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Extract CPU sampling routines. (#8697)
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| /** | ||
| * Copyright 2020-2023 by XGBoost Contributors | ||
| */ | ||
| #ifndef XGBOOST_TREE_HIST_SAMPLER_H_ | ||
| #define XGBOOST_TREE_HIST_SAMPLER_H_ | ||
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| #include <cstddef> // std::size-t | ||
| #include <cstdint> // std::uint64_t | ||
| #include <random> // bernoulli_distribution, linear_congruential_engine | ||
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| #include "../../common/random.h" // GlobalRandom | ||
| #include "../param.h" // TrainParam | ||
| #include "xgboost/base.h" // GradientPair | ||
| #include "xgboost/context.h" // Context | ||
| #include "xgboost/data.h" // MetaInfo | ||
| #include "xgboost/linalg.h" // TensorView | ||
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| namespace xgboost { | ||
| namespace tree { | ||
| struct RandomReplace { | ||
| public: | ||
| // similar value as for minstd_rand | ||
| static constexpr std::uint64_t kBase = 16807; | ||
| static constexpr std::uint64_t kMod = static_cast<std::uint64_t>(1) << 63; | ||
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| using EngineT = std::linear_congruential_engine<uint64_t, kBase, 0, kMod>; | ||
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| /* | ||
| Right-to-left binary method: https://en.wikipedia.org/wiki/Modular_exponentiation | ||
| */ | ||
| static std::uint64_t SimpleSkip(std::uint64_t exponent, std::uint64_t initial_seed, | ||
| std::uint64_t base, std::uint64_t mod) { | ||
| CHECK_LE(exponent, mod); | ||
| std::uint64_t result = 1; | ||
| while (exponent > 0) { | ||
| if (exponent % 2 == 1) { | ||
| result = (result * base) % mod; | ||
| } | ||
| base = (base * base) % mod; | ||
| exponent = exponent >> 1; | ||
| } | ||
| // with result we can now find the new seed | ||
| return (result * initial_seed) % mod; | ||
| } | ||
| }; | ||
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| // Only uniform sampling, no gradient-based yet. | ||
| inline void SampleGradient(Context const* ctx, TrainParam param, | ||
| linalg::MatrixView<GradientPair> out) { | ||
| CHECK(out.Contiguous()); | ||
| CHECK_EQ(param.sampling_method, TrainParam::kUniform) | ||
| << "Only uniform sampling is supported, gradient-based sampling is only support by GPU Hist."; | ||
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| if (param.subsample >= 1.0) { | ||
| return; | ||
| } | ||
| bst_row_t n_samples = out.Shape(0); | ||
| auto& rnd = common::GlobalRandom(); | ||
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| #if XGBOOST_CUSTOMIZE_GLOBAL_PRNG | ||
| std::bernoulli_distribution coin_flip(param.subsample); | ||
| CHECK_EQ(out.Shape(1), 1) << "Multi-target with sampling for R is not yet supported."; | ||
| for (size_t i = 0; i < n_samples; ++i) { | ||
| if (!(out(i, 0).GetHess() >= 0.0f && coin_flip(rnd)) || out(i, 0).GetGrad() == 0.0f) { | ||
| out(i, 0) = GradientPair(0); | ||
| } | ||
| } | ||
| #else | ||
| std::uint64_t initial_seed = rnd(); | ||
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| auto n_threads = static_cast<size_t>(ctx->Threads()); | ||
| std::size_t const discard_size = n_samples / n_threads; | ||
| std::bernoulli_distribution coin_flip(param.subsample); | ||
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| dmlc::OMPException exc; | ||
| #pragma omp parallel num_threads(n_threads) | ||
| { | ||
| exc.Run([&]() { | ||
| const size_t tid = omp_get_thread_num(); | ||
| const size_t ibegin = tid * discard_size; | ||
| const size_t iend = (tid == (n_threads - 1)) ? n_samples : ibegin + discard_size; | ||
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| const uint64_t displaced_seed = RandomReplace::SimpleSkip( | ||
| ibegin, initial_seed, RandomReplace::kBase, RandomReplace::kMod); | ||
| RandomReplace::EngineT eng(displaced_seed); | ||
| std::size_t n_targets = out.Shape(1); | ||
| if (n_targets > 1) { | ||
| for (std::size_t i = ibegin; i < iend; ++i) { | ||
| if (!coin_flip(eng)) { | ||
| for (std::size_t j = 0; j < n_targets; ++j) { | ||
| out(i, j) = GradientPair{}; | ||
| } | ||
| } | ||
| } | ||
| } else { | ||
| for (std::size_t i = ibegin; i < iend; ++i) { | ||
| if (!coin_flip(eng)) { | ||
| out(i, 0) = GradientPair{}; | ||
| } | ||
| } | ||
| } | ||
| }); | ||
| } | ||
| exc.Rethrow(); | ||
| #endif // XGBOOST_CUSTOMIZE_GLOBAL_PRNG | ||
| } | ||
| } // namespace tree | ||
| } // namespace xgboost | ||
| #endif // XGBOOST_TREE_HIST_SAMPLER_H_ |
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