---

name: onnxsim description: "Repo skill for ONNX Simplifier: ONNX model simplification, graph rewriting, metrics, profiling, packaging, and bindings." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ONNX Simplifier (onnxsim)

Use this repo skill when a task involves ONNX Simplifier / onnxsim: simplifying exported ONNX graphs, validating simplified models, controlling optimizer and constant-folding behavior, using custom rewrite rules, inspecting model metrics or profile traces, or maintaining the package's Python/C++/Rust/WASM bindings.

Start here

• Read package overview for package identity, install commands, backend model, and route selection.
• Read cross-cutting troubleshooting when the failure surface is unclear.
• Read repository provenance before deciding whether this skill is current for a checkout or before running a refresh.
• references/repo-routing-metadata.json is structured metadata for managed repo-skill import; do not hand-edit router Markdown.

Minimal installed-package check

python -m pip install -U onnxsim
python -c "import onnxsim; print(onnxsim.__version__)"
onnxsim --help
onnxsim --list-default-optimizers

If an environment may be broken, run the bundled checker from this skill root:

python scripts/check_onnxsim_env.py --help
python scripts/check_onnxsim_env.py --smoke --json
python scripts/check_onnxsim_env.py --providers CPUExecutionProvider --json

The checker imports onnxsim, verifies the compiled extension, reports optional ONNX Runtime providers, probes the CLI optimizer list, and can run a tiny in-memory simplification smoke.

Route by task

User task	Read
Simplify an ONNX file with the CLI, set input shapes, run correctness checks, choose CPU/CUDA providers, save external data, convert target opset, or use custom operator schemas	sub-skills/python-simplification/SKILL.md
Call onnxsim.simplify from Python with ModelProto objects, inspect the full API signature, map CLI flags to parameters, or recover from model/check/provider errors	sub-skills/python-simplification/SKILL.md
Author Python custom_rewriter callbacks, pure-data FunctionProto rewrite rules, or custom schema import logic	sub-skills/advanced-graph-control/SKILL.md
Use ModelInfo, MACs/FLOPs/memory metrics, annotate_metadata, diff_graphs, --graph-diff, profile, ort_profile, or merged ONNX Runtime traces	sub-skills/advanced-graph-control/SKILL.md
Install/build from source, diagnose missing submodules/nanobind/protobuf/CMake, build the C API, use Rust crates, build WASM/npm/web demo, or check release versions	sub-skills/bindings-and-packaging/SKILL.md

Key operating facts

• onnxsim.simplify(model, ...) accepts an ONNX file path or an in-memory onnx.ModelProto and returns (simplified_model, check_ok).
• CPU constant folding is the default. onnxruntime is optional for the Python package; without it, onnxsim can use ONNX's reference evaluator.
• Non-CPU execution providers require ONNX Runtime with the matching provider build. --cuda means --providers CUDAExecutionProvider CPUExecutionProvider.
• Custom operator schemas registered in Python ONNX are imported into onnxsim's registry by default; CLI users can disable that with --skip-schema-import.
• Python custom_rewriter callbacks and pure-data function_rewrite_rules are mutually exclusive in one simplify() call.
• FunctionProto rewrite rules are portable to C/Rust bindings because they are protobuf data matched in the C++ core.
• ModelInfo and annotate_metadata are static inspection tools; unknown shapes or missing optional sympy can make metrics best-effort or symbolic.
• The Python wheel/editable build uses -DONNXSIM_BUILTIN_ORT=OFF; it does not compile vendored ONNX Runtime C++. ONNX Runtime C++ builds belong to standalone CMake/C API/Rust native-library/default-WASM paths.

Bundled scripts

• scripts/check_onnxsim_env.py: installed package, provider, CLI, and tiny simplification smoke checker.
• sub-skills/python-simplification/scripts/simplify_tiny_model.py: safe tiny model simplification helper.
• sub-skills/advanced-graph-control/scripts/function_rule_smoke.py: pure-data FunctionProto rewrite smoke.
• sub-skills/advanced-graph-control/scripts/model_metrics_smoke.py: metrics, metadata, and graph-diff smoke.
• sub-skills/bindings-and-packaging/scripts/check_version_sync.py: read-only version metadata checker for a user-supplied source tree.

Safe defaults

• Prefer a released wheel plus onnxsim --help / --list-default-optimizers for ordinary use.
• Use check_n, deterministic input_fill, and explicit test_input_shapes when correctness matters.
• Start optional provider and build investigations with help/version/smoke checks; do not install heavy CUDA/QNN/TVM/Halide/model-export stacks or launch long WASM/Rust/native builds unless the user explicitly selected that path.
• Treat check_ok=False, provider validation errors, and schema validation errors as blockers for deployment-style tasks until the user accepts the risk or the issue is fixed.