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# mypy: allow-untyped-defs
from __future__ import annotations
import logging
from typing import Sequence
from torch.onnx._internal._lazy_import import onnxscript_apis, onnxscript_ir as ir
# The opset domain for ONNX operators
_ONNX_DOMAIN = ""
logger = logging.getLogger(__name__)
def rename_inputs(model: ir.Model, new_names: Sequence[str]) -> None:
# TODO: Ensure the names do not have duplicates
for input, new_name in zip(model.graph.inputs, new_names):
input.metadata_props["pkg.torch.onnx.original_node_name"] = str(input.name)
input.name = new_name
def rename_outputs(model: ir.Model, new_names: Sequence[str]) -> None:
for output, new_name in zip(model.graph.outputs, new_names):
output.metadata_props["pkg.torch.onnx.original_node_name"] = str(output.name)
output.name = new_name
def add_torchlib_common_imports(model: ir.Model) -> None:
"""Hack to add torchlib common imports to the model."""
try:
# TODO(justinchuby): Remove this hack and improved onnxscript
from onnxscript.function_libs.torch_lib.ops import common as common_ops
model.opset_imports["pkg.onnxscript.torch_lib.common"] = 1
rank_func = ir.serde.deserialize_function(common_ops.Rank.to_function_proto())
is_scalar_func = ir.serde.deserialize_function(
common_ops.IsScalar.to_function_proto()
)
model.functions[rank_func.identifier()] = rank_func
model.functions[is_scalar_func.identifier()] = is_scalar_func
except Exception:
logger.exception("Failed to add torchlib common imports to the model.")
def _maybe_set_opset_version(
opset_imports: dict[str, int], domain: str, version: int | None
) -> None:
"""Set the opset version for the domain."""
if domain in opset_imports and opset_imports[domain] != 1:
# Already set
return
if domain == _ONNX_DOMAIN:
# Set the default opset version for ONNX operators
opset_imports[domain] = onnxscript_apis.torchlib_opset_version()
return
if version is None:
# We don't know the opset version, so set it to 1
# This is valid for the custom function domains like "pkg.torch.__subgraph__"
opset_imports[domain] = 1
return
# Set the known opset version for the domain
opset_imports[domain] = version
def add_opset_imports(model: ir.Model) -> None:
"""Collect all opsets used and add opset imports to the model and functions."""
for node in ir.traversal.RecursiveGraphIterator(model.graph):
domain = node.domain
_maybe_set_opset_version(model.opset_imports, domain, node.version)
for function in model.functions.values():
for node in ir.traversal.RecursiveGraphIterator(function):
domain = node.domain
_maybe_set_opset_version(function.opset_imports, domain, node.version)
for domain, version in function.opset_imports.items():
# Add all opsets used in the function to the model, because ONNX Runtime
# does not handle adding the opset imports to the model after inlining during inference.
# This should happen after all opsets are collected for the function from its nodes.
_maybe_set_opset_version(model.opset_imports, domain, version)
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