Workflow for FLA backend dispatch decorators and backend implementations. Use when touching fla.ops.backends, @dispatch-decorated functions, BaseBackend subclasses, backend verifier methods, backend env vars, or backend tests.
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Workflow for FLA backend dispatch decorators and backend implementations. Use when touching fla.ops.backends, @dispatch-decorated functions, BaseBackend subclasses, backend verifier methods, backend env vars, or backend tests.
FLA Dispatch Backends Skill
Use this skill for the runtime backend dispatch system implemented in
fla/ops/backends/__init__.py.
Core model
Public functions opt in with @dispatch('<operation>').
First call lazily imports fla.ops.<operation>.backends, unless the operation
has a custom module in _OPERATION_BACKEND_MODULES (for example modules).
Backend modules create BackendRegistry('<operation>') and register
BaseBackend subclasses.
Dispatch tries registered backends sorted by priority where lower means
higher priority.
A backend is considered only when is_available() and is_enabled() are both
true.
Runtime dispatch checks is_available() and is_enabled() directly; do not
rely on the cached can_use() path inside code that must be torch.compile
friendly.
If <func_name>_verifier exists, it must return (True, None) or
(False, reason). Rejected calls fall back to the next backend.
If no backend handles the call, dispatch runs the original implementation.
FLA_DISABLE_BACKEND_DISPATCH=1 bypasses the decorator entirely.
The dispatch wrapper is marked with torch.compiler.disable, so keep backend
selection logic outside compiled graphs and keep compiled work inside the
selected backend implementation.
Backend implementation checklist
For a new backend:
Add a BaseBackend subclass under the operation's backends/ package.
Set backend_type, package_name, env_var, default_enable, and priority.
Implement <public_function_name>_verifier(...) with the same public call
surface as the decorated function.
Implement <public_function_name>(...) and keep return values identical to
the default implementation.
Register the backend in the operation's backends/__init__.py.
Add tests that cover accepted dispatch, verifier rejection, and fallback.
Verifier rules
Verifiers must be cheap, deterministic, and side-effect free.
Return a specific rejection reason; it is logged once and is useful in CI logs.
Check dtype, shape, layout, inference/training mode, external package
requirements, env flags, and unsupported options before calling backend code.
Do not silently copy or normalize inputs in a verifier; do that in the backend
implementation only when it is part of the backend contract.
If a backend supports only inference, check torch.is_grad_enabled() or
torch.is_inference_mode_enabled() as appropriate.
Do not mutate global backend registries, environment variables, tensors, RNG
state, or caches from a verifier.
Decorator placement
Decorate public operation entry points, not private helpers that are only used
inside one backend.
Keep the decorated function as the semantic fallback implementation. A user
should be able to set FLA_DISABLE_BACKEND_DISPATCH=1 and still get the same
API behavior.
Use the operation name that maps to the backend package. For normal ops,
@dispatch('kda') maps to fla.ops.kda.backends; special cases belong in
_OPERATION_BACKEND_MODULES.
Do not add import-time side effects in backend packages beyond registering
backends.
Testing guidance
Test the public decorated function, not only the backend helper.
Force dispatch off with FLA_DISABLE_BACKEND_DISPATCH=1 when comparing against
the Triton/default path.
Force or disable backend-specific env vars (FLA_FLASH_KDA, FLA_TILELANG,
FLA_INTRACARD_CP) when testing route behavior.
Include at least one rejection test for each verifier branch added or changed.
For backend changes under fla/ops/<op>/backends/, ensure dependent op tests
still run; scripts/find_dependent_tests.py maps backend changes back to the
decorated op files.
Style constraints
Use platform helpers from fla.utils for hardware/platform decisions instead
of adding new direct torch.cuda checks in public code or tests. If no helper
covers the condition, add a small helper in fla.utils first.
Keep backend imports lazy inside backend implementations when importing an
optional package would otherwise break environments without that package.
Keep error/rejection messages precise and user-facing; they appear in logs and
tests may assert them.