * num_dim)
assert len(expr_args) == len(all_args)
assert len(expr_args) == len(expr_arg_types)
assert len(expr_args) == len(sin + sout)
assert len(expr_args) == len(outer_sig.args[1:])
for var, arg, aty, gu_sig in zip(expr_args, all_args,
expr_arg_types, sin + sout):
if isinstance(aty, types.npytypes.Array):
i = aty.ndim - len(gu_sig)
i = 0
if config.DEBUG_ARRAY_OPT:
print("var =", var, "gu_sig =", gu_sig, "type =", aty, "i =", i)
for dim_sym in gu_sig:
if config.DEBUG_ARRAY_OPT:
print("var = ", var, " type = ", aty)
if var in races:
sig_dim_dict[dim_sym] = context.get_constant(types.intp, 1)
ary = context.make_array(aty)(context, builder, arg)
shapes = cgutils.unpack_tuple(builder, ary.shape, aty.ndim)
sig_dim_dict[dim_sym] = shapes[i]
if not (dim_sym in occurances):
if config.DEBUG_ARRAY_OPT:
print("dim_sym = ", dim_sym, ", i = ", i)
cgutils.printf(builder, dim_sym + " = %d\n", sig_dim_dict[dim_sym])
i = i + 1
# ----------------------------------------------------------------------------
# Prepare shapes, which is a single number (outer loop size), followed by
# the size of individual shape variables.
nshapes = len(sig_dim_dict) + 1
shapes = cgutils.alloca_once(builder, intp_t, size=nshapes, name="pshape")
# For now, outer loop size is the same as number of threads, shapes)
# Individual shape variables go next
i = 1
for dim_sym in occurances:
if config.DEBUG_ARRAY_OPT:
cgutils.printf(builder, dim_sym + " = %d\n", sig_dim_dict[dim_sym])
sig_dim_dict[dim_sym], builder.gep(
shapes, [
types.intp, i)]))
i = i + 1
# ----------------------------------------------------------------------------
# Prepare steps for each argument. Note that all steps are counted in
# bytes.
num_steps = num_args + 1 + len(array_strides)
steps = cgutils.alloca_once(
builder, intp_t, size=context.get_constant(
types.intp, num_steps), name="psteps")
# First goes the step size for sched, which is 2 * num_dim, 2 * num_dim * sizeof_intp),
# The steps for all others are 0, except for reduction results.
for i in range(num_args):
if i >= ninouts: # steps for reduction vars are abi_sizeof(typ)
j = i - ninouts
# Get the base dtype of the reduction array.
redtyp = lowerer.fndesc.typemap[redvars[j]]
red_stride = None
if isinstance(redtyp, types.npytypes.Array):
redtyp = redtyp.dtype
red_stride = red_shapes[i]
typ = context.get_value_type(redtyp)
sizeof = context.get_abi_sizeof(typ)
# Set stepsize to the size of that dtype.
stepsize = context.get_constant(types.intp, sizeof)
if red_stride is not None:
for rs in red_stride:
stepsize = builder.mul(stepsize, rs)
# steps are strides
stepsize = zero
dst = builder.gep(steps, [context.get_constant(types.intp, 1 + i)]), dst)
for j in range(len(array_strides)):
dst = builder.gep(
steps, [
types.intp, 1 + num_args + j)])[j], dst)
# ----------------------------------------------------------------------------
# prepare data
data = cgutils.get_null_value(byte_ptr_t)
fnty = lc.Type.function(lc.Type.void(), [byte_ptr_ptr_t, intp_ptr_t,
intp_ptr_t, byte_ptr_t])
fn = builder.module.get_or_insert_function(fnty, name=wrapper_name)
if config.DEBUG_ARRAY_OPT:
cgutils.printf(builder, "before calling kernel %p\n", fn), [args, shapes, steps, data])
if config.DEBUG_ARRAY_OPT:
cgutils.printf(builder, "after calling kernel %p\n", fn)
for k, v in rv_to_arg_dict.items():
arg, rv_arg = v
only_elem_ptr = builder.gep(rv_arg, [context.get_constant(types.intp, 0)]), lowerer.getvar(k))
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