def test1(self):
typingctx = typing.Context()
targetctx = cpu.CPUContext(typingctx)
test_ir = compiler.run_frontend(test_will_propagate)
#print("Num blocks = ", len(test_ir.blocks))
#print(test_ir.dump())
with cpu_target.nested_context(typingctx, targetctx):
typingctx.refresh()
targetctx.refresh()
args = (types.int64, types.int64, types.int64)
typemap, return_type, calltypes = compiler.type_inference_stage(typingctx, test_ir, args, None)
#print("typemap = ", typemap)
#print("return_type = ", return_type)
type_annotation = type_annotations.TypeAnnotation(
func_ir=test_ir,
typemap=typemap,
calltypes=calltypes,
lifted=(),
lifted_from=None,
args=args,
return_type=return_type,
html_output=config.HTML)
remove_dels(test_ir.blocks)
in_cps, out_cps = copy_propagate(test_ir.blocks, typemap)
apply_copy_propagate(test_ir.blocks, in_cps, get_name_var_table(test_ir.blocks), typemap, calltypes)
remove_dead(test_ir.blocks, test_ir.arg_names, test_ir)
self.assertFalse(findLhsAssign(test_ir, "x"))
def test1(self):
typingctx = typing.Context()
targetctx = cpu.CPUContext(typingctx)
test_ir = compiler.run_frontend(test_will_propagate)
#print("Num blocks = ", len(test_ir.blocks))
#print(test_ir.dump())
with cpu_target.nested_context(typingctx, targetctx):
typingctx.refresh()
targetctx.refresh()
args = (types.int64, types.int64, types.int64)
typemap, return_type, calltypes = compiler.type_inference_stage(typingctx, test_ir, args, None)
#print("typemap = ", typemap)
#print("return_type = ", return_type)
type_annotation = type_annotations.TypeAnnotation(
func_ir=test_ir,
typemap=typemap,
calltypes=calltypes,
lifted=(),
lifted_from=None,
args=args,
return_type=return_type,
html_output=config.HTML)
remove_dels(test_ir.blocks)
in_cps, out_cps = copy_propagate(test_ir.blocks, typemap)
apply_copy_propagate(test_ir.blocks, in_cps, get_name_var_table(test_ir.blocks), typemap, calltypes)
remove_dead(test_ir.blocks, test_ir.arg_names, test_ir)
self.assertFalse(findLhsAssign(test_ir, "x"))
def test_test1(self):
typingctx = typing.Context()
targetctx = cpu.CPUContext(typingctx)
test_ir = compiler.run_frontend(test1)
with cpu_target.nested_context(typingctx, targetctx):
one_arg = numba.types.npytypes.Array(
numba.types.scalars.Float(name="float64"), 1, 'C')
args = (one_arg, one_arg, one_arg, one_arg, one_arg)
tp = TestPipeline(typingctx, targetctx, args, test_ir)
numba.rewrites.rewrite_registry.apply('before-inference', tp,
tp.func_ir)
tp.typemap, tp.return_type, tp.calltypes = compiler.type_inference_stage(
tp.typingctx, tp.func_ir, tp.args, None)
type_annotation = type_annotations.TypeAnnotation(
func_ir=tp.func_ir,
typemap=tp.typemap,
calltypes=tp.calltypes,
lifted=(),
lifted_from=None,
args=tp.args,
return_type=tp.return_type,
html_output=config.HTML)
numba.rewrites.rewrite_registry.apply('after-inference', tp,
tp.func_ir)
parfor_pass = numba.parfor.ParforPass(tp.func_ir, tp.typemap,
tp.calltypes, tp.return_type,
tp.typingctx)
parfor_pass.run()
self.assertTrue(countParfors(test_ir) == 1)
def compile_ir(self, the_ir, args=(), return_type=None):
typingctx = self.typingctx
targetctx = self.targetctx
flags = self.flags
# Register the contexts in case for nested @jit or @overload calls
with cpu_target.nested_context(typingctx, targetctx):
return compile_ir(typingctx, targetctx, the_ir, args,
return_type, flags, locals={})
def countParfors(test_func, args, **kws):
typingctx = typing.Context()
targetctx = cpu.CPUContext(typingctx)
test_ir = compiler.run_frontend(test_func)
if kws:
options = cpu.ParallelOptions(kws)
else:
options = cpu.ParallelOptions(True)
with cpu_target.nested_context(typingctx, targetctx):
tp = TestPipeline(typingctx, targetctx, args, test_ir)
inline_pass = inline_closurecall.InlineClosureCallPass(
tp.func_ir, options)
inline_pass.run()
numba.rewrites.rewrite_registry.apply('before-inference', tp,
tp.func_ir)
tp.typemap, tp.return_type, tp.calltypes = compiler.type_inference_stage(
tp.typingctx, tp.func_ir, tp.args, None)
type_annotations.TypeAnnotation(func_ir=tp.func_ir,
typemap=tp.typemap,
calltypes=tp.calltypes,
lifted=(),
lifted_from=None,
args=tp.args,
return_type=tp.return_type,
html_output=config.HTML)
preparfor_pass = numba.parfor.PreParforPass(tp.func_ir, tp.typemap,
tp.calltypes, tp.typingctx,
options)
preparfor_pass.run()
numba.rewrites.rewrite_registry.apply('after-inference', tp,
tp.func_ir)
parfor_pass = numba.parfor.ParforPass(tp.func_ir, tp.typemap,
tp.calltypes, tp.return_type,
tp.typingctx, options)
parfor_pass.run()
ret_count = 0
for label, block in test_ir.blocks.items():
for i, inst in enumerate(block.body):
if isinstance(inst, numba.parfor.Parfor):
ret_count += 1
return ret_count
def compile(self, func, args, return_type=None, flags=DEFAULT_FLAGS):
"""
Compile the function or retrieve an already compiled result
from the cache.
"""
from numba.targets.registry import cpu_target
cache_key = (func, args, return_type, flags)
try:
cr = self.cr_cache[cache_key]
except KeyError:
# Register the contexts in case for nested @jit or @overload calls
# (same as compile_isolated())
with cpu_target.nested_context(self.typingctx, self.targetctx):
cr = compile_extra(self.typingctx, self.targetctx, func,
args, return_type, flags, locals={})
self.cr_cache[cache_key] = cr
return cr
def compile(self, func, args, return_type=None, flags=DEFAULT_FLAGS):
"""
Compile the function or retrieve an already compiled result
from the cache.
"""
from numba.targets.registry import cpu_target
cache_key = (func, args, return_type, flags)
try:
cr = self.cr_cache[cache_key]
except KeyError:
# Register the contexts in case for nested @jit or @overload calls
# (same as compile_isolated())
with cpu_target.nested_context(self.typingctx, self.targetctx):
cr = compile_extra(self.typingctx, self.targetctx, func,
args, return_type, flags, locals={})
self.cr_cache[cache_key] = cr
return cr
def get_stencil_ir(sf, typingctx, args, scope, loc, input_dict, typemap,
calltypes):
"""get typed IR from stencil bytecode
"""
from numba.targets.cpu import CPUContext
from numba.targets.registry import cpu_target
from numba.annotations import type_annotations
from numba.compiler import type_inference_stage
# get untyped IR
stencil_func_ir = sf.kernel_ir.copy()
# copy the IR nodes to avoid changing IR in the StencilFunc object
stencil_blocks = copy.deepcopy(stencil_func_ir.blocks)
stencil_func_ir.blocks = stencil_blocks
name_var_table = ir_utils.get_name_var_table(stencil_func_ir.blocks)
if "out" in name_var_table:
raise ValueError("Cannot use the reserved word 'out' in stencil kernels.")
# get typed IR with a dummy pipeline (similar to test_parfors.py)
targetctx = CPUContext(typingctx)
with cpu_target.nested_context(typingctx, targetctx):
tp = DummyPipeline(typingctx, targetctx, args, stencil_func_ir)
numba.rewrites.rewrite_registry.apply(
'before-inference', tp, tp.func_ir)
tp.typemap, tp.return_type, tp.calltypes = type_inference_stage(
tp.typingctx, tp.func_ir, tp.args, None)
type_annotations.TypeAnnotation(
func_ir=tp.func_ir,
typemap=tp.typemap,
calltypes=tp.calltypes,
lifted=(),
lifted_from=None,
args=tp.args,
return_type=tp.return_type,
html_output=numba.config.HTML)
# make block labels unique
stencil_blocks = ir_utils.add_offset_to_labels(stencil_blocks,
ir_utils.next_label())
min_label = min(stencil_blocks.keys())
max_label = max(stencil_blocks.keys())
ir_utils._max_label = max_label
if config.DEBUG_ARRAY_OPT == 1:
print("Initial stencil_blocks")
ir_utils.dump_blocks(stencil_blocks)
# rename variables,
var_dict = {}
for v, typ in tp.typemap.items():
new_var = ir.Var(scope, mk_unique_var(v), loc)
var_dict[v] = new_var
typemap[new_var.name] = typ # add new var type for overall function
ir_utils.replace_vars(stencil_blocks, var_dict)
if config.DEBUG_ARRAY_OPT == 1:
print("After replace_vars")
ir_utils.dump_blocks(stencil_blocks)
# add call types to overall function
for call, call_typ in tp.calltypes.items():
calltypes[call] = call_typ
arg_to_arr_dict = {}
# replace arg with arr
for block in stencil_blocks.values():
for stmt in block.body:
if isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Arg):
if config.DEBUG_ARRAY_OPT == 1:
print("input_dict", input_dict, stmt.value.index,
stmt.value.name, stmt.value.index in input_dict)
arg_to_arr_dict[stmt.value.name] = input_dict[stmt.value.index].name
stmt.value = input_dict[stmt.value.index]
if config.DEBUG_ARRAY_OPT == 1:
print("arg_to_arr_dict", arg_to_arr_dict)
print("After replace arg with arr")
ir_utils.dump_blocks(stencil_blocks)
ir_utils.remove_dels(stencil_blocks)
stencil_func_ir.blocks = stencil_blocks
return stencil_func_ir, sf.get_return_type(args)[0], arg_to_arr_dict