lrn_grad_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 LRNGradProgram implements WebGPUProgram {
  outputShape: number[] = [];
  shaderKey: string;
  dispatchLayout: {x: number[]};
  dispatch: [number, number, number];
  variableNames = ['inputImage', 'outputImage', 'dy'];
  uniforms = 'depthRadius : i32, bias : f32, alpha : f32, beta : f32,';
  workgroupSize: [number, number, number] = [64, 1, 1];
  size = true;

  constructor(inputShape: number[]) {
    this.outputShape = inputShape;
    this.dispatchLayout = flatDispatchLayout(this.outputShape);
    this.dispatch = computeDispatch(
        this.dispatchLayout, this.outputShape, this.workgroupSize);
    this.shaderKey = 'lrn_grad';
  }

  getUserCode(): string {
    const userCode = `
    ${main('index')} {
      if (index < uniforms.size) {
        let coords = getOutputCoords();
        let b = coords[0];
        let r = coords[1];
        let c = coords[2];

        let MIN_DEPTH_BEGIN = 0;
        let MAX_DEPTH_END = uniforms.outShape[3];
        var result = 0.0;
        for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {
          let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);
          let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);

          var norm = 0.0;
          for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {
            if (k < depthBegin) {
              continue;
            } else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            } else {
              break;
            }
          }

          norm = uniforms.alpha * norm + uniforms.bias;

          for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {
            if (k < depthBegin) {
              continue;
            } else if (k >= depthBegin && k < depthEnd) {
              var dyi = -2.0 * uniforms.alpha * uniforms.beta
                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * uniforms.beta);
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            } else {
              break;
            }
          }
        }

        setOutputAtIndex(index, result);
      }
    }
  `;
    return userCode;
  }
}