@tensorflow/tfjs-node/src/int64_tensors.ts

This repository provides native TensorFlow execution in backend JavaScript applications under the Node.js runtime, accelerated by the TensorFlow C binary under the hood. It provides the same API as [TensorFlow.js](https://js.tensorflow.org/api/latest/).

/**
 * @license
 * Copyright 2018 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 {Shape, util} from '@tensorflow/tfjs';
import {endianness} from 'os';

const INT32_MAX = 2147483648;

/**
 * Node.js-specific tensor type: int64-type scalar.
 *
 * This class is created for a specific purpose: to support
 * writing `step`s to TensorBoard via op-kernel bindings.
 * `step` is required to have an int64 dtype, but TensorFlow.js
 * (tfjs-core) doesn't have a built-in int64 dtype. This is
 * related to a lack of `Int64Array` or `Uint64Array` typed
 * array in basic JavaScript.
 *
 * This class is introduced as a workaround.
 */
export class Int64Scalar {
  readonly dtype: string = 'int64';
  readonly rank: number = 1;
  private valueArray_: Int32Array;

  private static endiannessOkay_: boolean;

  constructor(readonly value: number) {
    // The reason why we need to check endianness of the machine here is
    // negative int64 values and the way in which we represent them
    // using Int32Arrays in JavaScript. We represent each int64 value with
    // two consecutive elements of an Int32Array. For positive values,
    // the high part is simply zero; for negative values, the high part
    // should be -1. The ordering of the low and high parts assumes
    // little endian (i.e., least significant digits appear first).
    // This assumption is checked by the lines below.
    if (Int64Scalar.endiannessOkay_ == null) {
      if (endianness() !== 'LE') {
        throw new Error(
            `Int64Scalar does not support endianness of this machine: ` +
            `${endianness()}`);
      }
      Int64Scalar.endiannessOkay_ = true;
    }

    util.assert(
        value > -INT32_MAX && value < INT32_MAX - 1,
        () =>
            `Got a value outside of the bound of values supported for int64 ` +
            `dtype ([-${INT32_MAX}, ${INT32_MAX - 1}]): ${value}`);
    util.assert(
        Number.isInteger(value),
        () => `Expected value to be an integer, but got ${value}`);

    // We use two int32 elements to represent a int64 value. This assumes
    // little endian, which is checked above.
    const highPart = value >= 0 ? 0 : -1;
    const lowPart = value % INT32_MAX;
    this.valueArray_ = new Int32Array([lowPart, highPart]);
  }

  get shape(): Shape {
    return [];
  }

  /** Get the Int32Array that represents the int64 value. */
  get valueArray(): Int32Array {
    return this.valueArray_;
  }
}

/**
 * This method encodes a Int32Array as Int64 layout in order to create TF_INT64
 * tensor through binding.
 */
export function encodeInt32ArrayAsInt64(value: Int32Array): Int32Array {
  if (endianness() !== 'LE') {
    throw new Error(
        `Int64Scalar does not support endianness of this machine: ` +
        `${endianness()}`);
  }

  const buffer = new Int32Array(value.length * 2);
  for (let i = 0; i < value.length; i++) {
    buffer[i * 2] = value[i];
  }
  return buffer;
}