path: root/testing/web-platform/tests/interfaces/webnn.idl

// GENERATED CONTENT - DO NOT EDIT
// Content was automatically extracted by Reffy into webref
// (https://github.com/w3c/webref)
// Source: Web Neural Network API (https://webmachinelearning.github.io/webnn/)

interface mixin NavigatorML {
  [SecureContext, SameObject] readonly attribute ML ml;
};
Navigator includes NavigatorML;
WorkerNavigator includes NavigatorML;

enum MLDeviceType {
  "cpu",
  "gpu"
};

enum MLPowerPreference {
  "default",
  "high-performance",
  "low-power"
};

dictionary MLContextOptions {
  MLDeviceType deviceType = "cpu";
  MLPowerPreference powerPreference = "default";
};

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface ML {
  Promise<MLContext> createContext(optional MLContextOptions options = {});
  Promise<MLContext> createContext(GPUDevice gpuDevice);
};

typedef record<DOMString, ArrayBufferView> MLNamedArrayBufferViews;

dictionary MLComputeResult {
  MLNamedArrayBufferViews inputs;
  MLNamedArrayBufferViews outputs;
};

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface MLContext {
  Promise<MLComputeResult> compute(
      MLGraph graph, MLNamedArrayBufferViews inputs, MLNamedArrayBufferViews outputs);
};

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface MLGraph {};

enum MLInputOperandLayout {
  "nchw",
  "nhwc"
};

enum MLOperandDataType {
  "float32",
  "float16",
  "int32",
  "uint32",
  "int64",
  "uint64",
  "int8",
  "uint8"
};

dictionary MLOperandDescriptor {
  required MLOperandDataType dataType;
  sequence<[EnforceRange] unsigned long> dimensions = [];
};

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface MLOperand {
  MLOperandDataType dataType();
  sequence<unsigned long> shape();
};

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface MLActivation {};

typedef record<DOMString, MLOperand> MLNamedOperands;

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface MLGraphBuilder {
  // Construct the graph builder from the context.
  constructor(MLContext context);

  // Create an operand for a graph input.
  MLOperand input(DOMString name, MLOperandDescriptor descriptor);

  // Create an operand for a graph constant.
  MLOperand constant(MLOperandDescriptor descriptor, ArrayBufferView bufferView);

  // Create a single-value operand from the specified number of the specified type.
  MLOperand constant(double value, optional MLOperandDataType type = "float32");

  // Compile the graph up to the specified output operands asynchronously.
  Promise<MLGraph> build(MLNamedOperands outputs);
};

dictionary MLArgMinMaxOptions {
  sequence<[EnforceRange] unsigned long> axes;
  boolean keepDimensions = false;
  boolean selectLastIndex = false;
};

partial interface MLGraphBuilder {
  MLOperand argMin(MLOperand input, optional MLArgMinMaxOptions options = {});
  MLOperand argMax(MLOperand input, optional MLArgMinMaxOptions options = {});
};

dictionary MLBatchNormalizationOptions {
  MLOperand scale;
  MLOperand bias;
  [EnforceRange] unsigned long axis = 1;
  float epsilon = 1e-5;
  MLActivation activation;
};

partial interface MLGraphBuilder {
  MLOperand batchNormalization(MLOperand input, MLOperand mean, MLOperand variance,
                               optional MLBatchNormalizationOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand cast(MLOperand input, MLOperandDataType type);
};

dictionary MLClampOptions {
  float minValue;
  float maxValue;
};

partial interface MLGraphBuilder {
  MLOperand clamp(MLOperand input, optional MLClampOptions options = {});
  MLActivation clamp(optional MLClampOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand concat(sequence<MLOperand> inputs, [EnforceRange] unsigned long axis);
};

enum MLConv2dFilterOperandLayout {
  "oihw",
  "hwio",
  "ohwi",
  "ihwo"
};

dictionary MLConv2dOptions {
  sequence<[EnforceRange] unsigned long> padding;
  sequence<[EnforceRange] unsigned long> strides;
  sequence<[EnforceRange] unsigned long> dilations;
  [EnforceRange] unsigned long groups = 1;
  MLInputOperandLayout inputLayout = "nchw";
  MLConv2dFilterOperandLayout filterLayout = "oihw";
  MLOperand bias;
  MLActivation activation;
};

partial interface MLGraphBuilder {
  MLOperand conv2d(MLOperand input,
                   MLOperand filter,
                   optional MLConv2dOptions options = {});
};

enum MLConvTranspose2dFilterOperandLayout {
  "iohw",
  "hwoi",
  "ohwi"
};

dictionary MLConvTranspose2dOptions {
  sequence<[EnforceRange] unsigned long> padding;
  sequence<[EnforceRange] unsigned long> strides;
  sequence<[EnforceRange] unsigned long> dilations;
  sequence<[EnforceRange] unsigned long> outputPadding;
  sequence<[EnforceRange] unsigned long> outputSizes;
  [EnforceRange] unsigned long groups = 1;
  MLInputOperandLayout inputLayout = "nchw";
  MLConvTranspose2dFilterOperandLayout filterLayout = "iohw";
  MLOperand bias;
  MLActivation activation;
};

partial interface MLGraphBuilder {
  MLOperand convTranspose2d(MLOperand input, MLOperand filter,
                            optional MLConvTranspose2dOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand add(MLOperand a, MLOperand b);
  MLOperand sub(MLOperand a, MLOperand b);
  MLOperand mul(MLOperand a, MLOperand b);
  MLOperand div(MLOperand a, MLOperand b);
  MLOperand max(MLOperand a, MLOperand b);
  MLOperand min(MLOperand a, MLOperand b);
  MLOperand pow(MLOperand a, MLOperand b);
};

partial interface MLGraphBuilder {
  MLOperand equal(MLOperand a, MLOperand b);
  MLOperand greater(MLOperand a, MLOperand b);
  MLOperand greaterOrEqual(MLOperand a, MLOperand b);
  MLOperand lesser(MLOperand a, MLOperand b);
  MLOperand lesserOrEqual(MLOperand a, MLOperand b);
  MLOperand not(MLOperand a);
};

partial interface MLGraphBuilder {
  MLOperand abs(MLOperand input);
  MLOperand ceil(MLOperand input);
  MLOperand cos(MLOperand input);
  MLOperand erf(MLOperand input);
  MLOperand exp(MLOperand input);
  MLOperand floor(MLOperand input);
  MLOperand identity(MLOperand input);
  MLOperand log(MLOperand input);
  MLOperand neg(MLOperand input);
  MLOperand reciprocal(MLOperand input);
  MLOperand sin(MLOperand input);
  MLOperand sqrt(MLOperand input);
  MLOperand tan(MLOperand input);
};

dictionary MLEluOptions {
  float alpha = 1;
};

partial interface MLGraphBuilder {
  MLOperand elu(MLOperand input, optional MLEluOptions options = {});
  MLActivation elu(optional MLEluOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand expand(MLOperand input, sequence<[EnforceRange] unsigned long> newShape);
};

dictionary MLGatherOptions {
  [EnforceRange] unsigned long axis = 0;
};

partial interface MLGraphBuilder {
  MLOperand gather(MLOperand input,
                   MLOperand indices,
                   optional MLGatherOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand gelu(MLOperand input);
  MLActivation gelu();
};

dictionary MLGemmOptions {
  MLOperand c;
  float alpha = 1.0;
  float beta = 1.0;
  boolean aTranspose = false;
  boolean bTranspose = false;
};

partial interface MLGraphBuilder {
  MLOperand gemm(MLOperand a, MLOperand b, optional MLGemmOptions options = {});
};

enum MLGruWeightLayout {
  "zrn",  // update-reset-new gate ordering
  "rzn"   // reset-update-new gate ordering
};

enum MLRecurrentNetworkDirection {
  "forward",
  "backward",
  "both"
};

dictionary MLGruOptions {
  MLOperand bias;
  MLOperand recurrentBias;
  MLOperand initialHiddenState;
  boolean resetAfter = true;
  boolean returnSequence = false;
  MLRecurrentNetworkDirection direction = "forward";
  MLGruWeightLayout layout = "zrn";
  sequence<MLActivation> activations;
};

partial interface MLGraphBuilder {
  sequence<MLOperand> gru(MLOperand input,
                          MLOperand weight,
                          MLOperand recurrentWeight,
                          [EnforceRange] unsigned long steps,
                          [EnforceRange] unsigned long hiddenSize,
                          optional MLGruOptions options = {});
};

dictionary MLGruCellOptions {
  MLOperand bias;
  MLOperand recurrentBias;
  boolean resetAfter = true;
  MLGruWeightLayout layout = "zrn";
  sequence<MLActivation> activations;
};

partial interface MLGraphBuilder {
  MLOperand gruCell(MLOperand input,
                    MLOperand weight,
                    MLOperand recurrentWeight,
                    MLOperand hiddenState,
                    [EnforceRange] unsigned long hiddenSize,
                    optional MLGruCellOptions options = {});
};

dictionary MLHardSigmoidOptions {
  float alpha = 0.2;
  float beta = 0.5;
};

partial interface MLGraphBuilder {
  MLOperand hardSigmoid(MLOperand input, optional MLHardSigmoidOptions options = {});
  MLActivation hardSigmoid(optional MLHardSigmoidOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand hardSwish(MLOperand input);
  MLActivation hardSwish();
};

dictionary MLInstanceNormalizationOptions {
  MLOperand scale;
  MLOperand bias;
  float epsilon = 1e-5;
  MLInputOperandLayout layout = "nchw";
};

partial interface MLGraphBuilder {
  MLOperand instanceNormalization(MLOperand input,
                                  optional MLInstanceNormalizationOptions options = {});
};

dictionary MLLayerNormalizationOptions {
  MLOperand scale;
  MLOperand bias;
  sequence<[EnforceRange] unsigned long> axes;
  float epsilon = 1e-5;
};

partial interface MLGraphBuilder {
  MLOperand layerNormalization(MLOperand input,
                               optional MLLayerNormalizationOptions options = {});
};

dictionary MLLeakyReluOptions {
  float alpha = 0.01;
};

partial interface MLGraphBuilder {
  MLOperand leakyRelu(MLOperand input, optional MLLeakyReluOptions options = {});
  MLActivation leakyRelu(optional MLLeakyReluOptions options = {});
};

dictionary MLLinearOptions {
  float alpha = 1;
  float beta = 0;
};

partial interface MLGraphBuilder {
  MLOperand linear(MLOperand input, optional MLLinearOptions options = {});
  MLActivation linear(optional MLLinearOptions options = {});
};

enum MLLstmWeightLayout {
  "iofg", // input-output-forget-cell gate ordering
  "ifgo"  // input-forget-cell-output gate ordering
};

dictionary MLLstmOptions {
  MLOperand bias;
  MLOperand recurrentBias;
  MLOperand peepholeWeight;
  MLOperand initialHiddenState;
  MLOperand initialCellState;
  boolean returnSequence = false;
  MLRecurrentNetworkDirection direction = "forward";
  MLLstmWeightLayout layout = "iofg";
  sequence<MLActivation> activations;
};

partial interface MLGraphBuilder {
  sequence<MLOperand> lstm(MLOperand input,
                           MLOperand weight,
                           MLOperand recurrentWeight,
                           [EnforceRange] unsigned long steps,
                           [EnforceRange] unsigned long hiddenSize,
                           optional MLLstmOptions options = {});
};

dictionary MLLstmCellOptions {
  MLOperand bias;
  MLOperand recurrentBias;
  MLOperand peepholeWeight;
  MLLstmWeightLayout layout = "iofg";
  sequence<MLActivation> activations;
};

partial interface MLGraphBuilder {
  sequence<MLOperand> lstmCell(MLOperand input,
                               MLOperand weight,
                               MLOperand recurrentWeight,
                               MLOperand hiddenState,
                               MLOperand cellState,
                               [EnforceRange] unsigned long hiddenSize,
                               optional MLLstmCellOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand matmul(MLOperand a, MLOperand b);
};

enum MLPaddingMode {
  "constant",
  "edge",
  "reflection",
  "symmetric"
};

dictionary MLPadOptions {
  MLPaddingMode mode = "constant";
  float value = 0;
};

partial interface MLGraphBuilder {
  MLOperand pad(MLOperand input,
                sequence<[EnforceRange] unsigned long> beginningPadding,
                sequence<[EnforceRange] unsigned long> endingPadding,
                optional MLPadOptions options = {});
};

enum MLRoundingType {
  "floor",
  "ceil"
};

dictionary MLPool2dOptions {
  sequence<[EnforceRange] unsigned long> windowDimensions;
  sequence<[EnforceRange] unsigned long> padding;
  sequence<[EnforceRange] unsigned long> strides;
  sequence<[EnforceRange] unsigned long> dilations;
  MLInputOperandLayout layout = "nchw";
  MLRoundingType roundingType = "floor";
  sequence<[EnforceRange] unsigned long> outputSizes;
};

partial interface MLGraphBuilder {
  MLOperand averagePool2d(MLOperand input, optional MLPool2dOptions options = {});
  MLOperand l2Pool2d(MLOperand input, optional MLPool2dOptions options = {});
  MLOperand maxPool2d(MLOperand input, optional MLPool2dOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand prelu(MLOperand input, MLOperand slope);
};

dictionary MLReduceOptions {
  sequence<[EnforceRange] unsigned long> axes;
  boolean keepDimensions = false;
};

partial interface MLGraphBuilder {
  MLOperand reduceL1(MLOperand input, optional MLReduceOptions options = {});
  MLOperand reduceL2(MLOperand input, optional MLReduceOptions options = {});
  MLOperand reduceLogSum(MLOperand input, optional MLReduceOptions options = {});
  MLOperand reduceLogSumExp(MLOperand input, optional MLReduceOptions options = {});
  MLOperand reduceMax(MLOperand input, optional MLReduceOptions options = {});
  MLOperand reduceMean(MLOperand input, optional MLReduceOptions options = {});
  MLOperand reduceMin(MLOperand input, optional MLReduceOptions options = {});
  MLOperand reduceProduct(MLOperand input, optional MLReduceOptions options = {});
  MLOperand reduceSum(MLOperand input, optional MLReduceOptions options = {});
  MLOperand reduceSumSquare(MLOperand input, optional MLReduceOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand relu(MLOperand input);
  MLActivation relu();
};

enum MLInterpolationMode {
  "nearest-neighbor",
  "linear"
};

dictionary MLResample2dOptions {
  MLInterpolationMode mode = "nearest-neighbor";
  sequence<float> scales;
  sequence<[EnforceRange] unsigned long> sizes;
  sequence<[EnforceRange] unsigned long> axes;
};

partial interface MLGraphBuilder {
  MLOperand resample2d(MLOperand input, optional MLResample2dOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand reshape(MLOperand input, sequence<[EnforceRange] unsigned long> newShape);
};

partial interface MLGraphBuilder {
  MLOperand sigmoid(MLOperand input);
  MLActivation sigmoid();
};

partial interface MLGraphBuilder {
  MLOperand slice(MLOperand input,
                  sequence<[EnforceRange] unsigned long> starts,
                  sequence<[EnforceRange] unsigned long> sizes);
};

partial interface MLGraphBuilder {
  MLOperand softmax(MLOperand input, unsigned long axis);
  MLActivation softmax(unsigned long axis);
};

partial interface MLGraphBuilder {
  MLOperand softplus(MLOperand input);
  MLActivation softplus();
};

partial interface MLGraphBuilder {
  MLOperand softsign(MLOperand input);
  MLActivation softsign();
};

dictionary MLSplitOptions {
  [EnforceRange] unsigned long axis = 0;
};

partial interface MLGraphBuilder {
  sequence<MLOperand> split(
      MLOperand input,
      ([EnforceRange] unsigned long or sequence<[EnforceRange] unsigned long>) splits,
      optional MLSplitOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand tanh(MLOperand input);
  MLActivation tanh();
};

dictionary MLTransposeOptions {
  sequence<[EnforceRange] unsigned long> permutation;
};

partial interface MLGraphBuilder {
  MLOperand transpose(MLOperand input, optional MLTransposeOptions options = {});
};

dictionary MLTriangularOptions {
  boolean upper = true;
  [EnforceRange] long diagonal = 0;
};

partial interface MLGraphBuilder {
  MLOperand triangular(MLOperand input, optional MLTriangularOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand where(MLOperand condition, MLOperand input, MLOperand other);
};