Struct UniqueConsecutivePlan 

pub struct UniqueConsecutivePlan<T: Element> { /* private fields */ }

unique_consecutive plan.

Emits one cell per run-start in each row (PyTorch torch.unique_consecutive). Input is expected to be pre-sorted, or the caller wants only consecutive-equal runs collapsed.

When to use: dedup runs after a sort, or unique-detection on already-sorted data. For full unique-of-row use UniquePlan, which composes sort + this. Set-valued — no BW.

Dtypes: {f32, f64, i32}.

Shape limits: [batch, row_len] input; [batch, max_unique] outputs; [batch] counter. max_unique == row_len is the worst-case-safe bound.

Workspace: none.

Precision guarantee: non-deterministic ordering — slot assignment uses a per-row atomic counter (block-race order). The set of detected uniques is data-determined; only the row ordering varies. Callers needing input-order output should sort the result on [batch, counter] rows afterward.

Implementations

impl<T: Element> UniqueConsecutivePlan<T>

pub fn select( _stream: &Stream, desc: &UniqueConsecutiveDescriptor, _pref: PlanPreference, ) -> Result<Self>

Pick a kernel for desc.

pub fn can_implement(&self, args: &UniqueConsecutiveArgs<'_, T>) -> Result<()>

Validate args.

pub fn workspace_size(&self) -> usize

Workspace size in bytes.

pub fn sku(&self) -> KernelSku

Identity of the kernel this plan picked.

pub fn precision_guarantee(&self) -> PrecisionGuarantee

Numerical guarantees for this plan’s kernel.

pub fn run( &self, stream: &Stream, _workspace: Workspace<'_>, args: UniqueConsecutiveArgs<'_, T>, ) -> Result<()>

Launch.