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name: fx-graph-workflows description: "Operate Optimum Torch FX graph transformations and version-gated tensor parallelism guidance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

fx-graph-workflows

Use this sub-skill when a task involves Optimum's Torch FX graph workflow surface:

• CPU-compatible graph rewrites from optimum.fx.optimization.
• Custom Transformation / ReversibleTransformation classes, compose(...), and reverse=True restoration.
• GraphModule lint/recompile behavior and computation-preserving validation.
• Cautious triage for optional automatic tensor parallelism from optimum.fx.parallelization.

Do not use this sub-skill for GPTQ quantization, exporter task mapping, CLI export commands, dummy inputs, or normalized configs. Route those to the generated gptq-quantization, exporters-and-cli, or utilities-and-configs sub-skills.

Linked runtime files

• references/fx-optimization.md — transformation APIs, GraphModule expectations, built-in transformations, compose/reverse behavior, and local validation patterns.
• references/tensor-parallelism.md — optional automatic tensor-parallel API map, backend/version gates, layer replacements, and safe triage.
• references/troubleshooting.md — common failures and recoveries for FX imports, graph mutation, preservation checks, native-test downloads, Python 3.11 tensor-parallel import errors, and CUDA/NCCL requirements.
• scripts/fx_transform_smoke.py — deterministic CPU smoke test adapted from transformation behavior tests using only a tiny local torch.nn.Module.

Operating sequence

1. Confirm the user is asking for FX graph transformations or tensor-parallel triage, not export/quantization/config utilities.

2. For optimization work, require a torch.fx.GraphModule or trace a local torch.nn.Module into one. Keep inputs local and deterministic; avoid Hub downloads unless the user explicitly authorizes them.

3. Read references/fx-optimization.md before changing a graph. Apply transformations with default lint/recompile unless batching a compose chain.

4. Run the bundled smoke test when validating the local environment:

   python scripts/fx_transform_smoke.py
   python scripts/fx_transform_smoke.py --check-compose
   

5. For tensor parallelism, read references/tensor-parallelism.md first. Treat it as optional/advanced unless the environment has a compatible Python, CUDA-capable PyTorch with torch.compile, initialized distributed/NCCL process groups, and enough GPUs for the requested world size.

6. Use references/troubleshooting.md whenever import, graph lint/recompile, preservation, reverse, or backend errors appear.