gx

chenyc

2025-06-12 7b72ac13a83764a662159d4a49b7fffb90476ecb

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/**
 * @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 { ENGINE } from '../engine';
import { dispose, tidy } from '../globals';
import { add } from '../ops/add';
import { div } from '../ops/div';
import { mul } from '../ops/mul';
import { sqrt } from '../ops/sqrt';
import { square } from '../ops/square';
import { sub } from '../ops/sub';
import { zerosLike } from '../ops/zeros_like';
import { Optimizer } from './optimizer';
/** @doclink Optimizer */
export class RMSPropOptimizer extends Optimizer {
    /** @nocollapse */
    static get className() {
        // Name matters for Python compatibility.
        // This is a getter instead of a property because when it's a property, it
        // prevents the entire class from being tree-shaken.
        return 'RMSProp';
    }
    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false) {
        super();
        this.learningRate = learningRate;
        this.decay = decay;
        this.momentum = momentum;
        this.epsilon = epsilon;
        this.accumulatedMeanSquares = [];
        this.accumulatedMoments = [];
        this.accumulatedMeanGrads = [];
        this.centered = centered;
        if (epsilon == null) {
            this.epsilon = ENGINE.backend.epsilon();
        }
        if (learningRate == null) {
            throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
        }
    }
    applyGradients(variableGradients) {
        const variableNames = Array.isArray(variableGradients) ?
            variableGradients.map(item => item.name) :
            Object.keys(variableGradients);
        variableNames.forEach((name, i) => {
            const value = ENGINE.registeredVariables[name];
            const trainable = false;
            if (this.accumulatedMeanSquares[i] == null) {
                this.accumulatedMeanSquares[i] = {
                    originalName: `${name}/rms`,
                    variable: tidy(() => zerosLike(value).variable(trainable))
                };
            }
            if (this.accumulatedMoments[i] == null) {
                this.accumulatedMoments[i] = {
                    originalName: `${name}/momentum`,
                    variable: tidy(() => zerosLike(value).variable(trainable))
                };
            }
            if (this.accumulatedMeanGrads[i] == null && this.centered) {
                this.accumulatedMeanGrads[i] = {
                    originalName: `${name}/mg`,
                    variable: tidy(() => zerosLike(value).variable(trainable))
                };
            }
            const gradient = Array.isArray(variableGradients) ?
                variableGradients[i].tensor :
                variableGradients[name];
            if (gradient == null) {
                return;
            }
            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
            const accumulatedMoments = this.accumulatedMoments[i].variable;
            tidy(() => {
                const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
                if (this.centered) {
                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
                    // Centered gradient
                    const newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
                    const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), this.epsilon))));
                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), gradContribution);
                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
                    accumulatedMoments.assign(newAccumulatedMoments);
                    const newValue = sub(value, newAccumulatedMoments);
                    value.assign(newValue);
                }
                else {
                    // Plain gradient
                    const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add(newAccumulatedMeanSquare, this.epsilon))));
                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
                    accumulatedMoments.assign(newAccumulatedMoments);
                    const newValue = sub(value, newAccumulatedMoments);
                    value.assign(newValue);
                }
            });
        });
        this.incrementIterations();
    }
    dispose() {
        if (this.accumulatedMeanSquares != null) {
            dispose(this.accumulatedMeanSquares.map(v => v.variable));
        }
        if (this.accumulatedMeanGrads != null && this.centered) {
            dispose(this.accumulatedMeanGrads.map(v => v.variable));
        }
        if (this.accumulatedMoments != null) {
            dispose(this.accumulatedMoments.map(v => v.variable));
        }
    }
    async getWeights() {
        // Order matters for Python compatibility.
        const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
        if (this.centered) {
            variables.push(...this.accumulatedMeanGrads);
        }
        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));
    }
    async setWeights(weightValues) {
        weightValues = await this.extractIterations(weightValues);
        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
        const trainable = false;
        this.accumulatedMeanSquares =
            weightValues.slice(0, variableCount).map(v => ({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
        this.accumulatedMoments =
            weightValues.slice(variableCount, variableCount * 2)
                .map(v => ({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
        if (this.centered) {
            this.accumulatedMeanGrads =
                weightValues.slice(variableCount * 2, variableCount * 3)
                    .map(v => ({
                    originalName: v.name,
                    variable: v.tensor.variable(trainable)
                }));
        }
    }
    getConfig() {
        return {
            'learningRate': this.learningRate,
            'decay': this.decay,
            'momentum': this.momentum,
            'epsilon': this.epsilon,
            'centered': this.centered
        };
    }
    /** @nocollapse */
    static fromConfig(cls, config) {
        return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);
    }
}
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* @license\n * Copyright 2018 Google LLC. 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 {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {sub} from '../ops/sub';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, Serializable, SerializableConstructor} from '../serialization';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n  /** @nocollapse */\n  static get className() {\n    // Name matters for Python compatibility.\n    // This is a getter instead of a property because when it's a property, it\n    // prevents the entire class from being tree-shaken.\n    return 'RMSProp';\n  }\n  private centered: boolean;\n\n  private accumulatedMeanSquares: OptimizerVariable[] = [];\n  private accumulatedMoments: OptimizerVariable[] = [];\n  private accumulatedMeanGrads: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected decay = 0.9,\n      protected momentum = 0.0, protected epsilon: number = null,\n      centered = false) {\n    super();\n\n    this.centered = centered;\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n    if (learningRate == null) {\n      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n    }\n  }\n\n  applyGradients(variableGradients: NamedTensorMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedMeanSquares[i] == null) {\n        this.accumulatedMeanSquares[i] = {\n          originalName: `${name}/rms`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMoments[i] == null) {\n        this.accumulatedMoments[i] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMeanGrads[i] == null && this.centered) {\n        this.accumulatedMeanGrads[i] = {\n          originalName: `${name}/mg`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n\n      const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n      const accumulatedMoments = this.accumulatedMoments[i].variable;\n      tidy(() => {\n        const newAccumulatedMeanSquare =\n            add(mul(accumulatedMeanSquare, this.decay),\n                mul(square(gradient), 1 - this.decay));\n\n        if (this.centered) {\n          const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n          // Centered gradient\n          const newAccumulatedMeanGrad =\n              add(mul(accumulatedMeanGrad, this.decay),\n                  mul(gradient, 1 - this.decay));\n\n          const gradContribution =\n              div(mul(gradient, this.learningRate),\n                  sqrt(\n                      sub(newAccumulatedMeanSquare,\n                          add(square(newAccumulatedMeanGrad), this.epsilon))));\n          const newAccumulatedMoments =\n              add(mul(accumulatedMoments, this.momentum), gradContribution);\n\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n          accumulatedMoments.assign(newAccumulatedMoments);\n\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        } else {\n          // Plain gradient\n          const newAccumulatedMeanSquare =\n              add(mul(accumulatedMeanSquare, this.decay),\n                  mul(square(gradient), 1 - this.decay));\n\n          const newAccumulatedMoments =\n              add(mul(accumulatedMoments, this.momentum),\n                  div(mul(gradient, this.learningRate),\n                      sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n          accumulatedMoments.assign(newAccumulatedMoments);\n\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        }\n      });\n    });\n    this.incrementIterations();\n  }\n\n  override dispose(): void {\n    if (this.accumulatedMeanSquares != null) {\n      dispose(this.accumulatedMeanSquares.map(v => v.variable));\n    }\n    if (this.accumulatedMeanGrads != null && this.centered) {\n      dispose(this.accumulatedMeanGrads.map(v => v.variable));\n    }\n    if (this.accumulatedMoments != null) {\n      dispose(this.accumulatedMoments.map(v => v.variable));\n    }\n  }\n\n  override async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    const variables: OptimizerVariable[] =\n        [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n    if (this.centered) {\n      variables.push(...this.accumulatedMeanGrads);\n    }\n    return [await this.saveIterations()].concat(\n        variables.map(v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  override async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount =\n        this.centered ? weightValues.length / 3 : weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedMeanSquares =\n        weightValues.slice(0, variableCount).map(v => ({\n                                                   originalName: v.name,\n                                                   variable: v.tensor.variable(\n                                                       trainable)\n                                                 }));\n    this.accumulatedMoments =\n        weightValues.slice(variableCount, variableCount * 2)\n            .map(v => ({\n                   originalName: v.name,\n                   variable: v.tensor.variable(trainable)\n                 }));\n    if (this.centered) {\n      this.accumulatedMeanGrads =\n          weightValues.slice(variableCount * 2, variableCount * 3)\n              .map(v => ({\n                     originalName: v.name,\n                     variable: v.tensor.variable(trainable)\n                   }));\n    }\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'decay': this.decay,\n      'momentum': this.momentum,\n      'epsilon': this.epsilon,\n      'centered': this.centered\n    };\n  }\n\n  /** @nocollapse */\n  static override fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config['learningRate'], config['decay'], config['momentum'],\n        config['epsilon'], config['centered']);\n  }\n}\n"]}