onnxruntime-web/lib/wasm/jsep/webgpu/ops/gather-elements.ts

A Javascript library for running ONNX models on browsers

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

import { DataType } from '../../../wasm-common';
import { TensorView } from '../../tensor-view';
import { ShapeUtil } from '../../util';
import { AttributeWithCacheKey, createAttributeWithCacheKey } from '../attribute-with-cache-key';
import { ComputeContext, ProgramInfo, ProgramInputTensorInfoDependency, ProgramUniform } from '../types';

import { createTensorShapeVariables, inputVariable, outputVariable, ShaderHelper } from './common';

export interface GatherElementsAttributes extends AttributeWithCacheKey {
  axis: number;
}

const validateInputs = (inputs: readonly TensorView[]): void => {
  if (!inputs || inputs.length !== 2) {
    throw new Error('GatherElements requires 2 inputs.');
  }

  if (inputs[0].dims.length < 1) {
    throw new Error('GatherElements requires that the data input be rank >= 1.');
  }

  if (inputs[0].dims.length !== inputs[1].dims.length) {
    throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`);
  }
};

const createGatherElementsProgramInfo = (
  inputs: readonly TensorView[],
  attributes: GatherElementsAttributes,
): ProgramInfo => {
  const inputShape = inputs[0].dims;
  const inputOutputDataType = inputs[0].dataType;
  const inputRank = inputShape.length;

  const indicesShape = inputs[1].dims;
  const indicesDataType = inputs[1].dataType;
  const axis = ShapeUtil.normalizeAxis(attributes.axis, inputRank);
  const axisDimLimit = inputShape[axis];

  const outputShape = indicesShape.slice(0);
  const outputSize = ShapeUtil.size(outputShape);

  const input = inputVariable('input', inputOutputDataType, inputRank);
  const indices = inputVariable('indicesInput', indicesDataType, indicesShape.length);
  const output = outputVariable('output', inputOutputDataType, outputShape.length);

  const programUniforms: ProgramUniform[] = [
    { type: DataType.uint32, data: outputSize },
    { type: DataType.int32, data: axisDimLimit },
    { type: DataType.uint32, data: axis },
  ];
  programUniforms.push(...createTensorShapeVariables(inputShape, indicesShape, outputShape));
  const inputDependencies: ProgramInputTensorInfoDependency[] = ['rank', 'rank'];

  // int64 indices would be treated as little endian i32 with assumption they fall in i32 limits
  // That assumption is safe as it's not possible to allocate >2gb buffer for input tensor
  // Input data will be treated as u32 or two u32 for 8-byte tensors
  const getShaderSource = (shaderHelper: ShaderHelper) => `
      ${shaderHelper
        .registerUniform('outputSize', 'u32')
        .registerUniform('axisDimLimit', 'i32')
        .registerUniform('axis', 'u32')
        .declareVariables(input, indices, output)}
      ${shaderHelper.mainStart()}
      ${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes('uniforms.outputSize')}

      let outputIndices = ${output.offsetToIndices('global_idx')};

      var idx = ${indices.getByOffset('global_idx')};
      if (idx < 0) {
        idx = idx + uniforms.axisDimLimit;
      }
      var inputIndices = ${input.type.indices}(outputIndices);
      ${input.indicesSet('inputIndices', 'uniforms.axis', 'u32(idx)')};
      let value = ${input.getByIndices('inputIndices')};

      ${output.setByOffset('global_idx', 'value')};
  }`;

  return {
    name: 'GatherElements',
    shaderCache: { inputDependencies },
    getRunData: () => ({
      outputs: [{ dims: outputShape, dataType: inputs[0].dataType }],
      dispatchGroup: { x: Math.ceil(outputSize / 64 /* workgroup size */) },
      programUniforms,
    }),
    getShaderSource,
  };
};

export const parseGatherElementsAttributes = (attributes: Record<string, unknown>): GatherElementsAttributes =>
  createAttributeWithCacheKey({ axis: attributes.axis as number });

export const gatherElements = (context: ComputeContext, attributes: GatherElementsAttributes): void => {
  const inputs = context.inputs;
  validateInputs(inputs);
  context.compute(createGatherElementsProgramInfo(context.inputs, attributes));
};