fdf533f2b9
Support adaptive tree, a feature supported by both sklearn and lightgbm. The tree leaf is recomputed based on residue of labels and predictions after construction. For l1 error, the optimal value is the median (50 percentile). This is marked as experimental support for the following reasons: - The value is not well defined for distributed training, where we might have empty leaves for local workers. Right now I just use the original leaf value for computing the average with other workers, which might cause significant errors. - Some follow-ups are required, for exact, pruner, and optimization for quantile function. Also, we need to calculate the initial estimation.
50 lines
1.7 KiB
Plaintext
50 lines
1.7 KiB
Plaintext
/*!
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* Copyright 2021-2022 by XGBoost Contributors
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*/
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#ifndef XGBOOST_COMMON_LINALG_OP_CUH_
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#define XGBOOST_COMMON_LINALG_OP_CUH_
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#include "xgboost/generic_parameters.h"
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#include "device_helpers.cuh"
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#include "linalg_op.h"
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#include "xgboost/linalg.h"
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namespace xgboost {
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namespace linalg {
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template <typename T, int32_t D, typename Fn>
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void ElementWiseKernelDevice(linalg::TensorView<T, D> t, Fn&& fn, cudaStream_t s = nullptr) {
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dh::safe_cuda(cudaSetDevice(t.DeviceIdx()));
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static_assert(std::is_void<std::result_of_t<Fn(size_t, T&)>>::value,
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"For function with return, use transform instead.");
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if (t.Contiguous()) {
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auto ptr = t.Values().data();
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dh::LaunchN(t.Size(), s, [=] __device__(size_t i) mutable { fn(i, ptr[i]); });
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} else {
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dh::LaunchN(t.Size(), s, [=] __device__(size_t i) mutable {
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T& v = detail::Apply(t, linalg::UnravelIndex(i, t.Shape()));
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fn(i, v);
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});
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}
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}
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template <typename T, int32_t D, typename Fn>
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void ElementWiseTransformDevice(linalg::TensorView<T, D> t, Fn&& fn, cudaStream_t s = nullptr) {
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if (t.Contiguous()) {
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auto ptr = t.Values().data();
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dh::LaunchN(t.Size(), s, [=] __device__(size_t i) { ptr[i] = fn(i, ptr[i]); });
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} else {
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dh::LaunchN(t.Size(), s, [=] __device__(size_t i) mutable {
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T& v = detail::Apply(t, linalg::UnravelIndex(i, t.Shape()));
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v = fn(i, v);
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});
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}
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}
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template <typename T, int32_t D, typename Fn>
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void ElementWiseKernel(Context const* ctx, linalg::TensorView<T, D> t, Fn&& fn) {
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ctx->IsCPU() ? ElementWiseKernelHost(t, ctx->Threads(), fn) : ElementWiseKernelDevice(t, fn);
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}
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} // namespace linalg
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} // namespace xgboost
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#endif // XGBOOST_COMMON_LINALG_OP_CUH_
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