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2025-06-12 7b72ac13a83764a662159d4a49b7fffb90476ecb

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/**
 * @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 } from '../kernel_names';
import { add } from '../ops/add';
import { getReductionAxes } from '../ops/broadcast_util';
import { mul } from '../ops/mul';
import { reshape } from '../ops/reshape';
import { rsqrt } from '../ops/rsqrt';
import { scalar } from '../ops/scalar';
import { sub } from '../ops/sub';
import { sum } from '../ops/sum';
import { tile } from '../ops/tile';
export const fusedBatchNormGradConfig = {
    kernelName: FusedBatchNorm,
    inputsToSave: ['x', 'mean', 'variance', 'scale'],
    gradFunc: (dy, saved, attrs) => {
        const { varianceEpsilon } = attrs;
        const [x, mean, variance, scale] = saved;
        const scaleValue = scale == null ? scalar(1) : scale;
        const reductionAxes = getReductionAxes(mean.shape, x.shape);
        const tileShape = [];
        if (mean.rank === 1) {
            for (let i = 0; i < x.shape.length - 1; ++i) {
                tileShape.push(x.shape[i]);
            }
            tileShape.push(1);
        }
        const xMinusMean = sub(x, mean);
        const dyTimesScaleValue = mul(dy, scaleValue);
        const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));
        const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));
        const derX = () => {
            if (mean.rank === 1) {
                return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);
            }
            else {
                return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
            }
        };
        const derMean = () => {
            let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
            if (mean.rank === 1) {
                meanDer = sum(meanDer, reductionAxes);
            }
            return reshape(meanDer, mean.shape);
        };
        const derVariance = () => {
            let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
            if (mean.rank === 1) {
                varianceDer = sum(varianceDer, reductionAxes);
            }
            return reshape(varianceDer, mean.shape);
        };
        const derScale = () => {
            const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
            let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
            if (mean.rank === 1) {
                scaleDer = sum(scaleDer, reductionAxes);
            }
            return reshape(scaleDer, mean.shape);
        };
        const derOffset = () => {
            let offsetDer = dy;
            if (mean.rank === 1) {
                offsetDer = sum(offsetDer, reductionAxes);
            }
            return reshape(offsetDer, mean.shape);
        };
        return {
            x: derX,
            mean: derMean,
            variance: derVariance,
            scale: derScale,
            offset: derOffset
        };
    }
};
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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 */\nimport {FusedBatchNorm, FusedBatchNormAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {add} from '../ops/add';\nimport {getReductionAxes} from '../ops/broadcast_util';\nimport {mul} from '../ops/mul';\nimport {reshape} from '../ops/reshape';\nimport {rsqrt} from '../ops/rsqrt';\nimport {scalar} from '../ops/scalar';\nimport {sub} from '../ops/sub';\nimport {sum} from '../ops/sum';\nimport {tile} from '../ops/tile';\nimport {Tensor} from '../tensor';\nimport {Rank, ShapeMap} from '../types';\n\nexport const fusedBatchNormGradConfig: GradConfig = {\n  kernelName: FusedBatchNorm,\n  inputsToSave: ['x', 'mean', 'variance', 'scale'],\n  gradFunc: <R extends Rank>(\n      dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const {varianceEpsilon} = attrs as unknown as FusedBatchNormAttrs;\n    const [x, mean, variance, scale] = saved;\n\n    const scaleValue = scale == null ? scalar(1) : scale;\n    const reductionAxes = getReductionAxes(mean.shape, x.shape);\n    const tileShape: number[] = [];\n    if (mean.rank === 1) {\n      for (let i = 0; i < x.shape.length - 1; ++i) {\n        tileShape.push(x.shape[i]);\n      }\n      tileShape.push(1);\n    }\n\n    const xMinusMean = sub(x, mean);\n    const dyTimesScaleValue = mul(dy, scaleValue);\n    const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n    const minusHalfRCube = mul(\n        mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance),\n        scalar(-0.5));\n\n    const derX = () => {\n      if (mean.rank === 1) {\n        return reshape(\n            mul(mul(dy,\n                    tile(\n                        reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]),\n                        tileShape)),\n                scaleValue),\n            x.shape);\n      } else {\n        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n      }\n    };\n    const derMean = () => {\n      let meanDer =\n          mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n      if (mean.rank === 1) {\n        meanDer = sum(meanDer, reductionAxes);\n      }\n      return reshape(meanDer, mean.shape as ShapeMap[R]);\n    };\n    const derVariance = () => {\n      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n\n      if (mean.rank === 1) {\n        varianceDer = sum(varianceDer, reductionAxes);\n      }\n      return reshape(varianceDer, mean.shape as ShapeMap[R]);\n    };\n    const derScale = () => {\n      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n\n      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n      if (mean.rank === 1) {\n        scaleDer = sum(scaleDer, reductionAxes);\n      }\n      return reshape(scaleDer, mean.shape as ShapeMap[R]);\n    };\n    const derOffset = () => {\n      let offsetDer = dy;\n      if (mean.rank === 1) {\n        offsetDer = sum(offsetDer, reductionAxes);\n      }\n      return reshape(offsetDer, mean.shape as ShapeMap[R]);\n    };\n\n    return {\n      x: derX,\n      mean: derMean,\n      variance: derVariance,\n      scale: derScale,\n      offset: derOffset\n    };\n  }\n};\n"]}