@tensorflow/tfjs-core/src/backends/cpu/utils/kernel_utils.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * 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.
 * =============================================================================
 */

// TODO(yassogba) Import utils directly until we can break the cyclic dependency
import * as backend_util from '../../../backends/backend_util';
// TODO(yassogba) export types from core.
import {DataType, NumericDataType, TypedArray} from '../../../types';
import * as util from '../../../util';

export function broadcastedBinaryOp(
    aShape: number[], bShape: number[], aVals: TypedArray, bVals: TypedArray,
    dtype: DataType,
    op: (a: number, b: number) => number): [TypedArray, number[]] {
  const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);

  const resultRank = newShape.length;
  const resultStrides = util.computeStrides(newShape);
  const resultSize = util.sizeFromShape(newShape);

  const result =
      util.getTypedArrayFromDType(dtype as NumericDataType, resultSize);

  const aRank = aShape.length;
  const bRank = bShape.length;

  const aStrides = util.computeStrides(aShape);
  const bStrides = util.computeStrides(bShape);

  const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);
  const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);

  if (aBroadcastDims.length + bBroadcastDims.length === 0) {
    for (let i = 0; i < result.length; ++i) {
      result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);
    }
  } else {
    for (let i = 0; i < result.length; ++i) {
      const loc = util.indexToLoc(i, resultRank, resultStrides);

      const aLoc = loc.slice(-aRank);
      aBroadcastDims.forEach(d => aLoc[d] = 0);
      const aIndex = util.locToIndex(aLoc, aRank, aStrides);

      const bLoc = loc.slice(-bRank);
      bBroadcastDims.forEach(d => bLoc[d] = 0);
      const bIndex = util.locToIndex(bLoc, bRank, bStrides);

      result[i] = op(aVals[aIndex], bVals[bIndex]);
    }
  }

  return [result, newShape];
}