multinomial_webgpu.ts

/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */

import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
import {computeDispatch, flatDispatchLayout} from './webgpu_util';

export class MultinomialProgram implements WebGPUProgram {
  variableNames: string[] = ['probs'];
  outputShape: number[] = [];
  shaderKey: string;
  dispatchLayout: {x: number[]};
  dispatch: [number, number, number];
  uniforms = 'seed : f32, numOutcomes: i32,';
  workgroupSize: [number, number, number] = [64, 1, 1];
  size = true;

  constructor(batchSize: number, numSamples: number) {
    this.outputShape = [batchSize, numSamples];
    this.dispatchLayout = flatDispatchLayout(this.outputShape);
    this.dispatch = computeDispatch(
        this.dispatchLayout, this.outputShape, this.workgroupSize);

    this.shaderKey = 'multinomial';
  }

  getUserCode(): string {
    const userCode = `
    //Based on the work of Dave Hoskins
    //https://www.shadertoy.com/view/4djSRW
    fn random (seed : f32, resultUV : vec2<f32>) -> f32 {
      let HASHSCALE1 = 443.8975;
      let p = resultUV * seed;
      var p3  = fract(vec3<f32>(p.xyx) * HASHSCALE1);
      p3 = p3 + dot(p3, p3.yzx + 19.19);
      return fract((p3.x + p3.y) * p3.z);
    }

    ${main('index')} {
      if (index < uniforms.size) {
        let coords = getOutputCoords();
        let batch = coords[0];

        let resUV = vec2<f32>(f32(coords[1]) / f32(uniforms.outShape[1]),
            f32(coords[0]) / f32(uniforms.outShape[0]));
        let r = random(uniforms.seed, resUV);
        var cdf = 0.0;
        for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {
          cdf = cdf + getProbs(batch, i);

          if (r < cdf) {
            setOutputAtIndexI32(index, i);
            return;
          }
        }

        // If no other event happened, last event happened.
        setOutputAtIndexI32(index, uniforms.numOutcomes - 1);
      }
    }
  `;
    return userCode;
  }
}