Flash Linear Attention Repo Skill

Use this repo skill for tasks involving Flash Linear Attention (FLA), the fla Python package, its Triton-based linear-attention operators, fused modules, Transformers-compatible model families, Kimi Delta Attention (KDA), context-parallel routes, and FLA-specific benchmark or maintainer workflows.

Read references/repo-provenance.md before deciding whether this skill is current for a checkout. If the commit, package version, public API, or evidence paths differ materially, refresh this skill before relying on detailed guidance.

Fast routing

User task	Read
Install/import FLA, choose CUDA/ROCm/XPU/NPU/CPU extras, inspect runtime versions, debug backend wheel or env-var issues.	sub-skills/setup-and-backends/SKILL.md
Call or maintain fla.ops functions, debug @dispatch fallback, select op tests, or reason about Triton kernel correctness.	sub-skills/ops-kernels-and-dispatch/SKILL.md
Use fla.layers, fla.modules, fla.models, Hugging Face AutoModelForCausalLM, generation, training, evaluation, or fused losses.	sub-skills/layers-and-models/SKILL.md
Work on KDA, KimiDeltaAttention, KDAConfig, chunk_kda, safe gates, FlashKDA/TileLang KDA, or context parallel.	sub-skills/kda-and-context-parallel/SKILL.md
Build correctness-gated benchmark commands, interpret speedups, run optimization loops, or summarize benchmark JSON.	sub-skills/benchmarking-and-optimization/SKILL.md

Minimal package setup facts

• Import package: fla.
• Main distribution: flash-linear-attention for layers/models plus core kernels.
• Core distribution: fla-core for fla.ops, fla.modules, and fla.utils-only users.
• Python: >=3.10.
• Base dependencies: transformers>=4.45.0 and einops.
• torch and the correct Triton flavor are intentionally selected through backend extras, not the bare package install.

Typical install choices:

# NVIDIA CUDA
pip install 'flash-linear-attention[cuda]'

# ROCm: install torch from the PyTorch ROCm index first, then FLA.
pip install --index-url https://download.pytorch.org/whl/rocm7.2 torch
pip install 'flash-linear-attention[rocm]'

# CPU import-only or limited CPU checks.
pip install --index-url https://download.pytorch.org/whl/cpu torch
pip install 'flash-linear-attention[cpu]'

For XPU, NPU/Ascend, source installs, and --no-deps workflows, route to sub-skills/setup-and-backends/SKILL.md.

Safe first checks

From the owning sub-skill directories, these bundled scripts avoid downloads, training, native tests, and destructive writes:

# Setup/import/backend visibility.
python sub-skills/setup-and-backends/scripts/check_fla_runtime.py --show-env-vars

# Public operator names/signatures without running kernels.
python sub-skills/ops-kernels-and-dispatch/scripts/inspect_fla_ops.py --filter gla

# Tiny layer/model construction without checkpoint downloads.
python sub-skills/layers-and-models/scripts/smoke_layer_model.py --device cpu

# KDA import and optional tiny CUDA KDA smoke.
python sub-skills/kda-and-context-parallel/scripts/smoke_kda.py --help

# Build a gated benchmark command without running it.
python sub-skills/benchmarking-and-optimization/scripts/fla_verify_op_command.py --op chunk_gla --base main

Use CUDA-requiring script flags only when the active environment intentionally has CUDA hardware and matching wheels.

Cross-cutting operating rules

• Do not treat CPU import success as proof that Triton/CUDA, ROCm, XPU, NPU, TileLang, FlashKDA, or context-parallel kernels work.
• Set FLA_* backend and cache environment variables before starting Python; dispatch decisions are made in the active process.
• For maintainer tasks in an FLA checkout, correctness gates come before performance claims. A red or skipped gate means no speedup can be promoted.
• Do not change public layer/model symbols, config fields, checkpoint compatibility, validated numerical precision, or tolerances without explicit approval.
• Keep KDA-specific gate and CP behavior out of generic ops guidance; KDA has its own per-key-dimension gate and context-parallel constraints.

References

• references/package-overview.md — package surfaces, supported workflow map, public exports, and optional dependency boundaries.
• references/troubleshooting.md — cross-cutting install/import/runtime triage before selecting a deeper sub-skill.
• references/repo-provenance.md — source commit, package version, evidence paths, and refresh checklist.
• references/repo-routing-metadata.json — structured router metadata for managed import tooling.