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fhelium.backend.implementation ​

Index operation implementations contributed by backend components.

Implementation classes declare their operation classes and stable name where the implementation is defined. This registry stores those declarations, rejects duplicate identities, and resolves caller selections without importing or discovering implementation modules implicitly.

FusionImplementation ​

class View source

python
FusionImplementation()

Bases: OperationImplementation, Protocol

Describe region support supplied by one joint-execution implementation.

match_fusion ​

method

python
def match_fusion(operations: Sequence[Operation]) -> int | None: ...

Return supported computational-op count, zero for plumbing, or None.

The count identifies which operations contribute to a fusion candidate. Unknown facts remain unresolved until execution binding. Known unsupported operations or facts return None.

ImplementationRegistry ​

class View source

python
ImplementationRegistry(implementations: Sequence[ImplementationT]=(), *, category: str)

Store named implementations by operation class and identity.

The registry has no module-discovery or import-time mutation behavior. Backend components construct implementation objects beside their kernels, and an assembly function passes those objects to this registry.

operation_types ​

property

python
operation_types: tuple[type[Operation], ...]

Return operation classes in first declaration order.

implementations ​

property

python
implementations: tuple[ImplementationT, ...]

Return implementation contributions in construction order.

available ​

method

python
def available(operation_type: type[Operation]) -> tuple[str, ...]: ...

Return implementation names in declaration order.

supports ​

method

python
def supports(operation_type: type[Operation], *, name: str | None=None) -> bool: ...

Return whether at least one matching implementation is registered.

resolve ​

method

python
def resolve(operation_type: type[Operation], *, requested: str | None) -> ImplementationT: ...

Resolve one named implementation, refusing ambiguous defaults.

OperationImplementation ​

class View source

python
OperationImplementation()

Bases: Protocol

Execute one or more registered operations on Tensor payloads.

name ​

property

python
name: str

operation_types ​

property

python
operation_types: tuple[type[Operation], ...]

supports_in_place ​

property

python
supports_in_place: bool

resource_requirements ​

method

python
def resource_requirements(invocation: OperationInvocation) -> tuple[ResourceRequirement, ...]: ...

execute ​

method

python
def execute(invocation: OperationInvocation, inputs: tuple[torch.Tensor, ...], resources: tuple[BoundResource, ...], /, *, in_place: bool) -> tuple[torch.Tensor, ...]: ...

OperationImplementationRegistry ​

class View source

python
OperationImplementationRegistry(implementations: Sequence[OperationImplementation]=(), *, operation_specs: OperationSpecRegistry=DEFAULT_OPERATION_SPECS)

Resolve implementations by operation class and identity.

operation_types ​

property

python
operation_types: tuple[type[Operation], ...]

implementations ​

property

python
implementations: tuple[OperationImplementation, ...]

Return implementation contributions in construction order.

with_implementations ​

method

python
def with_implementations(implementations: Sequence[OperationImplementation]) -> OperationImplementationRegistry: ...

Return this registry extended by caller-supplied contributions.

available ​

method

python
def available(operation_type: type[Operation]) -> tuple[str, ...]: ...

supports ​

method

python
def supports(operation: Operation, *, implementation: str | None=None) -> bool: ...

resolve ​

method

python
def resolve(operation: Operation, *, requested: str | None, in_place: bool=False) -> OperationImplementation: ...

resolve_type ​

method

python
def resolve_type(operation_type: type[Operation], *, requested: str | None, in_place: bool=False) -> OperationImplementation: ...

effect ​

method

python
def effect(operation_type: type[Operation]) -> OperationEffect: ...

Return the registered effect of one operation class.

OperationInvocation ​

class View source

python
OperationInvocation(operation_type: type[Operation], operand_count: int, result_count: int, attributes: Mapping[str, object] = field(default_factory=dict), operand_prime_ids: tuple[tuple[int, ...] | None, ...] = (), operand_bases: tuple[str | None, ...] = (), operand_components: tuple[int | None, ...] = (), result_prime_ids: tuple[tuple[int, ...] | None, ...] = ())

Describe a backend operation call without an SSA graph.

Attributes ​

NameTypeDefault/value
operation_typetype[Operation]
operand_countint
result_countint
attributesMapping[str, object]field(default_factory=dict)
operand_prime_idstuple[tuple[int, ...] | None, ...]()
operand_basestuple[str | None, ...]()
operand_componentstuple[int | None, ...]()
result_prime_idstuple[tuple[int, ...] | None, ...]()

PreparingOperationImplementation ​

class View source

python
PreparingOperationImplementation()

Bases: OperationImplementation, Protocol

Prepare one represented operation for direct Tensor execution.

Code-generating implementations consume the operation and its regions once during Backend resolution. The returned implementation executes the complete operation without interpreting its nested operations at runtime.

prepare_operation ​

method

python
def prepare_operation(operation: Operation) -> OperationImplementation: ...

RegionCallable ​

data View source

python
RegionCallable = Callable[[tuple[torch.Tensor, ...]], tuple[torch.Tensor, ...]]

RegionOperationImplementation ​

class View source

python
RegionOperationImplementation()

Bases: OperationImplementation, Protocol

Execute an operation whose regions contain callable Tensor programs.

execute_regions ​

method

python
def execute_regions(invocation: OperationInvocation, inputs: tuple[torch.Tensor, ...], resources: tuple[BoundResource, ...], regions: tuple[RegionCallable, ...], /, *, in_place: bool) -> tuple[torch.Tensor, ...]: ...

operation_invocation ​

function View source

python
def operation_invocation(operation: Operation) -> OperationInvocation: ...

Convert one linked IR operation to its runtime invocation descriptor.

requested_implementation ​

function View source

python
def requested_implementation(operation: Operation, caller_selection: str | None=None) -> str | None: ...

Return the implementation recorded in IR or selected by the caller.

An assignment written by a Compile pass is a hard constraint. A caller may select an unassigned operation, but it may not contradict the Program.