triton.language.dot_scaled

triton.language.dot_scaled(lhs, lhs_scale, lhs_format, rhs, rhs_scale, rhs_format, acc=None, fast_math=False, out_dtype=triton.language.float32)

Returns the matrix product of two blocks in microscaling format.

lhs and rhs use microscaling formats described here: https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf

Software emulation enables targeting hardware architectures without native microscaling operation support. Right now for such case, microscaled lhs/rhs are upcasted to bf16 element type beforehand for dot computation, with one exception: for AMD CDNA3 specifically, if one of the inputs is of fp16 element type, the other input is also upcasted to fp16 element type instead. This behavior is experimental and may be subject to change in the future.

Parameters:

  • lhs (2D tensor representing fp4, fp8 or bf16 elements. Fp4 elements are packed into uint8 inputs with the first element in lower bits. Fp8 are stored as uint8 or the corresponding fp8 type.) – The first tensor to be multiplied.

  • lhs_scale (e8m0 type represented as an uint8 tensor.) – Scale factor for lhs tensor.

  • lhs_format (str) – format of the lhs tensor. Available formats: {e2m1, e4m3, e5m2, bf16, fp16}.

  • rhs (2D tensor representing fp4, fp8 or bf16 elements. Fp4 elements are packed into uint8 inputs with the first element in lower bits. Fp8 are stored as uint8 or the corresponding fp8 type.) – The second tensor to be multiplied.

  • rhs_scale (e8m0 type represented as an uint8 tensor.) – Scale factor for rhs tensor.

  • rhs_format (str) – format of the rhs tensor. Available formats: {e2m1, e4m3, e5m2, bf16, fp16}.

  • acc – The accumulator tensor. If not None, the result is added to this tensor.