Implement a general array view. (#7365)
* Replace existing matrix and vector view. This is to prepare for handling higher dimension data and prediction when we support multi-target models.
This commit is contained in:
Jiaming Yuan
GitHub
parent
232144ca09
commit
b06040b6d0
@ -1,113 +1,301 @@
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/*!
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* Copyright 2021 by Contributors
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* Copyright 2021 by XGBoost Contributors
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* \file linalg.h
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* \brief Linear algebra related utilities.
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*/
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#ifndef XGBOOST_LINALG_H_
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#define XGBOOST_LINALG_H_
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#include <xgboost/span.h>
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#include <xgboost/host_device_vector.h>
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#include <xgboost/base.h>
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#include <xgboost/generic_parameters.h>
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#include <xgboost/span.h>
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#include <array>
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#include <algorithm>
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#include <cassert>
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#include <type_traits>
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#include <utility>
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#include <vector>
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namespace xgboost {
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/*!
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* \brief A view over a matrix on contiguous storage.
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namespace linalg {
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namespace detail {
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template <typename S, typename Head, size_t D>
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constexpr size_t Offset(S (&strides)[D], size_t n, size_t dim, Head head) {
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assert(dim < D);
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return n + head * strides[dim];
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}
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template <typename S, size_t D, typename Head, typename... Tail>
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constexpr size_t Offset(S (&strides)[D], size_t n, size_t dim, Head head, Tail &&...rest) {
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assert(dim < D);
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return Offset(strides, n + (head * strides[dim]), dim + 1, rest...);
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}
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struct AllTag {};
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struct IntTag {};
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/**
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* \brief Calculate the dimension of sliced tensor.
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*/
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template <typename T>
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constexpr int32_t CalcSliceDim() {
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return std::is_same<T, IntTag>::value ? 0 : 1;
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}
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template <typename T, typename... S>
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constexpr std::enable_if_t<sizeof...(S) != 0, int32_t> CalcSliceDim() {
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return CalcSliceDim<T>() + CalcSliceDim<S...>();
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}
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template <int32_t D>
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constexpr size_t CalcSize(size_t (&shape)[D]) {
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size_t size = 1;
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for (auto d : shape) {
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size *= d;
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}
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return size;
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}
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template <typename S>
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using RemoveCRType = std::remove_const_t<std::remove_reference_t<S>>;
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template <typename S>
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using IndexToTag = std::conditional_t<std::is_integral<RemoveCRType<S>>::value, IntTag, AllTag>;
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template <int32_t n, typename Fn>
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XGBOOST_DEVICE constexpr auto UnrollLoop(Fn fn) {
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#if defined __CUDA_ARCH__
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#pragma unroll n
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#endif // defined __CUDA_ARCH__
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for (int32_t i = 0; i < n; ++i) {
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fn(i);
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}
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}
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} // namespace detail
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/**
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* \brief Specify all elements in the axis is used for slice.
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*/
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constexpr detail::AllTag All() { return {}; }
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/**
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* \brief A tensor view with static type and shape. It implements indexing and slicing.
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*
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* Most of the algorithms in XGBoost are implemented for both CPU and GPU without using
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* much linear algebra routines, this class is a helper intended to ease some high level
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* operations like indexing into prediction tensor or gradient matrix. It can be passed
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* into CUDA kernel as normal argument for GPU algorithms.
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*/
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template <typename T, int32_t kDim = 5>
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class TensorView {
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public:
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using ShapeT = size_t[kDim];
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using StrideT = ShapeT;
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private:
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StrideT stride_{1};
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ShapeT shape_{0};
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common::Span<T> data_;
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T* ptr_{nullptr}; // pointer of data_ to avoid bound check.
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size_t size_{0};
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int32_t device_{-1};
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// Unlike `Tensor`, the data_ can have arbitrary size since this is just a view.
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XGBOOST_DEVICE void CalcSize() {
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if (data_.empty()) {
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size_ = 0;
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} else {
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size_ = detail::CalcSize(shape_);
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}
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}
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struct SliceHelper {
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size_t old_dim;
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size_t new_dim;
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size_t offset;
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};
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template <int32_t D, typename... S>
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XGBOOST_DEVICE SliceHelper MakeSliceDim(size_t old_dim, size_t new_dim, size_t new_shape[D],
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size_t new_stride[D], detail::AllTag) const {
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new_stride[new_dim] = stride_[old_dim];
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new_shape[new_dim] = shape_[old_dim];
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return {old_dim + 1, new_dim + 1, 0};
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}
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template <int32_t D, typename... S>
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XGBOOST_DEVICE SliceHelper MakeSliceDim(size_t old_dim, size_t new_dim, size_t new_shape[D],
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size_t new_stride[D], detail::AllTag,
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S &&...slices) const {
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new_stride[new_dim] = stride_[old_dim];
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new_shape[new_dim] = shape_[old_dim];
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return MakeSliceDim<D>(old_dim + 1, new_dim + 1, new_shape, new_stride, slices...);
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}
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template <int32_t D, typename Index>
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XGBOOST_DEVICE SliceHelper MakeSliceDim(size_t old_dim, size_t new_dim, size_t new_shape[D],
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size_t new_stride[D], Index i) const {
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return {old_dim + 1, new_dim, stride_[old_dim] * i};
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}
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template <int32_t D, typename Index, typename... S>
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XGBOOST_DEVICE std::enable_if_t<std::is_integral<Index>::value, SliceHelper> MakeSliceDim(
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size_t old_dim, size_t new_dim, size_t new_shape[D], size_t new_stride[D], Index i,
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S &&...slices) const {
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auto offset = stride_[old_dim] * i;
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auto res = MakeSliceDim<D>(old_dim + 1, new_dim, new_shape, new_stride, slices...);
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return {res.old_dim, res.new_dim, res.offset + offset};
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}
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public:
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size_t constexpr static kValueSize = sizeof(T);
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size_t constexpr static kDimension = kDim;
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public:
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/**
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* \brief Create a tensor with data and shape.
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*
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* \tparam I Type of the shape array element.
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* \tparam D Size of the shape array, can be lesser than or equal to tensor dimension.
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*
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* \param data Raw data input, can be const if this tensor has const type in its
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* template parameter.
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* \param shape shape of the tensor
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* \param device Device ordinal
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*/
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template <typename I, int32_t D>
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XGBOOST_DEVICE TensorView(common::Span<T> data, I const (&shape)[D], int32_t device)
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: data_{data}, ptr_{data_.data()}, device_{device} {
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static_assert(D > 0 && D <= kDim, "Invalid shape.");
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// shape
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detail::UnrollLoop<D>([&](auto i) { shape_[i] = shape[i]; });
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for (auto i = D; i < kDim; ++i) {
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shape_[i] = 1;
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}
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// stride
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stride_[kDim - 1] = 1;
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for (int32_t s = kDim - 2; s >= 0; --s) {
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stride_[s] = shape_[s + 1] * stride_[s + 1];
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}
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this->CalcSize();
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};
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/**
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* \brief Create a tensor with data, shape and strides. Don't use this constructor if
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* stride can be calculated from shape.
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*/
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template <typename I, int32_t D>
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XGBOOST_DEVICE TensorView(common::Span<T> data, I const (&shape)[D], I const (&stride)[D],
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int32_t device)
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: data_{data}, ptr_{data_.data()}, device_{device} {
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static_assert(D == kDim, "Invalid shape & stride.");
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detail::UnrollLoop<D>([&](auto i) {
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shape_[i] = shape[i];
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stride_[i] = stride[i];
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});
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this->CalcSize();
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};
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XGBOOST_DEVICE TensorView(TensorView const &that)
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: data_{that.data_}, ptr_{data_.data()}, size_{that.size_}, device_{that.device_} {
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detail::UnrollLoop<kDim>([&](auto i) {
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stride_[i] = that.stride_[i];
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shape_[i] = that.shape_[i];
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});
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}
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/**
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* \brief Index the tensor to obtain a scalar value.
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*
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* \code
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*
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* // Create a 3-dim tensor.
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* Tensor<float, 3> t {data, shape, 0};
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* float pi = 3.14159;
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* t(1, 2, 3) = pi;
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* ASSERT_EQ(t(1, 2, 3), pi);
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*
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* \endcode
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*/
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template <typename... Index>
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XGBOOST_DEVICE T &operator()(Index &&...index) {
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static_assert(sizeof...(index) <= kDim, "Invalid index.");
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size_t offset = detail::Offset(stride_, 0ul, 0ul, index...);
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return ptr_[offset];
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}
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/**
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* \brief Index the tensor to obtain a scalar value.
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*/
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template <typename... Index>
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XGBOOST_DEVICE T const &operator()(Index &&...index) const {
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static_assert(sizeof...(index) <= kDim, "Invalid index.");
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size_t offset = detail::Offset(stride_, 0ul, 0ul, index...);
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return ptr_[offset];
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}
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/**
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* \brief Slice the tensor. The returned tensor has inferred dim and shape.
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*
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* \code
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*
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* // Create a 3-dim tensor.
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* Tensor<float, 3> t {data, shape, 0};
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* // s has 2 dimensions (matrix)
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* auto s = t.Slice(1, All(), All());
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*
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* \endcode
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*/
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template <typename... S>
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XGBOOST_DEVICE auto Slice(S &&...slices) const {
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static_assert(sizeof...(slices) <= kDim, "Invalid slice.");
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int32_t constexpr kNewDim{detail::CalcSliceDim<detail::IndexToTag<S>...>()};
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size_t new_shape[kNewDim];
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size_t new_stride[kNewDim];
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auto res = MakeSliceDim<kNewDim>(size_t(0), size_t(0), new_shape, new_stride, slices...);
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// ret is a different type due to changed dimension, so we can not access its private
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// fields.
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TensorView<T, kNewDim> ret{data_.subspan(data_.empty() ? 0 : res.offset), new_shape, new_stride,
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device_};
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return ret;
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}
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XGBOOST_DEVICE auto Shape() const { return common::Span<size_t const, kDim>{shape_}; }
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/**
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* Get the shape for i^th dimension
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*/
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XGBOOST_DEVICE auto Shape(size_t i) const { return shape_[i]; }
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XGBOOST_DEVICE auto Stride() const { return common::Span<size_t const, kDim>{stride_}; }
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/**
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* Get the stride for i^th dimension, stride is specified as number of items instead of bytes.
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*/
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XGBOOST_DEVICE auto Stride(size_t i) const { return stride_[i]; }
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XGBOOST_DEVICE auto cbegin() const { return data_.cbegin(); } // NOLINT
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XGBOOST_DEVICE auto cend() const { return data_.cend(); } // NOLINT
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XGBOOST_DEVICE auto begin() { return data_.begin(); } // NOLINT
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XGBOOST_DEVICE auto end() { return data_.end(); } // NOLINT
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XGBOOST_DEVICE size_t Size() const { return size_; }
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XGBOOST_DEVICE auto Values() const { return data_; }
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XGBOOST_DEVICE auto DeviceIdx() const { return device_; }
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};
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/**
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* \brief A view over a vector, specialization of Tensor
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*
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* \tparam T data type of vector
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*/
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template <typename T>
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using VectorView = TensorView<T, 1>;
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/**
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* \brief A view over a matrix, specialization of Tensor.
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*
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* \tparam T data type of matrix
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*/
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template <typename T> class MatrixView {
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int32_t device_;
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common::Span<T> values_;
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size_t strides_[2];
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size_t shape_[2];
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template <typename Vec> static auto InferValues(Vec *vec, int32_t device) {
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return device == GenericParameter::kCpuId ? vec->HostSpan()
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: vec->DeviceSpan();
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}
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public:
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/*!
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* \param vec storage.
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* \param strides Strides for matrix.
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* \param shape Rows and columns.
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* \param device Where the data is stored in.
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*/
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MatrixView(HostDeviceVector<T> *vec, std::array<size_t, 2> strides,
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std::array<size_t, 2> shape, int32_t device)
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: device_{device}, values_{InferValues(vec, device)} {
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std::copy(strides.cbegin(), strides.cend(), strides_);
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std::copy(shape.cbegin(), shape.cend(), shape_);
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}
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MatrixView(HostDeviceVector<std::remove_const_t<T>> const *vec,
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std::array<size_t, 2> strides, std::array<size_t, 2> shape,
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int32_t device)
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: device_{device}, values_{InferValues(vec, device)} {
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std::copy(strides.cbegin(), strides.cend(), strides_);
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std::copy(shape.cbegin(), shape.cend(), shape_);
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}
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/*! \brief Row major constructor. */
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MatrixView(HostDeviceVector<T> *vec, std::array<size_t, 2> shape,
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int32_t device)
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: device_{device}, values_{InferValues(vec, device)} {
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std::copy(shape.cbegin(), shape.cend(), shape_);
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strides_[0] = shape[1];
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strides_[1] = 1;
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}
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MatrixView(std::vector<T> *vec, std::array<size_t, 2> shape)
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: device_{GenericParameter::kCpuId}, values_{*vec} {
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CHECK_EQ(vec->size(), shape[0] * shape[1]);
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std::copy(shape.cbegin(), shape.cend(), shape_);
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strides_[0] = shape[1];
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strides_[1] = 1;
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}
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MatrixView(HostDeviceVector<std::remove_const_t<T>> const *vec,
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std::array<size_t, 2> shape, int32_t device)
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: device_{device}, values_{InferValues(vec, device)} {
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std::copy(shape.cbegin(), shape.cend(), shape_);
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strides_[0] = shape[1];
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strides_[1] = 1;
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}
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XGBOOST_DEVICE T const &operator()(size_t r, size_t c) const {
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return values_[strides_[0] * r + strides_[1] * c];
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}
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XGBOOST_DEVICE T &operator()(size_t r, size_t c) {
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return values_[strides_[0] * r + strides_[1] * c];
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}
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auto Strides() const { return strides_; }
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auto Shape() const { return shape_; }
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auto Values() const { return values_; }
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auto Size() const { return shape_[0] * shape_[1]; }
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auto DeviceIdx() const { return device_; }
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};
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/*! \brief A slice for 1 column of MatrixView. Can be extended to row if needed. */
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template <typename T> class VectorView {
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MatrixView<T> matrix_;
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size_t column_;
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public:
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explicit VectorView(MatrixView<T> matrix, size_t column)
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: matrix_{std::move(matrix)}, column_{column} {}
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XGBOOST_DEVICE T &operator[](size_t i) {
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return matrix_(i, column_);
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}
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XGBOOST_DEVICE T const &operator[](size_t i) const {
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return matrix_(i, column_);
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}
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size_t Size() { return matrix_.Shape()[0]; }
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int32_t DeviceIdx() const { return matrix_.DeviceIdx(); }
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};
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template <typename T>
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using MatrixView = TensorView<T, 2>;
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} // namespace linalg
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} // namespace xgboost
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#endif // XGBOOST_LINALG_H_
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@ -73,7 +73,7 @@ class TreeUpdater : public Configurable {
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* updated by the time this function returns.
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*/
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virtual bool UpdatePredictionCache(const DMatrix * /*data*/,
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VectorView<float> /*out_preds*/) {
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linalg::VectorView<float> /*out_preds*/) {
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return false;
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}
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@ -243,7 +243,10 @@ class GBLinear : public GradientBooster {
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// The bias is the last weight
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out_scores->resize(model_.weight.size() - learner_model_param_->num_output_group, 0);
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auto n_groups = learner_model_param_->num_output_group;
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MatrixView<float> scores{out_scores, {learner_model_param_->num_feature, n_groups}};
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linalg::TensorView<float, 2> scores{
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*out_scores,
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{learner_model_param_->num_feature, n_groups},
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GenericParameter::kCpuId};
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for (size_t i = 0; i < learner_model_param_->num_feature; ++i) {
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for (bst_group_t g = 0; g < n_groups; ++g) {
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scores(i, g) = model_[i][g];
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@ -229,16 +229,19 @@ void GBTree::DoBoost(DMatrix* p_fmat,
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auto device = tparam_.tree_method != TreeMethod::kGPUHist
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? GenericParameter::kCpuId
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: generic_param_->gpu_id;
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auto out = MatrixView<float>(
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&predt->predictions,
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{static_cast<size_t>(p_fmat->Info().num_row_), static_cast<size_t>(ngroup)}, device);
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auto out = linalg::TensorView<float, 2>{
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device == GenericParameter::kCpuId ? predt->predictions.HostSpan()
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: predt->predictions.DeviceSpan(),
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{static_cast<size_t>(p_fmat->Info().num_row_),
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static_cast<size_t>(ngroup)},
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device};
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CHECK_NE(ngroup, 0);
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if (ngroup == 1) {
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std::vector<std::unique_ptr<RegTree>> ret;
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BoostNewTrees(in_gpair, p_fmat, 0, &ret);
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const size_t num_new_trees = ret.size();
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new_trees.push_back(std::move(ret));
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auto v_predt = VectorView<float>{out, 0};
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auto v_predt = out.Slice(linalg::All(), 0);
|
||||
if (updaters_.size() > 0 && num_new_trees == 1 &&
|
||||
predt->predictions.Size() > 0 &&
|
||||
updaters_.back()->UpdatePredictionCache(p_fmat, v_predt)) {
|
||||
@ -257,7 +260,7 @@ void GBTree::DoBoost(DMatrix* p_fmat,
|
||||
BoostNewTrees(&tmp, p_fmat, gid, &ret);
|
||||
const size_t num_new_trees = ret.size();
|
||||
new_trees.push_back(std::move(ret));
|
||||
auto v_predt = VectorView<float>{out, static_cast<size_t>(gid)};
|
||||
auto v_predt = out.Slice(linalg::All(), gid);
|
||||
if (!(updaters_.size() > 0 && predt->predictions.Size() > 0 &&
|
||||
num_new_trees == 1 &&
|
||||
updaters_.back()->UpdatePredictionCache(p_fmat, v_predt))) {
|
||||
|
||||
@ -12,15 +12,14 @@ namespace gbm {
|
||||
void GPUCopyGradient(HostDeviceVector<GradientPair> const *in_gpair,
|
||||
bst_group_t n_groups, bst_group_t group_id,
|
||||
HostDeviceVector<GradientPair> *out_gpair) {
|
||||
MatrixView<GradientPair const> in{
|
||||
in_gpair,
|
||||
{n_groups, 1ul},
|
||||
auto mat = linalg::TensorView<GradientPair const, 2>(
|
||||
in_gpair->ConstDeviceSpan(),
|
||||
{in_gpair->Size() / n_groups, static_cast<size_t>(n_groups)},
|
||||
in_gpair->DeviceIdx()};
|
||||
auto v_in = VectorView<GradientPair const>{in, group_id};
|
||||
in_gpair->DeviceIdx());
|
||||
auto v_in = mat.Slice(linalg::All(), group_id);
|
||||
out_gpair->Resize(v_in.Size());
|
||||
auto d_out = out_gpair->DeviceSpan();
|
||||
dh::LaunchN(v_in.Size(), [=] __device__(size_t i) { d_out[i] = v_in[i]; });
|
||||
dh::LaunchN(v_in.Size(), [=] __device__(size_t i) { d_out[i] = v_in(i); });
|
||||
}
|
||||
|
||||
void GPUDartPredictInc(common::Span<float> out_predts,
|
||||
|
||||
@ -13,6 +13,7 @@
|
||||
#include <vector>
|
||||
|
||||
#include "rabit/rabit.h"
|
||||
#include "xgboost/linalg.h"
|
||||
#include "xgboost/host_device_vector.h"
|
||||
#include "xgboost/metric.h"
|
||||
|
||||
@ -83,41 +84,45 @@ double MultiClassOVR(common::Span<float const> predts, MetaInfo const &info,
|
||||
CHECK_NE(n_classes, 0);
|
||||
auto const &labels = info.labels_.ConstHostVector();
|
||||
|
||||
std::vector<double> results(n_classes * 3, 0);
|
||||
auto s_results = common::Span<double>(results);
|
||||
|
||||
auto local_area = s_results.subspan(0, n_classes);
|
||||
auto tp = s_results.subspan(n_classes, n_classes);
|
||||
auto auc = s_results.subspan(2 * n_classes, n_classes);
|
||||
std::vector<double> results_storage(n_classes * 3, 0);
|
||||
linalg::TensorView<double> results(results_storage,
|
||||
{n_classes, static_cast<size_t>(3)},
|
||||
GenericParameter::kCpuId);
|
||||
auto local_area = results.Slice(linalg::All(), 0);
|
||||
auto tp = results.Slice(linalg::All(), 1);
|
||||
auto auc = results.Slice(linalg::All(), 2);
|
||||
|
||||
auto weights = OptionalWeights{info.weights_.ConstHostSpan()};
|
||||
auto predts_t = linalg::TensorView<float const, 2>(
|
||||
predts, {static_cast<size_t>(info.num_row_), n_classes},
|
||||
GenericParameter::kCpuId);
|
||||
|
||||
if (!info.labels_.Empty()) {
|
||||
common::ParallelFor(n_classes, n_threads, [&](auto c) {
|
||||
std::vector<float> proba(info.labels_.Size());
|
||||
std::vector<float> response(info.labels_.Size());
|
||||
for (size_t i = 0; i < proba.size(); ++i) {
|
||||
proba[i] = predts[i * n_classes + c];
|
||||
proba[i] = predts_t(i, c);
|
||||
response[i] = labels[i] == c ? 1.0f : 0.0;
|
||||
}
|
||||
double fp;
|
||||
std::tie(fp, tp[c], auc[c]) = binary_auc(proba, response, weights);
|
||||
local_area[c] = fp * tp[c];
|
||||
std::tie(fp, tp(c), auc(c)) = binary_auc(proba, response, weights);
|
||||
local_area(c) = fp * tp(c);
|
||||
});
|
||||
}
|
||||
|
||||
// we have 2 averages going in here, first is among workers, second is among
|
||||
// classes. allreduce sums up fp/tp auc for each class.
|
||||
rabit::Allreduce<rabit::op::Sum>(results.data(), results.size());
|
||||
rabit::Allreduce<rabit::op::Sum>(results.Values().data(), results.Values().size());
|
||||
double auc_sum{0};
|
||||
double tp_sum{0};
|
||||
for (size_t c = 0; c < n_classes; ++c) {
|
||||
if (local_area[c] != 0) {
|
||||
if (local_area(c) != 0) {
|
||||
// normalize and weight it by prevalence. After allreduce, `local_area`
|
||||
// means the total covered area (not area under curve, rather it's the
|
||||
// accessible area for each worker) for each class.
|
||||
auc_sum += auc[c] / local_area[c] * tp[c];
|
||||
tp_sum += tp[c];
|
||||
auc_sum += auc(c) / local_area(c) * tp(c);
|
||||
tp_sum += tp(c);
|
||||
} else {
|
||||
auc_sum = std::numeric_limits<double>::quiet_NaN();
|
||||
break;
|
||||
|
||||
@ -496,7 +496,7 @@ struct GPUHistMakerDevice {
|
||||
});
|
||||
}
|
||||
|
||||
void UpdatePredictionCache(VectorView<float> out_preds_d) {
|
||||
void UpdatePredictionCache(linalg::VectorView<float> out_preds_d) {
|
||||
dh::safe_cuda(cudaSetDevice(device_id));
|
||||
CHECK_EQ(out_preds_d.DeviceIdx(), device_id);
|
||||
auto d_ridx = row_partitioner->GetRows();
|
||||
@ -512,13 +512,13 @@ struct GPUHistMakerDevice {
|
||||
auto d_node_sum_gradients = device_node_sum_gradients.data().get();
|
||||
auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
|
||||
|
||||
dh::LaunchN(d_ridx.size(), [=] __device__(int local_idx) {
|
||||
dh::LaunchN(d_ridx.size(), [=, out_preds_d = out_preds_d] __device__(
|
||||
int local_idx) mutable {
|
||||
int pos = d_position[local_idx];
|
||||
bst_float weight = evaluator.CalcWeight(
|
||||
pos, param_d, GradStats{d_node_sum_gradients[pos]});
|
||||
static_assert(!std::is_const<decltype(out_preds_d)>::value, "");
|
||||
auto v_predt = out_preds_d; // for some reason out_preds_d is const by both nvcc and clang.
|
||||
v_predt[d_ridx[local_idx]] += weight * param_d.learning_rate;
|
||||
out_preds_d(d_ridx[local_idx]) += weight * param_d.learning_rate;
|
||||
});
|
||||
row_partitioner.reset();
|
||||
}
|
||||
@ -834,7 +834,8 @@ class GPUHistMakerSpecialised {
|
||||
maker->UpdateTree(gpair, p_fmat, p_tree, &reducer_);
|
||||
}
|
||||
|
||||
bool UpdatePredictionCache(const DMatrix* data, VectorView<bst_float> p_out_preds) {
|
||||
bool UpdatePredictionCache(const DMatrix *data,
|
||||
linalg::VectorView<bst_float> p_out_preds) {
|
||||
if (maker == nullptr || p_last_fmat_ == nullptr || p_last_fmat_ != data) {
|
||||
return false;
|
||||
}
|
||||
@ -920,8 +921,9 @@ class GPUHistMaker : public TreeUpdater {
|
||||
}
|
||||
}
|
||||
|
||||
bool UpdatePredictionCache(const DMatrix *data,
|
||||
VectorView<bst_float> p_out_preds) override {
|
||||
bool
|
||||
UpdatePredictionCache(const DMatrix *data,
|
||||
linalg::VectorView<bst_float> p_out_preds) override {
|
||||
if (hist_maker_param_.single_precision_histogram) {
|
||||
return float_maker_->UpdatePredictionCache(data, p_out_preds);
|
||||
} else {
|
||||
|
||||
@ -105,7 +105,7 @@ void QuantileHistMaker::Update(HostDeviceVector<GradientPair> *gpair,
|
||||
}
|
||||
|
||||
bool QuantileHistMaker::UpdatePredictionCache(
|
||||
const DMatrix* data, VectorView<float> out_preds) {
|
||||
const DMatrix* data, linalg::VectorView<float> out_preds) {
|
||||
if (hist_maker_param_.single_precision_histogram && float_builder_) {
|
||||
return float_builder_->UpdatePredictionCache(data, out_preds);
|
||||
} else if (double_builder_) {
|
||||
@ -319,7 +319,7 @@ void QuantileHistMaker::Builder<GradientSumT>::Update(
|
||||
template<typename GradientSumT>
|
||||
bool QuantileHistMaker::Builder<GradientSumT>::UpdatePredictionCache(
|
||||
const DMatrix* data,
|
||||
VectorView<float> out_preds) {
|
||||
linalg::VectorView<float> out_preds) {
|
||||
// p_last_fmat_ is a valid pointer as long as UpdatePredictionCache() is called in
|
||||
// conjunction with Update().
|
||||
if (!p_last_fmat_ || !p_last_tree_ || data != p_last_fmat_ ||
|
||||
@ -352,7 +352,7 @@ bool QuantileHistMaker::Builder<GradientSumT>::UpdatePredictionCache(
|
||||
leaf_value = (*p_last_tree_)[nid].LeafValue();
|
||||
|
||||
for (const size_t* it = rowset.begin + r.begin(); it < rowset.begin + r.end(); ++it) {
|
||||
out_preds[*it] += leaf_value;
|
||||
out_preds(*it) += leaf_value;
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
@ -105,7 +105,7 @@ class QuantileHistMaker: public TreeUpdater {
|
||||
const std::vector<RegTree*>& trees) override;
|
||||
|
||||
bool UpdatePredictionCache(const DMatrix *data,
|
||||
VectorView<float> out_preds) override;
|
||||
linalg::VectorView<float> out_preds) override;
|
||||
|
||||
void LoadConfig(Json const& in) override {
|
||||
auto const& config = get<Object const>(in);
|
||||
@ -174,7 +174,7 @@ class QuantileHistMaker: public TreeUpdater {
|
||||
RegTree* p_tree);
|
||||
|
||||
bool UpdatePredictionCache(const DMatrix* data,
|
||||
VectorView<float> out_preds);
|
||||
linalg::VectorView<float> out_preds);
|
||||
|
||||
protected:
|
||||
// initialize temp data structure
|
||||
|
||||
@ -1,18 +1,19 @@
|
||||
#include <gtest/gtest.h>
|
||||
#include <xgboost/host_device_vector.h>
|
||||
#include <xgboost/linalg.h>
|
||||
|
||||
#include <numeric>
|
||||
|
||||
namespace xgboost {
|
||||
|
||||
namespace linalg {
|
||||
auto MakeMatrixFromTest(HostDeviceVector<float> *storage, size_t n_rows, size_t n_cols) {
|
||||
storage->Resize(n_rows * n_cols);
|
||||
auto& h_storage = storage->HostVector();
|
||||
auto &h_storage = storage->HostVector();
|
||||
|
||||
std::iota(h_storage.begin(), h_storage.end(), 0);
|
||||
|
||||
auto m = MatrixView<float>{storage, {n_cols, 1}, {n_rows, n_cols}, -1};
|
||||
auto m = linalg::TensorView<float, 2>{h_storage, {n_rows, static_cast<size_t>(n_cols)}, -1};
|
||||
return m;
|
||||
|
||||
}
|
||||
|
||||
TEST(Linalg, Matrix) {
|
||||
@ -28,11 +29,84 @@ TEST(Linalg, Vector) {
|
||||
size_t kRows = 31, kCols = 77;
|
||||
HostDeviceVector<float> storage;
|
||||
auto m = MakeMatrixFromTest(&storage, kRows, kCols);
|
||||
auto v = VectorView<float>(m, 3);
|
||||
auto v = m.Slice(linalg::All(), 3);
|
||||
for (size_t i = 0; i < v.Size(); ++i) {
|
||||
ASSERT_EQ(v[i], m(i, 3));
|
||||
ASSERT_EQ(v(i), m(i, 3));
|
||||
}
|
||||
|
||||
ASSERT_EQ(v[0], 3);
|
||||
ASSERT_EQ(v(0), 3);
|
||||
}
|
||||
|
||||
TEST(Linalg, Tensor) {
|
||||
std::vector<double> data(2 * 3 * 4, 0);
|
||||
std::iota(data.begin(), data.end(), 0);
|
||||
|
||||
TensorView<double> t{data, {2, 3, 4}, -1};
|
||||
ASSERT_EQ(t.Shape()[0], 2);
|
||||
ASSERT_EQ(t.Shape()[1], 3);
|
||||
ASSERT_EQ(t.Shape()[2], 4);
|
||||
|
||||
float v = t(0, 1, 2);
|
||||
ASSERT_EQ(v, 6);
|
||||
|
||||
auto s = t.Slice(1, All(), All());
|
||||
ASSERT_EQ(s.Shape().size(), 2);
|
||||
ASSERT_EQ(s.Shape()[0], 3);
|
||||
ASSERT_EQ(s.Shape()[1], 4);
|
||||
|
||||
std::vector<std::vector<double>> sol{
|
||||
{12.0, 13.0, 14.0, 15.0}, {16.0, 17.0, 18.0, 19.0}, {20.0, 21.0, 22.0, 23.0}};
|
||||
for (size_t i = 0; i < s.Shape()[0]; ++i) {
|
||||
for (size_t j = 0; j < s.Shape()[1]; ++j) {
|
||||
ASSERT_EQ(s(i, j), sol[i][j]);
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
// as vector
|
||||
TensorView<double, 1> vec{data, {data.size()}, -1};
|
||||
ASSERT_EQ(vec.Size(), data.size());
|
||||
ASSERT_EQ(vec.Shape(0), data.size());
|
||||
ASSERT_EQ(vec.Shape().size(), 1);
|
||||
for (size_t i = 0; i < data.size(); ++i) {
|
||||
ASSERT_EQ(vec(i), data[i]);
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
// as matrix
|
||||
TensorView<double, 2> mat(data, {6, 4}, -1);
|
||||
auto s = mat.Slice(2, All());
|
||||
ASSERT_EQ(s.Shape().size(), 1);
|
||||
s = mat.Slice(All(), 1);
|
||||
ASSERT_EQ(s.Shape().size(), 1);
|
||||
}
|
||||
|
||||
{
|
||||
// assignment
|
||||
TensorView<double, 3> t{data, {2, 3, 4}, 0};
|
||||
double pi = 3.14159;
|
||||
t(1, 2, 3) = pi;
|
||||
ASSERT_EQ(t(1, 2, 3), pi);
|
||||
}
|
||||
|
||||
{
|
||||
// Don't assign the initial dimension, tensor should be able to deduce the correct dim
|
||||
// for Slice.
|
||||
TensorView<double> t{data, {2, 3, 4}, 0};
|
||||
auto s = t.Slice(1, 2, All());
|
||||
static_assert(decltype(s)::kDimension == 1, "");
|
||||
}
|
||||
}
|
||||
|
||||
TEST(Linalg, Empty) {
|
||||
auto t = TensorView<double, 2>{{}, {0, 3}, GenericParameter::kCpuId};
|
||||
for (int32_t i : {0, 1, 2}) {
|
||||
auto s = t.Slice(All(), i);
|
||||
ASSERT_EQ(s.Size(), 0);
|
||||
ASSERT_EQ(s.Shape().size(), 1);
|
||||
ASSERT_EQ(s.Shape(0), 0);
|
||||
}
|
||||
}
|
||||
} // namespace linalg
|
||||
} // namespace xgboost
|
||||
|
||||
@ -400,10 +400,8 @@ void UpdateTree(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
|
||||
hist_maker.Configure(args, &generic_param);
|
||||
|
||||
hist_maker.Update(gpair, dmat, {tree});
|
||||
hist_maker.UpdatePredictionCache(
|
||||
dmat,
|
||||
VectorView<float>{
|
||||
MatrixView<float>(preds, {preds->Size(), 1}, preds->DeviceIdx()), 0});
|
||||
auto cache = linalg::VectorView<float>{preds->DeviceSpan(), {preds->Size()}, 0};
|
||||
hist_maker.UpdatePredictionCache(dmat, cache);
|
||||
}
|
||||
|
||||
TEST(GpuHist, UniformSampling) {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user