---

name: "python-simplification" description: "CLI and Python API workflows for simplifying ONNX models." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Python Simplification

Use this sub-skill when the task is to simplify an ONNX model from a file or an onnx.ModelProto with onnxsim.simplify or the onnxsim CLI.

Covers

• Static and dynamic input shapes.
• Correctness checking with check_n, check_rtol, check_atol, and input_fill.
• Input data injection, optimizer skipping, and constant-folding control.
• Provider selection, including the CUDA shortcut behavior.
• External-data output and large tensor warnings.
• Target opset conversion and local function inlining.
• Custom operator schema import for validation.
• Graph diff output after simplification.

Do not use this sub-skill for

• Custom graph rewriter design, function-rule authoring, or profiling / model metrics depth. Hand those off to the sibling advanced-graph-control sub-skill (../advanced-graph-control/SKILL.md).
• Build, install, wheel, C API, Rust, WASM, or packaging troubleshooting. Hand those off to the sibling bindings-and-packaging sub-skill (../bindings-and-packaging/SKILL.md).

Read first

• API and CLI summary
• Workflow recipes
• Troubleshooting

Standard flow

1. Identify whether the caller has an ONNX file or an in-memory ModelProto.
2. Decide how shapes and validation inputs will be supplied: overwrite_input_shapes, test_input_shapes, input_data, or input_fill.
3. Choose optimizer and folding controls: skip_optimization, skip_constant_folding, skip_shape_inference, skip_fuse_bn, mutable_initializer, initializers_as_constants, and inline_functions.
4. Decide backend and opset behavior: providers, --cuda, and target_opset_version.
5. Run simplification, then validate with check_n and a direct round-trip comparison when needed.
6. Save external data or print graph diff only when asked.

Helper script

Use scripts/simplify_tiny_model.py for a safe smoke run:

python scripts/simplify_tiny_model.py --help
python scripts/simplify_tiny_model.py --check-n 3 --input-fill arange --print-summary
python scripts/simplify_tiny_model.py --output tiny-simplified.onnx

The script builds a tiny synthetic model, simplifies it with the public API, validates the result, and never downloads models.

Common outcomes

• File CLI calls return a simplified ONNX path and, when requested, graph diff output.
• Python API calls return (model_simp, check); treat check=False as a stop signal unless the caller explicitly wants an exploratory run.
• If providers is omitted, folding stays on CPU.
• For a fast smoke run, use the bundled helper script with --print-summary.

Routing notes

• The sub-skill accepts the core simplification surface only. For custom rewriters and profiling / metrics depth, move to the sibling advanced-graph-control sub-skill.
• For packaging or backend installation problems, move to the sibling bindings-and-packaging sub-skill.
• If a provider request fails, consult the troubleshooting table before changing the model.