min / max

min / max#

min and max apply the native instruction independently at every register repeat. They do not perform a warp, block, or tensor reduction. Choose NaN, sign, absolute-value, packed-type and other supported semantics through the instruction spelling.

Each scalar tensor operand ends in (1,). Binary and three-input forms are distinguished by the explicitly supplied operands, not by a layout search. max.f32 with dst,a,b,c uses the registered three-input form; dst,a,b uses its binary form. The selected target must support that form. Packed f16x2/bf16x2 variants use b32 carriers as in add / sub / mul.

Example: three-input maximum#

from tvm.script import tirx as T


@T.function
def maximum3(
    A: T.Tensor((4, 1), "float32"), B: T.Tensor((4, 1), "float32"), Out: T.Tensor((4, 1), "float32")
):
    T.device_entry(launch=T.cuda.LaunchConfig(grid=1, block=1))
    a = T.alloc_local((4, 1), "float32", layout="default")
    b = T.alloc_local((4, 1), "float32", layout="default")
    T.cuda.tile.apply(T.ptx["ld.global.f32"], a, A)
    T.cuda.tile.apply(T.ptx["ld.global.f32"], b, B)
    T.cuda.tile.apply(T.ptx["max.f32"], a, a, b, T.float32(0))
    T.cuda.tile.apply(T.ptx["st.global.f32"], Out, a)

The result for finite inputs is maximum(maximum(A,B),0). The tile expansion issues four three-input max instructions, not eight binary ones. NaN and signed-zero behavior is that of the selected PTX form and must not be inferred from this finite-input reference.

Changing max.f32 to min.f32 changes the operation on the same coordinate pattern. Reducing many values still requires explicit reduction loops, shuffle operations and/or shared communication around these atoms. In particular, a repeat axis of length four produces four independent results; it does not accumulate into a single output.

All inputs share group/repeat extents unless provided as scalar broadcasts. Output locations must be distinct across repeats. Predication follows mov. Implementation: python/tvm/backend/cuda/tile/patterns/register.py; native form definitions are in python/tvm/backend/cuda/ptx/table.py.

Contents