@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.
 * =============================================================================
 */

import * as backend_util from '../../../backends/backend_util';
import {BinaryInputs} from '../../../kernel_names';
import {KernelConfig} from '../../../kernel_registry';
import {DataType, NumericDataType, TypedArray} from '../../../types';
import * as util from '../../../util';
import {MathBackendCPU} from '../backend_cpu';
import {assertNotComplex} from '../cpu_util';

export function createBinaryKernelConfig(
    name: string,
    op: (
        aShape: number[], bShape: number[], aVals: TypedArray,
        bVals: TypedArray,
        dtype: DataType) => [TypedArray, number[]]): KernelConfig {
  return {
    kernelName: name,
    backendName: 'cpu',
    kernelFunc: ({inputs, backend}) => {
      const {a, b} = inputs as BinaryInputs;
      const cpuBackend = backend as MathBackendCPU;
      assertNotComplex([a, b], name);

      const aVals = cpuBackend.data.get(a.dataId).values as TypedArray;
      const bVals = cpuBackend.data.get(b.dataId).values as TypedArray;

      const [resultData, resultShape] =
          op(a.shape, b.shape, aVals, bVals, a.dtype);

      const dataId = cpuBackend.write(resultData, resultShape, a.dtype);
      return {dataId, shape: resultShape, dtype: a.dtype};
    }
  };
}

export function createBinaryKernelImpl(op: (a: number, b: number) => number) {
  return (aShape: number[], bShape: number[], aVals: TypedArray,
          bVals: TypedArray, dtype: DataType): [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];
  };
}