Struct caffe2op_segmentreduction::AbstractLengthsOp

pub struct AbstractLengthsOp<TData, TLengths, Context, R: Reducer, const SparseFused: bool, InputAccessor> { /* private fields */ }

| @brief Segment reduction op with optional fused | embedding lookup | | Base implementation for LengthsXXX and | SparseLengthsXXX depending on SparseFused static | argument. | | Inputs: | 0: DATA - input embedding to do lookups in | 1..P: AUX_ARG_ - optional additional arguments to be passed to the | reducer, should have the same first dimension as | LENGTHS (e.g. scalars in WeightedSum) | # if SparseFused == true: | P+1: INDICES - 1-D vector with indices to look up in DATA. Should have the | same dimension as LENGTHS | # P+1 if SparseFused == false: | P+1 or P+2: LENGTHS - lengths on indecies vector | | Output: | Tensor with first dimension of K, where K = len(LENGTHS). Rest | of dimensions are decided by reducer but usually are the same size as extra | dimensions of DATA | | bool SparseFused = true, | class InputAccessor = BaseInputAccessor> | | TODO(dzhulgakov): for now it’s implemented with | incremental reducers because of fused sparse | support. But using “lengths” representation | actually implies continuous segments and thus | range reducers can be used for non-sparse | version.

Implementations

impl<TData, TLengths, Context, R: Reducer, const SparseFused: bool, InputAccessor> AbstractLengthsOp<TData, TLengths, Context, R, SparseFused, InputAccessor>

pub fn run_on_device(&mut self) -> bool

pub fn do_run_with_type<IndexType>(&mut self) -> bool

pub fn do_run_with_value<IndexType, const FixedSize: i32>(&mut self) -> bool