/**
 * @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 { FusedBatchNorm, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
export function batchNorm(args) {
    const { inputs, backend, attrs } = args;
    const { x, scale, offset, mean, variance } = inputs;
    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +
        'equal ranks.');
    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +
        'equal ranks.');
    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +
        'equal ranks.');
    assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');
    let { varianceEpsilon } = attrs;
    if (varianceEpsilon == null) {
        varianceEpsilon = 0.001;
    }
    const xVals = backend.data.get(x.dataId).values;
    const mVals = backend.data.get(mean.dataId).values;
    const varVals = backend.data.get(variance.dataId).values;
    const sVals = scale ? backend.data.get(scale.dataId).values :
        new Float32Array([1]);
    const offVals = offset ?
        backend.data.get(offset.dataId).values :
        new Float32Array([0]);
    const outVals = new Float32Array(xVals.length);
    const offValsLength = offVals.length;
    const sValsLength = sVals.length;
    const varValsLength = varVals.length;
    const mValsLength = mVals.length;
    let offi = 0;
    let mi = 0;
    let si = 0;
    let vi = 0;
    for (let i = 0; i < xVals.length; ++i) {
        outVals[i] = offVals[offi++] +
            (xVals[i] - mVals[mi++]) * sVals[si++] /
                Math.sqrt(varVals[vi++] + varianceEpsilon);
        if (offi >= offValsLength) {
            offi = 0;
        }
        if (mi >= mValsLength) {
            mi = 0;
        }
        if (si >= sValsLength) {
            si = 0;
        }
        if (vi >= varValsLength) {
            vi = 0;
        }
    }
    return backend.makeTensorInfo(x.shape, x.dtype, outVals);
}
export const batchNormConfig = {
    kernelName: FusedBatchNorm,
    backendName: 'cpu',
    kernelFunc: batchNorm,
};
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function batchNorm(args: {\n  inputs: FusedBatchNormInputs,\n  backend: MathBackendCPU,\n  attrs: FusedBatchNormAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x, scale, offset, mean, variance} = inputs;\n\n  util.assert(\n      mean.shape.length === variance.shape.length,\n      () => 'Batch normalization gradient requires mean and variance to have ' +\n          'equal ranks.');\n  util.assert(\n      offset == null || mean.shape.length === offset.shape.length,\n      () => 'Batch normalization gradient requires mean and offset to have ' +\n          'equal ranks.');\n  util.assert(\n      scale == null || mean.shape.length === scale.shape.length,\n      () => 'Batch normalization gradient requires mean and scale to have ' +\n          'equal ranks.');\n\n  assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n\n  let {varianceEpsilon} = attrs;\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 0.001;\n  }\n\n  const xVals = backend.data.get(x.dataId).values as TypedArray;\n  const mVals = backend.data.get(mean.dataId).values as TypedArray;\n  const varVals = backend.data.get(variance.dataId).values as TypedArray;\n  const sVals = scale ? backend.data.get(scale.dataId).values as TypedArray :\n                        new Float32Array([1]);\n  const offVals = offset ?\n      backend.data.get(offset.dataId).values as TypedArray :\n      new Float32Array([0]);\n  const outVals = new Float32Array(xVals.length);\n\n  const offValsLength = offVals.length;\n  const sValsLength = sVals.length;\n  const varValsLength = varVals.length;\n  const mValsLength = mVals.length;\n\n  let offi = 0;\n  let mi = 0;\n  let si = 0;\n  let vi = 0;\n  for (let i = 0; i < xVals.length; ++i) {\n    outVals[i] = offVals[offi++] +\n        (xVals[i] - mVals[mi++]) * sVals[si++] /\n            Math.sqrt(varVals[vi++] + varianceEpsilon);\n    if (offi >= offValsLength) {\n      offi = 0;\n    }\n    if (mi >= mValsLength) {\n      mi = 0;\n    }\n    if (si >= sValsLength) {\n      si = 0;\n    }\n    if (vi >= varValsLength) {\n      vi = 0;\n    }\n  }\n  return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\n\nexport const batchNormConfig: KernelConfig = {\n  kernelName: FusedBatchNorm,\n  backendName: 'cpu',\n  kernelFunc: batchNorm as unknown as KernelFunc,\n};\n"]}