blob: 4a2c2661b96cd5eff565407d5c42e5c33e506ae2 [file] [log] [blame]
import importlib
import inspect
import itertools
import warnings
from typing import Any, Callable, Dict, Tuple, Union
from torch import _C
from torch.onnx import _constants, errors
__all__ = [
"get_op_supported_version",
"get_ops_in_version",
"get_registered_op",
"is_registered_op",
"is_registered_version",
"register_op",
"register_ops_helper",
"register_ops_in_version",
"register_version",
"unregister_op",
]
_SymbolicFunction = Callable[..., Union[_C.Value, Tuple[_C.Value]]]
"""
The symbolic registry "_registry" is a dictionary that maps operators
(for a specific domain and opset version) to their symbolic functions.
An operator is defined by its domain, opset version, and opname.
The keys are tuples (domain, version), (where domain is a string, and version is an int),
and the operator's name (string).
The map's entries are as follows : _registry[(domain, version)][op_name] = op_symbolic
"""
_registry: Dict[
Tuple[str, int],
Dict[str, _SymbolicFunction],
] = {}
_symbolic_versions: Dict[Union[int, str], Any] = {}
def _import_symbolic_opsets():
for opset_version in itertools.chain(
_constants.onnx_stable_opsets, [_constants.onnx_main_opset]
):
module = importlib.import_module(f"torch.onnx.symbolic_opset{opset_version}")
global _symbolic_versions
_symbolic_versions[opset_version] = module
def register_version(domain: str, version: int):
if not is_registered_version(domain, version):
global _registry
_registry[(domain, version)] = {}
register_ops_in_version(domain, version)
def register_ops_helper(domain: str, version: int, iter_version: int):
for domain, op_name, op_func in get_ops_in_version(iter_version):
if not is_registered_op(op_name, domain, version):
register_op(op_name, op_func, domain, version)
def register_ops_in_version(domain: str, version: int):
# iterates through the symbolic functions of
# the specified opset version, and the previous
# opset versions for operators supported in
# previous versions.
# Opset 9 is the base version. It is selected as the base version because
# 1. It is the first opset version supported by PyTorch export.
# 2. opset 9 is more robust than previous opset versions. Opset versions like 7/8 have limitations
# that certain basic operators cannot be expressed in ONNX. Instead of basing on these limitations,
# we chose to handle them as special cases separately.
# Backward support for opset versions beyond opset 7 is not in our roadmap.
# For opset versions other than 9, by default they will inherit the symbolic functions defined in
# symbolic_opset9.py.
# To extend support for updated operators in different opset versions on top of opset 9,
# simply add the updated symbolic functions in the respective symbolic_opset{version}.py file.
# Checkout topk in symbolic_opset10.py, and upsample_nearest2d in symbolic_opset8.py for example.
iter_version = version
while iter_version != 9:
register_ops_helper(domain, version, iter_version)
if iter_version > 9:
iter_version = iter_version - 1
else:
iter_version = iter_version + 1
register_ops_helper(domain, version, 9)
def get_ops_in_version(version: int):
if not _symbolic_versions:
_import_symbolic_opsets()
members = inspect.getmembers(_symbolic_versions[version])
domain_opname_ops = []
for obj in members:
if isinstance(obj[1], type) and hasattr(obj[1], "domain"):
ops = inspect.getmembers(obj[1], predicate=inspect.isfunction)
for op in ops:
domain_opname_ops.append((obj[1].domain, op[0], op[1])) # type: ignore[attr-defined]
elif inspect.isfunction(obj[1]):
if obj[0] == "_len":
obj = ("len", obj[1])
if obj[0] == "_list":
obj = ("list", obj[1])
if obj[0] == "_any":
obj = ("any", obj[1])
if obj[0] == "_all":
obj = ("all", obj[1])
domain_opname_ops.append(("", obj[0], obj[1]))
return domain_opname_ops
def is_registered_version(domain: str, version: int):
global _registry
return (domain, version) in _registry
def register_op(opname, op, domain, version):
if domain is None or version is None:
warnings.warn(
"ONNX export failed. The ONNX domain and/or version to register are None."
)
global _registry
if not is_registered_version(domain, version):
_registry[(domain, version)] = {}
_registry[(domain, version)][opname] = op
def is_registered_op(opname: str, domain: str, version: int):
if domain is None or version is None:
warnings.warn("ONNX export failed. The ONNX domain and/or version are None.")
global _registry
return (domain, version) in _registry and opname in _registry[(domain, version)]
def unregister_op(opname: str, domain: str, version: int):
global _registry
if is_registered_op(opname, domain, version):
del _registry[(domain, version)][opname]
if not _registry[(domain, version)]:
del _registry[(domain, version)]
else:
warnings.warn("The opname " + opname + " is not registered.")
def get_op_supported_version(opname: str, domain: str, version: int):
iter_version = version
while iter_version <= _constants.onnx_main_opset:
ops = [(op[0], op[1]) for op in get_ops_in_version(iter_version)]
if (domain, opname) in ops:
return iter_version
iter_version += 1
return None
def get_registered_op(opname: str, domain: str, version: int) -> _SymbolicFunction:
if domain is None or version is None:
warnings.warn("ONNX export failed. The ONNX domain and/or version are None.")
global _registry
if not is_registered_op(opname, domain, version):
raise errors.UnsupportedOperatorError(
domain, opname, version, get_op_supported_version(opname, domain, version)
)
return _registry[(domain, version)][opname]