/**
 * autograd.ts – Lightweight tape-based automatic differentiation engine.
 */
export declare class Tensor {
    data: GPUBuffer | null;
    shape: number[];
    numel: number;
    requiresGrad: boolean;
    grad: GPUBuffer | null;
    _gradFn: number | null;
    constructor(data: GPUBuffer | null, shape: number[], requiresGrad?: boolean);
    get byteSize(): number;
    zeroGrad(device: GPUDevice): void;
    destroy(): void;
}
export declare function enableGrad(): void;
export declare function noGrad(): void;
export declare function clearTape(): void;
export declare function recordOperation(backwardFn: () => void | Promise<void>): number;
export declare function backward(): Promise<void>;
export declare function ensureGradBuffer(device: GPUDevice, tensor: Tensor): void;
export declare function allocateGradients(device: GPUDevice, tensors: Tensor[]): void;
export declare function zeroGradients(device: GPUDevice, tensors: Tensor[]): void;
export declare function onesLikeScalar(device: GPUDevice): GPUBuffer;
export declare function crossEntropyLoss(logits: Float32Array, targetId: number): number;
export declare function crossEntropyGrad(logits: Float32Array, targetId: number): Float32Array;
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