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chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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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 { Multinomial } from '@tensorflow/tfjs-core';
import { MultinomialProgram } from '../multinomial_gpu';
import { softmax } from './Softmax';
export function multinomial(args) {
    const { inputs, backend, attrs } = args;
    const { logits } = inputs;
    const { numSamples, seed, normalized } = attrs;
    const probs = normalized ?
        logits :
        softmax({ inputs: { logits }, backend, attrs: { dim: logits.shape.length - 1 } });
    const batchSize = probs.shape[0];
    const numOutcomes = probs.shape[1];
    const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);
    const customValues = [[seed]];
    const res = backend.runWebGLProgram(program, [probs], 'int32', customValues);
    if (!normalized) {
        backend.disposeIntermediateTensorInfo(probs);
    }
    return res;
}
export const multinomialConfig = {
    kernelName: Multinomial,
    backendName: 'webgl',
    kernelFunc: multinomial
};
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