onnxruntime-web/lib/wasm/jsep/webnn/webnn.d.ts

A Javascript library for running ONNX models on browsers

// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

/* eslint-disable @typescript-eslint/naming-convention */

interface NavigatorML {
  readonly ml: ML;
}
interface Navigator extends NavigatorML {}
interface WorkerNavigator extends NavigatorML {}
type MLDeviceType = 'cpu'|'gpu'|'npu';
type MLPowerPreference = 'default'|'high-performance'|'low-power';
interface MLContextOptions {
  deviceType?: MLDeviceType;
  powerPreference?: MLPowerPreference;
}
interface ML {
  createContext(options?: MLContextOptions): Promise<MLContext>;
  createContext(gpuDevice: GPUDevice): Promise<MLContext>;
}
type MLNamedArrayBufferViews = Record<string, ArrayBufferView>;
interface MLComputeResult {
  inputs?: MLNamedArrayBufferViews;
  outputs?: MLNamedArrayBufferViews;
}
interface MLContext {
  compute(graph: MLGraph, inputs: MLNamedArrayBufferViews, outputs: MLNamedArrayBufferViews): Promise<MLComputeResult>;
}
interface MLGraph {}
type MLInputOperandLayout = 'nchw'|'nhwc';
type MLOperandDataType = 'float32'|'float16'|'int32'|'uint32'|'int64'|'uint64'|'int8'|'uint8'|'int4'|'uint4';
interface MLOperandDescriptor {
  dataType: MLOperandDataType;
  shape?: readonly number[];
  /** @deprecated Use shape instead of dimensions */
  dimensions?: readonly number[];
}
interface MLOperand {
  dataType: MLOperandDataType;
  shape: readonly number[];
}
interface MLActivation {}
type MLNamedOperands = Record<string, MLOperand>;
interface MLGraphBuilder {
  // eslint-disable-next-line @typescript-eslint/no-misused-new
  new(context: MLContext): MLGraphBuilder;
  input(name: string, descriptor: MLOperandDescriptor): MLOperand;
  constant(descriptor: MLOperandDescriptor, bufferView: ArrayBufferView): MLOperand;
  constant(type: MLOperandDataType, value: number): MLOperand;
  build(outputs: MLNamedOperands): Promise<MLGraph>;
}
interface MLArgMinMaxOptions {
  axes?: number[];
  keepDimensions?: boolean;
  selectLastIndex?: boolean;
}
interface MLGraphBuilder {
  argMin(input: MLOperand, options?: MLArgMinMaxOptions): MLOperand;
  argMax(input: MLOperand, options?: MLArgMinMaxOptions): MLOperand;
}
interface MLBatchNormalizationOptions {
  scale?: MLOperand;
  bias?: MLOperand;
  axis?: number;
  epsilon?: number;
}
interface MLGraphBuilder {
  batchNormalization(input: MLOperand, mean: MLOperand, variance: MLOperand, options?: MLBatchNormalizationOptions):
      MLOperand;
}
interface MLGraphBuilder {
  cast(input: MLOperand, type: MLOperandDataType): MLOperand;
}
interface MLClampOptions {
  minValue?: number;
  maxValue?: number;
}
interface MLGraphBuilder {
  clamp(input: MLOperand, options?: MLClampOptions): MLOperand;
  clamp(options?: MLClampOptions): MLActivation;
}
interface MLGraphBuilder {
  concat(inputs: MLOperand[], axis: number): MLOperand;
}
type MLConv2dFilterOperandLayout = 'oihw'|'hwio'|'ohwi'|'ihwo';
interface MLConv2dOptions {
  padding?: number[];
  strides?: number[];
  dilations?: number[];
  groups?: number;
  inputLayout?: MLInputOperandLayout;
  filterLayout?: MLConv2dFilterOperandLayout;
  bias?: MLOperand;
}
interface MLGraphBuilder {
  conv2d(input: MLOperand, filter: MLOperand, options?: MLConv2dOptions): MLOperand;
}
type MLConvTranspose2dFilterOperandLayout = 'iohw'|'hwoi'|'ohwi';
interface MLConvTranspose2dOptions {
  padding?: number[];
  strides?: number[];
  dilations?: number[];
  outputPadding?: number[];
  outputSizes?: number[];
  groups?: number;
  inputLayout?: MLInputOperandLayout;
  filterLayout?: MLConvTranspose2dFilterOperandLayout;
  bias?: MLOperand;
}
interface MLGraphBuilder {
  convTranspose2d(input: MLOperand, filter: MLOperand, options?: MLConvTranspose2dOptions): MLOperand;
}
interface MLGraphBuilder {
  add(a: MLOperand, b: MLOperand): MLOperand;
  sub(a: MLOperand, b: MLOperand): MLOperand;
  mul(a: MLOperand, b: MLOperand): MLOperand;
  div(a: MLOperand, b: MLOperand): MLOperand;
  max(a: MLOperand, b: MLOperand): MLOperand;
  min(a: MLOperand, b: MLOperand): MLOperand;
  pow(a: MLOperand, b: MLOperand): MLOperand;
}
interface MLGraphBuilder {
  equal(a: MLOperand, b: MLOperand): MLOperand;
  greater(a: MLOperand, b: MLOperand): MLOperand;
  greaterOrEqual(a: MLOperand, b: MLOperand): MLOperand;
  lesser(a: MLOperand, b: MLOperand): MLOperand;
  lesserOrEqual(a: MLOperand, b: MLOperand): MLOperand;
  logicalNot(a: MLOperand): MLOperand;
}
interface MLGraphBuilder {
  abs(input: MLOperand): MLOperand;
  ceil(input: MLOperand): MLOperand;
  cos(input: MLOperand): MLOperand;
  erf(input: MLOperand): MLOperand;
  exp(input: MLOperand): MLOperand;
  floor(input: MLOperand): MLOperand;
  identity(input: MLOperand): MLOperand;
  log(input: MLOperand): MLOperand;
  neg(input: MLOperand): MLOperand;
  reciprocal(input: MLOperand): MLOperand;
  sin(input: MLOperand): MLOperand;
  sqrt(input: MLOperand): MLOperand;
  tan(input: MLOperand): MLOperand;
}
interface MLEluOptions {
  alpha?: number;
}
interface MLGraphBuilder {
  elu(input: MLOperand, options?: MLEluOptions): MLOperand;
  elu(options?: MLEluOptions): MLActivation;
}
interface MLGraphBuilder {
  expand(input: MLOperand, newShape: number[]): MLOperand;
}
interface MLGatherOptions {
  axis?: number;
}
interface MLGraphBuilder {
  gather(input: MLOperand, indices: MLOperand, options?: MLGatherOptions): MLOperand;
}
interface MLGraphBuilder {
  gelu(input: MLOperand): MLOperand;
  gelu(): MLActivation;
}
interface MLGemmOptions {
  c?: MLOperand;
  alpha?: number;
  beta?: number;
  aTranspose?: boolean;
  bTranspose?: boolean;
}
interface MLGraphBuilder {
  gemm(a: MLOperand, b: MLOperand, options?: MLGemmOptions): MLOperand;
}
type MLGruWeightLayout = 'zrn'|'rzn';
type MLRecurrentNetworkDirection = 'forward'|'backward'|'both';
interface MLGruOptions {
  bias?: MLOperand;
  recurrentBias?: MLOperand;
  initialHiddenState?: MLOperand;
  resetAfter?: boolean;
  returnSequence?: boolean;
  direction?: MLRecurrentNetworkDirection;
  layout?: MLGruWeightLayout;
  activations?: MLActivation[];
}
interface MLGraphBuilder {
  gru(input: MLOperand, weight: MLOperand, recurrentWeight: MLOperand, steps: number, hiddenSize: number,
      options?: MLGruOptions): MLOperand[];
}
interface MLGruCellOptions {
  bias?: MLOperand;
  recurrentBias?: MLOperand;
  resetAfter?: boolean;
  layout?: MLGruWeightLayout;
  activations?: MLActivation[];
}
interface MLGraphBuilder {
  gruCell(
      input: MLOperand, weight: MLOperand, recurrentWeight: MLOperand, hiddenState: MLOperand, hiddenSize: number,
      options?: MLGruCellOptions): MLOperand;
}
interface MLHardSigmoidOptions {
  alpha?: number;
  beta?: number;
}
interface MLGraphBuilder {
  hardSigmoid(input: MLOperand, options?: MLHardSigmoidOptions): MLOperand;
  hardSigmoid(options?: MLHardSigmoidOptions): MLActivation;
}
interface MLGraphBuilder {
  hardSwish(input: MLOperand): MLOperand;
  hardSwish(): MLActivation;
}
interface MLInstanceNormalizationOptions {
  scale?: MLOperand;
  bias?: MLOperand;
  epsilon?: number;
  layout?: MLInputOperandLayout;
}
interface MLGraphBuilder {
  instanceNormalization(input: MLOperand, options?: MLInstanceNormalizationOptions): MLOperand;
}
interface MLLayerNormalizationOptions {
  scale?: MLOperand;
  bias?: MLOperand;
  axes?: number[];
  epsilon?: number;
}
interface MLGraphBuilder {
  layerNormalization(input: MLOperand, options?: MLLayerNormalizationOptions): MLOperand;
}
interface MLLeakyReluOptions {
  alpha?: number;
}
interface MLGraphBuilder {
  leakyRelu(input: MLOperand, options?: MLLeakyReluOptions): MLOperand;
  leakyRelu(options?: MLLeakyReluOptions): MLActivation;
}
interface MLLinearOptions {
  alpha?: number;
  beta?: number;
}
interface MLGraphBuilder {
  linear(input: MLOperand, options?: MLLinearOptions): MLOperand;
  linear(options?: MLLinearOptions): MLActivation;
}
type MLLstmWeightLayout = 'iofg'|'ifgo';
interface MLLstmOptions {
  bias?: MLOperand;
  recurrentBias?: MLOperand;
  peepholeWeight?: MLOperand;
  initialHiddenState?: MLOperand;
  initialCellState?: MLOperand;
  returnSequence?: boolean;
  direction?: MLRecurrentNetworkDirection;
  layout?: MLLstmWeightLayout;
  activations?: MLActivation[];
}
interface MLGraphBuilder {
  lstm(
      input: MLOperand, weight: MLOperand, recurrentWeight: MLOperand, steps: number, hiddenSize: number,
      options?: MLLstmOptions): MLOperand[];
}
interface MLLstmCellOptions {
  bias?: MLOperand;
  recurrentBias?: MLOperand;
  peepholeWeight?: MLOperand;
  layout?: MLLstmWeightLayout;
  activations?: MLActivation[];
}
interface MLGraphBuilder {
  lstmCell(
      input: MLOperand, weight: MLOperand, recurrentWeight: MLOperand, hiddenState: MLOperand, cellState: MLOperand,
      hiddenSize: number, options?: MLLstmCellOptions): MLOperand[];
}
interface MLGraphBuilder {
  matmul(a: MLOperand, b: MLOperand): MLOperand;
}
type MLPaddingMode = 'constant'|'edge'|'reflection'|'symmetric';
interface MLPadOptions {
  mode?: MLPaddingMode;
  value?: number;
}
interface MLGraphBuilder {
  pad(input: MLOperand, beginningPadding: number[], endingPadding: number[], options?: MLPadOptions): MLOperand;
}
type MLRoundingType = 'floor'|'ceil';
interface MLPool2dOptions {
  windowDimensions?: number[];
  padding?: number[];
  strides?: number[];
  dilations?: number[];
  layout?: MLInputOperandLayout;
  roundingType?: MLRoundingType;
  outputSizes?: number[];
}
interface MLGraphBuilder {
  averagePool2d(input: MLOperand, options?: MLPool2dOptions): MLOperand;
  l2Pool2d(input: MLOperand, options?: MLPool2dOptions): MLOperand;
  maxPool2d(input: MLOperand, options?: MLPool2dOptions): MLOperand;
}
interface MLGraphBuilder {
  prelu(input: MLOperand, slope: MLOperand): MLOperand;
}
interface MLReduceOptions {
  axes?: number[];
  keepDimensions?: boolean;
}
interface MLGraphBuilder {
  reduceL1(input: MLOperand, options?: MLReduceOptions): MLOperand;
  reduceL2(input: MLOperand, options?: MLReduceOptions): MLOperand;
  reduceLogSum(input: MLOperand, options?: MLReduceOptions): MLOperand;
  reduceLogSumExp(input: MLOperand, options?: MLReduceOptions): MLOperand;
  reduceMax(input: MLOperand, options?: MLReduceOptions): MLOperand;
  reduceMean(input: MLOperand, options?: MLReduceOptions): MLOperand;
  reduceMin(input: MLOperand, options?: MLReduceOptions): MLOperand;
  reduceProduct(input: MLOperand, options?: MLReduceOptions): MLOperand;
  reduceSum(input: MLOperand, options?: MLReduceOptions): MLOperand;
  reduceSumSquare(input: MLOperand, options?: MLReduceOptions): MLOperand;
}
interface MLGraphBuilder {
  relu(input: MLOperand): MLOperand;
  relu(): MLActivation;
}
type MLInterpolationMode = 'nearest-neighbor'|'linear';
interface MLResample2dOptions {
  mode?: MLInterpolationMode;
  scales?: number[];
  sizes?: number[];
  axes?: number[];
}
interface MLGraphBuilder {
  resample2d(input: MLOperand, options?: MLResample2dOptions): MLOperand;
}
interface MLGraphBuilder {
  reshape(input: MLOperand, newShape: number[]): MLOperand;
}
interface MLGraphBuilder {
  sigmoid(input: MLOperand): MLOperand;
  sigmoid(): MLActivation;
}
interface MLGraphBuilder {
  slice(input: MLOperand, starts: number[], sizes: number[]): MLOperand;
}
interface MLGraphBuilder {
  softmax(input: MLOperand, axis: number): MLOperand;
  softmax(axis: number): MLActivation;
}
interface MLGraphBuilder {
  softplus(input: MLOperand): MLOperand;
  softplus(): MLActivation;
}
interface MLGraphBuilder {
  softsign(input: MLOperand): MLOperand;
  softsign(): MLActivation;
}
interface MLSplitOptions {
  axis?: number;
}
interface MLGraphBuilder {
  split(input: MLOperand, splits: number|number[], options?: MLSplitOptions): MLOperand[];
}
interface MLGraphBuilder {
  tanh(input: MLOperand): MLOperand;
  tanh(): MLActivation;
}
interface MLTransposeOptions {
  permutation?: number[];
}
interface MLGraphBuilder {
  transpose(input: MLOperand, options?: MLTransposeOptions): MLOperand;
}
interface MLTriangularOptions {
  upper?: boolean;
  diagonal?: number;
}
interface MLGraphBuilder {
  triangular(input: MLOperand, options?: MLTriangularOptions): MLOperand;
}
interface MLGraphBuilder {
  where(condition: MLOperand, input: MLOperand, other: MLOperand): MLOperand;
}

// Experimental MLTensor interface

interface MLTensor {
  destroy(): void;
}

type MLNamedTensor = Record<string, MLTensor>;

type MLTensorUsageFlags = number;

// TODO(@Honry): Remove this once it is deprecated in Chromium.
declare const MLTensorUsage: {
  readonly WEBGPU_INTEROP: MLTensorUsageFlags;
  readonly READ: MLTensorUsageFlags;
  readonly WRITE: MLTensorUsageFlags;
};

interface MLTensorDescriptor extends MLOperandDescriptor {
  /** @deprecated Use readable/writeable instead of usage */
  usage: MLTensorUsageFlags | undefined;
  importableToWebGPU?: boolean;
  readable?: boolean;
  writable?: boolean;
}

interface MLContext {
  createTensor(descriptor: MLTensorDescriptor): Promise<MLTensor>;
  writeTensor(
      destinationTensor: MLTensor, sourceData: ArrayBufferView|ArrayBuffer, sourceElementOffset?: number,
      sourceElementSize?: number): void;
  readTensor(sourceTensor: MLTensor): Promise<ArrayBuffer>;
  readTensor(sourceTensor: MLTensor, destinationData: ArrayBufferView|ArrayBuffer): Promise<undefined>;
  dispatch(graph: MLGraph, inputs: MLNamedTensor, outputs: MLNamedTensor): void;
  opSupportLimits() : MLOpSupportLimits;
}

interface MLOpSupportLimits {
  input: MLSupportLimits;
  output: MLSupportLimits;
}

interface MLSupportLimits {
  dataTypes: MLOperandDataType[];
}