/**
 * Normalization operations
 */
import type { WebInferContext } from '../core/context.ts';
import type { Tensor } from '../core/tensor.ts';
/**
 * Root Mean Square Normalization
 *
 * Computes: output = (input / sqrt(mean(input^2) + eps)) * weight
 *
 * @param ctx WebInfer context
 * @param input Input tensor of shape [..., hidden_size]
 * @param weight Weight tensor of shape [hidden_size]
 * @param eps Small constant for numerical stability (default: 1e-5)
 * @returns Normalized tensor of same shape as input
 */
export declare function rmsnorm(ctx: WebInferContext, input: Tensor, weight: Tensor, eps?: number): Promise<Tensor>;
/**
 * Fused Add + RMSNorm
 *
 * Computes: output = rmsnorm(input + residual)
 * Returns both the normalized output and the new residual (input + residual)
 *
 * @param ctx WebInfer context
 * @param input Input tensor
 * @param residual Residual tensor to add
 * @param weight Weight tensor
 * @param eps Small constant for numerical stability (default: 1e-5)
 * @returns [output, new_residual] tuple
 */
export declare function fused_add_rmsnorm(ctx: WebInferContext, input: Tensor, residual: Tensor, weight: Tensor, eps?: number): Promise<[Tensor, Tensor]>;
/**
 * Gemma RMSNorm variant (adds 1.0 to weight)
 *
 * Computes: output = (input / sqrt(mean(input^2) + eps)) * (weight + 1.0)
 *
 * @param ctx WebInfer context
 * @param input Input tensor
 * @param weight Weight tensor
 * @param eps Small constant for numerical stability (default: 1e-5)
 * @returns Normalized tensor
 */
export declare function gemma_rmsnorm(ctx: WebInferContext, input: Tensor, weight: Tensor, eps?: number): Promise<Tensor>;