---

name: acceleration-backends description: "Diagnose and choose TurboDiffusion CUDA custom-op, INT8/FastNorm, SLA, and SageSLA acceleration backends." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Acceleration Backends

Use this sub-skill when the task concerns TurboDiffusion backend readiness or acceleration choices: CUDA extension builds, turbo_diffusion_ops, INT8 Linear, FastRMSNorm/FastLayerNorm, SLA/SageSLA, attention_type, sla_topk, quant_linear, default_norm, or source-layout import quirks.

Route first

• For backend installation, extension build requirements, CUDA_HOME, nvcc, CUTLASS, and custom-op import checks, read backend-build.md.
• For API and option semantics around SparseLinearAttention, SageSparseLinearAttention, Int8Linear, FastNorm modules, and model replacement helpers, read acceleration-api.md.
• For symptom-to-fix matrices covering CUDA headers, turbo_diffusion_ops, flash-attn, missing SpargeAttn, SAGESLA_ENABLED = false, source-layout imports, and tiny SLA smoke warnings, read troubleshooting.md.
• To inspect a user's current environment without downloads or model execution, run check_acceleration_backend.py.

Boundaries

• User-facing T2V/I2V launch construction belongs to the sibling video-inference sub-skill.
• Interactive TUI serving belongs to the sibling interactive-serving sub-skill.
• Training launch, checkpoint conversion, and quantized checkpoint export command construction belong to the sibling training-and-checkpoints sub-skill.
• TurboT2AV's TileLang W8A8/FastNorm stack and Pixi/LTX-2 environment belong to the sibling turbot2av-extension sub-skill; only compare the concepts here.

Operating procedure

1. Establish whether the user is checking an installed package, a source build, or runtime flag choices. Do not ask them to download checkpoints for backend-only checks.

2. Run the safe diagnostic script when backend state is unknown:

   python sub-skills/acceleration-backends/scripts/check_acceleration_backend.py
   

   Add --require-cuda only when the requested capability truly needs CUDA custom ops.

3. If attention_type=sagesla is requested, require SpargeAttn-backed SageSparseLinearAttention; otherwise route to the sla or original fallback decision in acceleration-api.md.

4. If a quantized checkpoint is used, require both --quant_linear and working turbo_diffusion_ops; if an unquantized checkpoint is used, do not add --quant_linear by habit.

5. If a command is run from a source checkout and imports fail for top-level modules such as imaginaire, rcm, ops, SLA, or serve, apply the source-layout PYTHONPATH note in backend-build.md instead of hard-coding local paths.