ex2 / lg2

These PTX instructions calculate base-two exponential and logarithm. They are not natural exp/log. Select the explicit approximation and subnormal policy, for example ex2.approx.ftz.f32 or lg2.approx.ftz.f32. The tile layer forwards this spelling without selecting a replacement.

Both tensor operands use local registers and end in (1,); each preceding repeat selects another scalar input/output. Output repeats cannot alias. A scalar read-only input broadcasts over all repeats.

Example: four base-two exponentials

from tvm.script import tirx as T


@T.function
def exp2_native(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["ex2.approx.ftz.f32"], r, r)
    T.cuda.tile.apply(T.ptx["st.global.f32"], Out, r)

Inputs [-1,0,1,2] give the power-of-two reference [0.5,1,2,4]. The arithmetic expansion contains four ex2 instructions. For general inputs, test with tolerances appropriate to the approximate opcode; .ftz is part of the chosen handling of subnormal values.

Use lg2.approx.ftz.f32 with the same operand pattern for base-two logarithms. If the operation is natural exp, SiLU, or an exp/scale/bias combination, write that scalar expression through ewise: caller-defined scalar compositions. The scalar compiler then owns its expansion; the call is not documented as one ex2.

Implementation: python/tvm/backend/cuda/tile/patterns/register.py.