wehub-resource-sync
123 lines
3.9 KiB
C++
123 lines
3.9 KiB
C++
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "paddle/phi/core/device_context.h"
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#include "paddle/phi/core/enforce.h"
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namespace phi {
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namespace funcs {
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template <typename T, typename Context>
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void MultinomialFunctor(const Context& dev_ctx,
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int64_t* out_data,
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const T* in_data,
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const int64_t num_samples,
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const bool replacement,
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const int64_t num_categories,
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const int64_t num_distributions) {
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std::vector<T> cumulative_probs(num_categories);
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std::uniform_real_distribution<T> dist(0, 1);
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auto engine = dev_ctx.GetHostGenerator()->GetCPUEngine();
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for (int64_t i = 0; i < num_distributions; i++) {
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T probs_sum = 0;
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T prob_value;
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int64_t num_zeros = 0;
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for (int64_t j = 0; j < num_categories; j++) {
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prob_value = in_data[i * num_categories + j];
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PADDLE_ENFORCE_GE(
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prob_value,
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0.0,
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errors::InvalidArgument("The input of multinomial distribution "
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"should be >= 0, but got %f.",
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prob_value));
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probs_sum += prob_value;
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if (prob_value == 0) {
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num_zeros += 1;
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}
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cumulative_probs[j] = probs_sum;
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}
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PADDLE_ENFORCE_GT(
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probs_sum,
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0.0,
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errors::InvalidArgument("The sum of one multinomial distribution "
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"probability should be > 0, but got %f.",
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probs_sum));
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PADDLE_ENFORCE_EQ(
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(replacement || (num_categories - num_zeros >= num_samples)),
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true,
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errors::InvalidArgument("When replacement is False, number of "
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"samples should be less than non-zero "
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"categories."));
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for (int64_t j = 0; j < num_categories; j++) {
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cumulative_probs[j] /= probs_sum;
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}
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for (int64_t s = 0; s < num_samples; s++) {
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T uniform_rand = dist(*engine);
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// use binary search to get the selected category sample id.
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// let cumulative_probs[id-1] < uniform_rand < cumulative_probs[id].
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int64_t left = 0;
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int64_t right = num_categories;
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int64_t mid;
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int64_t sample_id;
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T temp_prob;
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cumulative_probs[(num_categories - 1)] = 1;
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while (right > left) {
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mid = left + (right - left) / 2;
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temp_prob = cumulative_probs[mid];
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if (temp_prob < uniform_rand) {
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left = mid + 1;
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} else {
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right = mid;
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}
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}
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sample_id = left;
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out_data[i * num_samples + s] = sample_id;
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// if replacement is false, the selected category should be removed.
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if (!replacement && s < num_samples - 1) {
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T sample_prob;
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T new_prob = 0;
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T new_sum;
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if (sample_id != 0) {
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new_prob = cumulative_probs[sample_id - 1];
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}
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sample_prob = cumulative_probs[sample_id] - new_prob;
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new_sum = 1.0 - sample_prob;
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for (int64_t j = 0; j < num_categories; j++) {
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new_prob = cumulative_probs[j];
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if (j >= sample_id) {
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new_prob -= sample_prob;
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}
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new_prob /= new_sum;
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cumulative_probs[j] = new_prob;
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}
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}
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}
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}
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}
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} // namespace funcs
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} // namespace phi
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