Mojo function

rms_norm_fused_fp8

rms_norm_fused_fp8[in_dtype: DType, out_dtype: DType, scales_dtype: DType, rank: Int, input_fn: def[width: Int, rank: Int](IndexList[rank]) capturing -> SIMD[in_dtype, width], /, target: StringSlice[StaticConstantOrigin] = "gpu", compile_only: Bool = False](shape: IndexList[rank], output: TileTensor[out_dtype, output.LayoutType, output.origin, address_space=output.address_space, linear_idx_type=output.linear_idx_type, element_size=output.element_size], gamma: TileTensor[in_dtype, gamma.LayoutType, gamma.origin, address_space=gamma.address_space, linear_idx_type=gamma.linear_idx_type, element_size=gamma.element_size], epsilon: Scalar[in_dtype], weight_offset: Scalar[in_dtype], ctx: DeviceContextPtr, scale_ub: Float32, scale_output: TileTensor[scales_dtype, scale_output.LayoutType, scale_output.origin, address_space=scale_output.address_space, linear_idx_type=scale_output.linear_idx_type, element_size=scale_output.element_size])

Fused RMSNorm + FP8 quantization kernel (TileTensor overload).

Computes RMSNorm normalization and quantizes the output to FP8 format in a single pass. This is the primary implementation that operates on TileTensor inputs.

Parameters:

in_dtype (DType): Input data type (float32, float16, or bfloat16).
out_dtype (DType): Output FP8 data type (float8_e4m3fn or float8_e4m3fnuz).
scales_dtype (DType): Data type for scale factors (bfloat16, float16, or float32).
rank (Int): Tensor rank.
input_fn (def[width: Int, rank: Int](IndexList[rank]) capturing -> SIMD[in_dtype, width]): Function to load input values.
target (StringSlice): Target device ("gpu" or "cpu").
compile_only (Bool): If True, only compiles the kernel without executing it. Used to pre-compile kernels and avoid JIT compilation deadlocks in multi-GPU contexts.

Args:

shape (IndexList): Input tensor shape.
output (TileTensor): Output TileTensor to write FP8 quantized values.
gamma (TileTensor): RMSNorm scale parameter (rank 1).
epsilon (Scalar): Small constant for numerical stability.
weight_offset (Scalar): Offset to add after normalization.
ctx (DeviceContextPtr): Device context.
scale_ub (Float32): Upper bound for dynamic scale factor to limit the scale value.
scale_output (TileTensor): TileTensor to write per-row dynamic scales.