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Mojo function

fp4_gemv_kernel

def fp4_gemv_kernel[c_type: DType, c_layout: TensorLayout, a_layout: TensorLayout, p_layout: TensorLayout, s_layout: TensorLayout, elementwise_lambda_fn: Optional[def[dtype: DType, width: SIMDLength, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing thin -> None]](c: TileTensor[c_type, c_layout, MutAnyOrigin], a: TileTensor[DType.bfloat16, a_layout, ImmutAnyOrigin], packed: TileTensor[DType.uint8, p_layout, ImmutAnyOrigin], scales: TileTensor[DType.float8_e4m3fn, s_layout, ImmutAnyOrigin], n: Int, k: Int)

One warp per output column; 32 lanes stride down K decoding FP4 -> fp32.

c is [1, N], a the bf16 activation [1, K], packed the FP4 weight [N, K//2] (lo-nibble first), scales the FP8-E4M3 block scales [N, ceil(K/16)]. Accumulation is fp32.

Parameters:

c_type (DType): Output element type (fp16, bf16, fp32). Accumulation is fp32.
c_layout (TensorLayout): TileTensor layout of the output c.
a_layout (TensorLayout): TileTensor layout of the activation a.
p_layout (TensorLayout): TileTensor layout of the packed FP4 weight.
s_layout (TensorLayout): TileTensor layout of the FP8 block scales.
elementwise_lambda_fn (Optional[def[dtype: DType, width: SIMDLength, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing thin -> None]): Optional fused epilogue applied on the width-1 store.

Args:

c (TileTensor[c_type, c_layout, MutAnyOrigin]): Output tile tensor [1, N] receiving the GEMV result.
a (TileTensor[DType.bfloat16, a_layout, ImmutAnyOrigin]): Bf16 activation tile tensor [1, K], the single activation row.
packed (TileTensor[DType.uint8, p_layout, ImmutAnyOrigin]): FP4-packed weight tile tensor [N, K//2] (lo-nibble first).
scales (TileTensor[DType.float8_e4m3fn, s_layout, ImmutAnyOrigin]): FP8-E4M3 block scales tile tensor [N, ceil(K/16)].
n (Int): Number of output columns (rows of the transposed weight).
k (Int): Inner dimension length; must be a multiple of NVFP4_SF_VECTOR_SIZE (16).