def lower(sch, args, shape_params=None, name="default_function", binds=None, attrs=None,
simple_mode=False, polyhedral=False, tuning=False, target="cce"):
"""Lowering function."""
tmp_binds = None
if binds is not None:
tmp_binds = None if not bool(binds) else binds
tmp_attrs = None
if attrs is not None:
tmp_attrs = None if not bool(attrs) else attrs
if shape_params is None:
shape_params = []
cfg = _api_internal._GetCurrentBuildConfig()
ret = _api_internal._Lower(sch, args, shape_params, name,
tmp_binds, tmp_attrs, simple_mode,
polyhedral, tuning, target, cfg)
level = tmp_attrs.get("help_tiling")
if tuning or (level is not None and level > help_tiling_level['None']):
level = help_tiling_level['Tuning'] if tuning else level
global tuning_spaces
tuning_spaces = {}
tuning_spaces["index"] = ret.index_table.asnumpy().tolist()
tuning_spaces["c1_range"] = ret.c1_tile_range_table.asnumpy().tolist()
tuning_spaces["c0_range"] = ret.c0_tile_range_table.asnumpy().tolist()
tuning_spaces["c1_mod"] = ret.c1_tile_mod_table.asnumpy().tolist()
tuning_spaces["c0_mod"] = ret.c0_tile_mod_table.asnumpy().tolist()
if level >= help_tiling_level["Candidates"]:
tuning_spaces["tuning_space"] = ret.tiling_candidate.asnumpy().tolist()
if not tuning:
dump_tiling_info(level)
return ret
def build_to_func(inputs, args, shape_params=None, name="default_function",
binds=None, attrs=None, polyhedral=False, target="cce"):
"""Build module."""
tmp_binds = None
if binds is not None:
tmp_binds = None if not bool(binds) else binds
tmp_attrs = None
if attrs is not None:
tmp_attrs = None if not bool(attrs) else attrs
for arg in args:
vc_util.tensor_max_size_check(arg)
if shape_params is None:
shape_params = []
cfg = _api_internal._GetCurrentBuildConfig()
return _api_internal._BuildToFunc(inputs, args, shape_params, name, tmp_binds, tmp_attrs,
polyhedral, target, cfg)
def lower(sch, args, shape_params=None, name="default_function", binds=None, attrs=None,
simple_mode=False, polyhedral=False, tuning=False, target="cce"):
"""Lowering function."""
tmp_binds = None
if binds is not None:
tmp_binds = None if not bool(binds) else binds
tmp_attrs = None
if attrs is not None:
tmp_attrs = None if not bool(attrs) else attrs
if shape_params is None:
shape_params = []
cfg = _api_internal._GetCurrentBuildConfig()
ret = _api_internal._Lower(sch, args, shape_params, name,
tmp_binds, tmp_attrs, simple_mode,
polyhedral, tuning, target, cfg)
level = tmp_attrs.get("help_tiling")
if attrs.get("use_new_space", False):
# new space: constraints format
print("NEW SPACE: ", ret)
space = dict()
space['tune_schedule'] = ret.tune_schedule
space['tune_constraints'] = ret.tune_constraints
return space
elif tuning or (level is not None and level > help_tiling_level['None']):
level = help_tiling_level['Tuning'] if tuning else level
global tuning_spaces
tuning_spaces = {}
tuning_spaces["index"] = ret.index_table.asnumpy().tolist()
tuning_spaces["c1_range"] = ret.c1_tile_range_table.asnumpy().tolist()
tuning_spaces["c0_range"] = ret.c0_tile_range_table.asnumpy().tolist()
tuning_spaces["c1_mod"] = ret.c1_tile_mod_table.asnumpy().tolist()
tuning_spaces["c0_mod"] = ret.c0_tile_mod_table.asnumpy().tolist()
tuning_spaces["thread_range"] = ret.gpu_thread_range_table.asnumpy().tolist()
tuning_spaces["block_range"] = ret.gpu_block_range_table.asnumpy().tolist()
tuning_spaces["thread_mod"] = ret.gpu_thread_mod_table.asnumpy().tolist()
tuning_spaces["block_mod"] = ret.gpu_block_mod_table.asnumpy().tolist()
if level >= help_tiling_level["Candidates"]:
tuning_spaces["tuning_space"] = ret.tiling_candidate.asnumpy().tolist()
if not tuning:
dump_tiling_info(level)
return ret
