abs / neg

abs / neg#

Call parameters#

T.cuda.tile.apply(T.ptx[opcode], *operands, scope="thread", **parameters)

Operand order: dst, src.

Parameter

Type / default

Meaning

scope

static str; default “thread”

Execution scope; group axes follow Explicit CUDA tile patterns. It does not elect an issuing thread.

pred

runtime bool; default None

Execute the instruction when true; None is unconditional.

preserve_dst

static bool; default False

For a predicated register result, retain the initialized destination when pred is false; otherwise the inactive result is undefined.

Only the parameters listed here are instruction parameters. Opcode qualifiers are written in T.ptx[opcode].

Data representation#

Each operand uses the scalar PTX register type or its equal-width bit container. A packed f16x2/bf16x2 value occupies one b32 container; f32x2 occupies one b64 container. The final tensor axis counts native registers, not the packed scalar elements. Rounding, saturation, signedness and packing are selected in the opcode.

Operand specification#

abs and neg operate independently on local register values. Their opcode selects the numeric type and applicable qualifiers. Operand order is dst,src; each scalar tensor operand ends in (1,) after the group and repeat axes. An ordinary dtype view does not change the numeric type of the requested operation.

Legality conditions#

All tensor operands are local registers owned by the issuing thread. Their group/repeat shapes agree; the final extent equals that operand’s native register count (one for a scalar operand). Scalar expressions are allowed for single-register read inputs and broadcast across repeats. Destination slots must not overlap. Register storage strides are in container elements; they need not be one. The selected PTX form validates operand types.

Code generation#

For each group and row-major repeat coordinate, bind each tensor’s final register axis in increasing order and emit one selected instruction. A scalar input is reused. No packing, memory staging or synchronization is inserted. The number of expanded instructions per issuing thread is the product of repeat extents; downstream compiler optimizations may simplify unused or constant results.

Examples#

Example: an explicit two-instruction scalar expression#

from tvm.script import tirx as T


@T.function
def negative_magnitude(A: T.Tensor((4, 1), "float32"), Out: T.Tensor((4, 1), "float32")):
    T.device_entry(launch=T.cuda.LaunchConfig(grid=1, block=1))
    r = T.alloc_local((4, 1), "float32", layout="default")
    T.cuda.tile.apply(T.ptx["ld.global.f32"], r, A)
    T.cuda.tile.apply(T.ptx["abs.f32"], r, r)
    T.cuda.tile.apply(T.ptx["neg.f32"], r, r)
    T.cuda.tile.apply(T.ptx["st.global.f32"], Out, r)

For finite nonzero inputs this computes -abs(A). The tile expansion has four abs calls followed by four neg calls. This order comes from the two explicit operations, not from a hidden unary-op combination. Later compiler optimizations may simplify the expression. For a single caller-defined scalar expression with no exact-instruction requirement, use ewise: caller-defined scalar compositions.

NaN, signed-zero, and integer boundary behavior belongs to the selected PTX type/form, not to a generic Python abs reference. Destination storage may be gapped but must be distinct across repeats. Register ownership cannot change between source and destination. Predication follows mov.

Implementation: python/tvm/backend/cuda/tile/patterns/register.py. No temporary memory, numeric cast, barrier, or packed form is inserted.

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