Cleanup to prepare for using mmap pointer in external memory. (#9317)
- Update SparseDMatrix comment. - Use a pointer in the bitfield. We will replace the `std::vector<bool>` in `ColumnMatrix` with bitfield. - Clean up the page source. The timer is removed as it's inaccurate once we swap the mmap pointer into the page.
This commit is contained in:
Jiaming Yuan
GitHub
parent
4066d68261
commit
54da4b3185
@ -70,7 +70,7 @@ NcclDeviceCommunicator::~NcclDeviceCommunicator() {
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namespace {
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ncclDataType_t GetNcclDataType(DataType const &data_type) {
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ncclDataType_t result;
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ncclDataType_t result{ncclInt8};
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switch (data_type) {
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case DataType::kInt8:
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result = ncclInt8;
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@ -108,7 +108,7 @@ bool IsBitwiseOp(Operation const &op) {
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}
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ncclRedOp_t GetNcclRedOp(Operation const &op) {
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ncclRedOp_t result;
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ncclRedOp_t result{ncclMax};
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switch (op) {
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case Operation::kMax:
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result = ncclMax;
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@ -1,5 +1,5 @@
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/*!
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* Copyright 2019 by Contributors
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/**
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* Copyright 2019-2023, XGBoost Contributors
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* \file bitfield.h
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*/
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#ifndef XGBOOST_COMMON_BITFIELD_H_
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@ -50,14 +50,17 @@ __forceinline__ __device__ BitFieldAtomicType AtomicAnd(BitFieldAtomicType* addr
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}
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#endif // defined(__CUDACC__)
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/*!
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* \brief A non-owning type with auxiliary methods defined for manipulating bits.
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/**
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* @brief A non-owning type with auxiliary methods defined for manipulating bits.
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*
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* \tparam Direction Whether the bits start from left or from right.
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* @tparam VT Underlying value type, must be an unsigned integer.
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* @tparam Direction Whether the bits start from left or from right.
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* @tparam IsConst Whether the view is const.
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*/
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template <typename VT, typename Direction, bool IsConst = false>
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struct BitFieldContainer {
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using value_type = std::conditional_t<IsConst, VT const, VT>; // NOLINT
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using size_type = size_t; // NOLINT
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using index_type = size_t; // NOLINT
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using pointer = value_type*; // NOLINT
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@ -70,8 +73,9 @@ struct BitFieldContainer {
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};
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private:
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common::Span<value_type> bits_;
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static_assert(!std::is_signed<VT>::value, "Must use unsiged type as underlying storage.");
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value_type* bits_{nullptr};
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size_type n_values_{0};
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static_assert(!std::is_signed<VT>::value, "Must use an unsiged type as the underlying storage.");
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public:
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XGBOOST_DEVICE static Pos ToBitPos(index_type pos) {
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@ -86,13 +90,15 @@ struct BitFieldContainer {
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public:
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BitFieldContainer() = default;
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XGBOOST_DEVICE explicit BitFieldContainer(common::Span<value_type> bits) : bits_{bits} {}
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XGBOOST_DEVICE BitFieldContainer(BitFieldContainer const& other) : bits_{other.bits_} {}
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XGBOOST_DEVICE explicit BitFieldContainer(common::Span<value_type> bits)
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: bits_{bits.data()}, n_values_{bits.size()} {}
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BitFieldContainer(BitFieldContainer const& other) = default;
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BitFieldContainer(BitFieldContainer&& other) = default;
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BitFieldContainer &operator=(BitFieldContainer const &that) = default;
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BitFieldContainer &operator=(BitFieldContainer &&that) = default;
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XGBOOST_DEVICE common::Span<value_type> Bits() { return bits_; }
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XGBOOST_DEVICE common::Span<value_type const> Bits() const { return bits_; }
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XGBOOST_DEVICE auto Bits() { return common::Span<value_type>{bits_, NumValues()}; }
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XGBOOST_DEVICE auto Bits() const { return common::Span<value_type const>{bits_, NumValues()}; }
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/*\brief Compute the size of needed memory allocation. The returned value is in terms
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* of number of elements with `BitFieldContainer::value_type'.
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@ -103,17 +109,17 @@ struct BitFieldContainer {
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#if defined(__CUDA_ARCH__)
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__device__ BitFieldContainer& operator|=(BitFieldContainer const& rhs) {
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auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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size_t min_size = min(bits_.size(), rhs.bits_.size());
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size_t min_size = min(NumValues(), rhs.NumValues());
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if (tid < min_size) {
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bits_[tid] |= rhs.bits_[tid];
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Data()[tid] |= rhs.Data()[tid];
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}
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return *this;
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}
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#else
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BitFieldContainer& operator|=(BitFieldContainer const& rhs) {
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size_t min_size = std::min(bits_.size(), rhs.bits_.size());
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size_t min_size = std::min(NumValues(), rhs.NumValues());
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for (size_t i = 0; i < min_size; ++i) {
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bits_[i] |= rhs.bits_[i];
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Data()[i] |= rhs.Data()[i];
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}
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return *this;
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}
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@ -121,75 +127,85 @@ struct BitFieldContainer {
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#if defined(__CUDA_ARCH__)
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__device__ BitFieldContainer& operator&=(BitFieldContainer const& rhs) {
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size_t min_size = min(bits_.size(), rhs.bits_.size());
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size_t min_size = min(NumValues(), rhs.NumValues());
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auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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if (tid < min_size) {
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bits_[tid] &= rhs.bits_[tid];
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Data()[tid] &= rhs.Data()[tid];
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}
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return *this;
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}
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#else
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BitFieldContainer& operator&=(BitFieldContainer const& rhs) {
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size_t min_size = std::min(bits_.size(), rhs.bits_.size());
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size_t min_size = std::min(NumValues(), rhs.NumValues());
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for (size_t i = 0; i < min_size; ++i) {
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bits_[i] &= rhs.bits_[i];
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Data()[i] &= rhs.Data()[i];
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}
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return *this;
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}
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#endif // defined(__CUDA_ARCH__)
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#if defined(__CUDA_ARCH__)
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__device__ auto Set(index_type pos) {
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__device__ auto Set(index_type pos) noexcept(true) {
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Pos pos_v = Direction::Shift(ToBitPos(pos));
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value_type& value = bits_[pos_v.int_pos];
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value_type& value = Data()[pos_v.int_pos];
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value_type set_bit = kOne << pos_v.bit_pos;
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using Type = typename dh::detail::AtomicDispatcher<sizeof(value_type)>::Type;
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atomicOr(reinterpret_cast<Type *>(&value), set_bit);
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}
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__device__ void Clear(index_type pos) {
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__device__ void Clear(index_type pos) noexcept(true) {
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Pos pos_v = Direction::Shift(ToBitPos(pos));
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value_type& value = bits_[pos_v.int_pos];
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value_type& value = Data()[pos_v.int_pos];
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value_type clear_bit = ~(kOne << pos_v.bit_pos);
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using Type = typename dh::detail::AtomicDispatcher<sizeof(value_type)>::Type;
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atomicAnd(reinterpret_cast<Type *>(&value), clear_bit);
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}
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#else
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void Set(index_type pos) {
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void Set(index_type pos) noexcept(true) {
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Pos pos_v = Direction::Shift(ToBitPos(pos));
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value_type& value = bits_[pos_v.int_pos];
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value_type& value = Data()[pos_v.int_pos];
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value_type set_bit = kOne << pos_v.bit_pos;
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value |= set_bit;
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}
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void Clear(index_type pos) {
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void Clear(index_type pos) noexcept(true) {
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Pos pos_v = Direction::Shift(ToBitPos(pos));
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value_type& value = bits_[pos_v.int_pos];
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value_type& value = Data()[pos_v.int_pos];
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value_type clear_bit = ~(kOne << pos_v.bit_pos);
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value &= clear_bit;
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}
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#endif // defined(__CUDA_ARCH__)
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XGBOOST_DEVICE bool Check(Pos pos_v) const {
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XGBOOST_DEVICE bool Check(Pos pos_v) const noexcept(true) {
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pos_v = Direction::Shift(pos_v);
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SPAN_LT(pos_v.int_pos, bits_.size());
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value_type const value = bits_[pos_v.int_pos];
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assert(pos_v.int_pos < NumValues());
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value_type const value = Data()[pos_v.int_pos];
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value_type const test_bit = kOne << pos_v.bit_pos;
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value_type result = test_bit & value;
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return static_cast<bool>(result);
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}
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XGBOOST_DEVICE bool Check(index_type pos) const {
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[[nodiscard]] XGBOOST_DEVICE bool Check(index_type pos) const noexcept(true) {
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Pos pos_v = ToBitPos(pos);
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return Check(pos_v);
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}
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/**
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* @brief Returns the total number of bits that can be viewed. This is equal to or
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* larger than the acutal number of valid bits.
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*/
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[[nodiscard]] XGBOOST_DEVICE size_type Capacity() const noexcept(true) {
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return kValueSize * NumValues();
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}
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/**
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* @brief Number of storage unit used in this bit field.
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*/
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[[nodiscard]] XGBOOST_DEVICE size_type NumValues() const noexcept(true) { return n_values_; }
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XGBOOST_DEVICE size_t Size() const { return kValueSize * bits_.size(); }
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XGBOOST_DEVICE pointer Data() const noexcept(true) { return bits_; }
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XGBOOST_DEVICE pointer Data() const { return bits_.data(); }
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inline friend std::ostream &
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operator<<(std::ostream &os, BitFieldContainer<VT, Direction, IsConst> field) {
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os << "Bits " << "storage size: " << field.bits_.size() << "\n";
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for (typename common::Span<value_type>::index_type i = 0; i < field.bits_.size(); ++i) {
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std::bitset<BitFieldContainer<VT, Direction, IsConst>::kValueSize> bset(field.bits_[i]);
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inline friend std::ostream& operator<<(std::ostream& os,
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BitFieldContainer<VT, Direction, IsConst> field) {
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os << "Bits "
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<< "storage size: " << field.NumValues() << "\n";
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for (typename common::Span<value_type>::index_type i = 0; i < field.NumValues(); ++i) {
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std::bitset<BitFieldContainer<VT, Direction, IsConst>::kValueSize> bset(field.Data()[i]);
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os << bset << "\n";
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}
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return os;
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@ -1,5 +1,5 @@
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/*!
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* Copyright 2020-2022 by XGBoost Contributors
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/**
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* Copyright 2020-2023, XGBoost Contributors
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* \file categorical.h
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*/
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#ifndef XGBOOST_COMMON_CATEGORICAL_H_
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@ -10,7 +10,6 @@
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#include "bitfield.h"
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#include "xgboost/base.h"
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#include "xgboost/data.h"
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#include "xgboost/parameter.h"
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#include "xgboost/span.h"
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namespace xgboost {
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@ -84,7 +84,7 @@ class HistogramCuts {
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return *this;
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}
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uint32_t FeatureBins(bst_feature_t feature) const {
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[[nodiscard]] bst_bin_t FeatureBins(bst_feature_t feature) const {
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return cut_ptrs_.ConstHostVector().at(feature + 1) - cut_ptrs_.ConstHostVector()[feature];
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}
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@ -92,8 +92,8 @@ class HistogramCuts {
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std::vector<float> const& Values() const { return cut_values_.ConstHostVector(); }
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std::vector<float> const& MinValues() const { return min_vals_.ConstHostVector(); }
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bool HasCategorical() const { return has_categorical_; }
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float MaxCategory() const { return max_cat_; }
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[[nodiscard]] bool HasCategorical() const { return has_categorical_; }
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[[nodiscard]] float MaxCategory() const { return max_cat_; }
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/**
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* \brief Set meta info about categorical features.
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*
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@ -105,12 +105,13 @@ class HistogramCuts {
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max_cat_ = max_cat;
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}
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size_t TotalBins() const { return cut_ptrs_.ConstHostVector().back(); }
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[[nodiscard]] bst_bin_t TotalBins() const { return cut_ptrs_.ConstHostVector().back(); }
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// Return the index of a cut point that is strictly greater than the input
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// value, or the last available index if none exists
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bst_bin_t SearchBin(float value, bst_feature_t column_id, std::vector<uint32_t> const& ptrs,
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std::vector<float> const& values) const {
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[[nodiscard]] bst_bin_t SearchBin(float value, bst_feature_t column_id,
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std::vector<uint32_t> const& ptrs,
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std::vector<float> const& values) const {
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auto end = ptrs[column_id + 1];
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auto beg = ptrs[column_id];
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auto it = std::upper_bound(values.cbegin() + beg, values.cbegin() + end, value);
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@ -119,20 +120,20 @@ class HistogramCuts {
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return idx;
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}
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bst_bin_t SearchBin(float value, bst_feature_t column_id) const {
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[[nodiscard]] bst_bin_t SearchBin(float value, bst_feature_t column_id) const {
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return this->SearchBin(value, column_id, Ptrs(), Values());
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}
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/**
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* \brief Search the bin index for numerical feature.
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*/
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bst_bin_t SearchBin(Entry const& e) const { return SearchBin(e.fvalue, e.index); }
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[[nodiscard]] bst_bin_t SearchBin(Entry const& e) const { return SearchBin(e.fvalue, e.index); }
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/**
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* \brief Search the bin index for categorical feature.
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*/
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bst_bin_t SearchCatBin(float value, bst_feature_t fidx, std::vector<uint32_t> const& ptrs,
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std::vector<float> const& vals) const {
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[[nodiscard]] bst_bin_t SearchCatBin(float value, bst_feature_t fidx,
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std::vector<uint32_t> const& ptrs,
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std::vector<float> const& vals) const {
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auto end = ptrs.at(fidx + 1) + vals.cbegin();
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auto beg = ptrs[fidx] + vals.cbegin();
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// Truncates the value in case it's not perfectly rounded.
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@ -143,12 +144,14 @@ class HistogramCuts {
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}
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return bin_idx;
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}
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bst_bin_t SearchCatBin(float value, bst_feature_t fidx) const {
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[[nodiscard]] bst_bin_t SearchCatBin(float value, bst_feature_t fidx) const {
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auto const& ptrs = this->Ptrs();
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auto const& vals = this->Values();
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return this->SearchCatBin(value, fidx, ptrs, vals);
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}
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bst_bin_t SearchCatBin(Entry const& e) const { return SearchCatBin(e.fvalue, e.index); }
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[[nodiscard]] bst_bin_t SearchCatBin(Entry const& e) const {
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return SearchCatBin(e.fvalue, e.index);
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}
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/**
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* \brief Return numerical bin value given bin index.
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@ -590,7 +590,7 @@ class ArrayInterface {
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template <std::int32_t D, typename Fn>
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void DispatchDType(ArrayInterface<D> const array, std::int32_t device, Fn fn) {
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// Only used for cuDF at the moment.
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CHECK_EQ(array.valid.Size(), 0);
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CHECK_EQ(array.valid.Capacity(), 0);
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auto dispatch = [&](auto t) {
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using T = std::remove_const_t<decltype(t)> const;
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// Set the data size to max as we don't know the original size of a sliced array:
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@ -416,7 +416,8 @@ void CopyTensorInfoImpl(Context const& ctx, Json arr_interface, linalg::Tensor<T
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p_out->Reshape(array.shape);
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return;
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}
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CHECK(array.valid.Size() == 0) << "Meta info like label or weight can not have missing value.";
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CHECK_EQ(array.valid.Capacity(), 0)
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<< "Meta info like label or weight can not have missing value.";
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if (array.is_contiguous && array.type == ToDType<T>::kType) {
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// Handle contigious
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p_out->ModifyInplace([&](HostDeviceVector<T>* data, common::Span<size_t, D> shape) {
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@ -33,7 +33,8 @@ void CopyTensorInfoImpl(CUDAContext const* ctx, Json arr_interface, linalg::Tens
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p_out->Reshape(array.shape);
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return;
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}
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CHECK(array.valid.Size() == 0) << "Meta info like label or weight can not have missing value.";
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CHECK_EQ(array.valid.Capacity(), 0)
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<< "Meta info like label or weight can not have missing value.";
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auto ptr_device = SetDeviceToPtr(array.data);
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p_out->SetDevice(ptr_device);
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@ -5,6 +5,7 @@
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#include <thrust/iterator/transform_output_iterator.h>
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#include "../common/categorical.h"
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#include "../common/cuda_context.cuh"
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#include "../common/hist_util.cuh"
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#include "../common/random.h"
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#include "../common/transform_iterator.h" // MakeIndexTransformIter
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@ -313,7 +314,8 @@ void CopyGHistToEllpack(GHistIndexMatrix const& page, common::Span<size_t const>
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auto d_csc_indptr = dh::ToSpan(csc_indptr);
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auto bin_type = page.index.GetBinTypeSize();
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common::CompressedBufferWriter writer{page.cut.TotalBins() + 1}; // +1 for null value
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common::CompressedBufferWriter writer{page.cut.TotalBins() +
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static_cast<std::size_t>(1)}; // +1 for null value
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dh::LaunchN(row_stride * page.Size(), [=] __device__(size_t idx) mutable {
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auto ridx = idx / row_stride;
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@ -357,8 +359,10 @@ EllpackPageImpl::EllpackPageImpl(Context const* ctx, GHistIndexMatrix const& pag
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// copy gidx
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common::CompressedByteT* d_compressed_buffer = gidx_buffer.DevicePointer();
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dh::device_vector<size_t> row_ptr(page.row_ptr);
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dh::device_vector<size_t> row_ptr(page.row_ptr.size());
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auto d_row_ptr = dh::ToSpan(row_ptr);
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dh::safe_cuda(cudaMemcpyAsync(d_row_ptr.data(), page.row_ptr.data(), d_row_ptr.size_bytes(),
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cudaMemcpyHostToDevice, ctx->CUDACtx()->Stream()));
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auto accessor = this->GetDeviceAccessor(ctx->gpu_id, ft);
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auto null = accessor.NullValue();
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@ -7,9 +7,6 @@
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#ifndef XGBOOST_DATA_SPARSE_PAGE_DMATRIX_H_
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#define XGBOOST_DATA_SPARSE_PAGE_DMATRIX_H_
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#include <xgboost/data.h>
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#include <xgboost/logging.h>
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#include <algorithm>
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#include <map>
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#include <memory>
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@ -20,35 +17,33 @@
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#include "ellpack_page_source.h"
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#include "gradient_index_page_source.h"
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#include "sparse_page_source.h"
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#include "xgboost/data.h"
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#include "xgboost/logging.h"
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namespace xgboost {
|
||||
namespace data {
|
||||
namespace xgboost::data {
|
||||
/**
|
||||
* \brief DMatrix used for external memory.
|
||||
*
|
||||
* The external memory is created for controlling memory usage by splitting up data into
|
||||
* multiple batches. However that doesn't mean we will actually process exact 1 batch at
|
||||
* a time, which would be terribly slow considering that we have to loop through the
|
||||
* whole dataset for every tree split. So we use async pre-fetch and let caller to decide
|
||||
* how many batches it wants to process by returning data as shared pointer. The caller
|
||||
* can use async function to process the data or just stage those batches, making the
|
||||
* decision is out of the scope for sparse page dmatrix. These 2 optimizations might
|
||||
* defeat the purpose of splitting up dataset since if you load all the batches then the
|
||||
* memory usage is even worse than using a single batch. Essentially we need to control
|
||||
* how many batches can be in memory at the same time.
|
||||
* multiple batches. However that doesn't mean we will actually process exactly 1 batch
|
||||
* at a time, which would be terribly slow considering that we have to loop through the
|
||||
* whole dataset for every tree split. So we use async to pre-fetch pages and let the
|
||||
* caller to decide how many batches it wants to process by returning data as a shared
|
||||
* pointer. The caller can use async function to process the data or just stage those
|
||||
* batches based on its use cases. These two optimizations might defeat the purpose of
|
||||
* splitting up dataset since if you stage all the batches then the memory usage might be
|
||||
* even worse than using a single batch. As a result, we must control how many batches can
|
||||
* be in memory at any given time.
|
||||
*
|
||||
* Right now the write to the cache is sequential operation and is blocking, reading from
|
||||
* cache is async but with a hard coded limit of 4 pages as an heuristic. So by sparse
|
||||
* dmatrix itself there can be only 9 pages in main memory (might be of different types)
|
||||
* at the same time: 1 page pending for write, 4 pre-fetched sparse pages, 4 pre-fetched
|
||||
* dependent pages. If the caller stops iteration at the middle and start again, then the
|
||||
* number of pages in memory can hit 16 due to pre-fetching, but this should be a bug in
|
||||
* caller's code (XGBoost doesn't discard a large portion of data at the end, there's not
|
||||
* sampling algo that samples only the first portion of data).
|
||||
* Right now the write to the cache is a sequential operation and is blocking. Reading
|
||||
* from cache on ther other hand, is async but with a hard coded limit of 3 pages as an
|
||||
* heuristic. So by sparse dmatrix itself there can be only 7 pages in main memory (might
|
||||
* be of different types) at the same time: 1 page pending for write, 3 pre-fetched sparse
|
||||
* pages, 3 pre-fetched dependent pages.
|
||||
*
|
||||
* Of course if the caller decides to retain some batches to perform parallel processing,
|
||||
* then we might load all pages in memory, which is also considered as a bug in caller's
|
||||
* code. So if the algo supports external memory, it must be careful that queue for async
|
||||
* code. So if the algo supports external memory, it must be careful that queue for async
|
||||
* call must have an upper limit.
|
||||
*
|
||||
* Another assumption we make is that the data must be immutable so caller should never
|
||||
@ -101,7 +96,7 @@ class SparsePageDMatrix : public DMatrix {
|
||||
MetaInfo &Info() override;
|
||||
const MetaInfo &Info() const override;
|
||||
Context const *Ctx() const override { return &fmat_ctx_; }
|
||||
|
||||
// The only DMatrix implementation that returns false.
|
||||
bool SingleColBlock() const override { return false; }
|
||||
DMatrix *Slice(common::Span<int32_t const>) override {
|
||||
LOG(FATAL) << "Slicing DMatrix is not supported for external memory.";
|
||||
@ -153,6 +148,5 @@ inline std::string MakeCache(SparsePageDMatrix *ptr, std::string format, std::st
|
||||
}
|
||||
return id;
|
||||
}
|
||||
} // namespace data
|
||||
} // namespace xgboost
|
||||
} // namespace xgboost::data
|
||||
#endif // XGBOOST_DATA_SPARSE_PAGE_DMATRIX_H_
|
||||
|
||||
@ -6,39 +6,43 @@
|
||||
#define XGBOOST_DATA_SPARSE_PAGE_SOURCE_H_
|
||||
|
||||
#include <algorithm> // for min
|
||||
#include <future> // async
|
||||
#include <future> // for async
|
||||
#include <map>
|
||||
#include <memory>
|
||||
#include <string>
|
||||
#include <thread>
|
||||
#include <utility>
|
||||
#include <utility> // for pair, move
|
||||
#include <vector>
|
||||
|
||||
#include "../common/common.h"
|
||||
#include "../common/io.h" // for PrivateMmapStream, PadPageForMMAP
|
||||
#include "../common/io.h" // for PrivateMmapConstStream
|
||||
#include "../common/timer.h" // for Monitor, Timer
|
||||
#include "adapter.h"
|
||||
#include "dmlc/common.h" // OMPException
|
||||
#include "proxy_dmatrix.h"
|
||||
#include "sparse_page_writer.h"
|
||||
#include "dmlc/common.h" // for OMPException
|
||||
#include "proxy_dmatrix.h" // for DMatrixProxy
|
||||
#include "sparse_page_writer.h" // for SparsePageFormat
|
||||
#include "xgboost/base.h"
|
||||
#include "xgboost/data.h"
|
||||
|
||||
namespace xgboost::data {
|
||||
inline void TryDeleteCacheFile(const std::string& file) {
|
||||
if (std::remove(file.c_str()) != 0) {
|
||||
// Don't throw, this is called in a destructor.
|
||||
LOG(WARNING) << "Couldn't remove external memory cache file " << file
|
||||
<< "; you may want to remove it manually";
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Information about the cache including path and page offsets.
|
||||
*/
|
||||
struct Cache {
|
||||
// whether the write to the cache is complete
|
||||
bool written;
|
||||
std::string name;
|
||||
std::string format;
|
||||
// offset into binary cache file.
|
||||
std::vector<size_t> offset;
|
||||
std::vector<std::uint64_t> offset;
|
||||
|
||||
Cache(bool w, std::string n, std::string fmt)
|
||||
: written{w}, name{std::move(n)}, format{std::move(fmt)} {
|
||||
@ -50,14 +54,24 @@ struct Cache {
|
||||
return name + format;
|
||||
}
|
||||
|
||||
std::string ShardName() {
|
||||
[[nodiscard]] std::string ShardName() const {
|
||||
return ShardName(this->name, this->format);
|
||||
}
|
||||
void Push(std::size_t n_bytes) {
|
||||
offset.push_back(n_bytes);
|
||||
/**
|
||||
* @brief Record a page with size of n_bytes.
|
||||
*/
|
||||
void Push(std::size_t n_bytes) { offset.push_back(n_bytes); }
|
||||
/**
|
||||
* @brief Returns the view start and length for the i^th page.
|
||||
*/
|
||||
[[nodiscard]] auto View(std::size_t i) const {
|
||||
std::uint64_t off = offset.at(i);
|
||||
std::uint64_t len = offset.at(i + 1) - offset[i];
|
||||
return std::pair{off, len};
|
||||
}
|
||||
|
||||
// The write is completed.
|
||||
/**
|
||||
* @brief Call this once the write for the cache is complete.
|
||||
*/
|
||||
void Commit() {
|
||||
if (!written) {
|
||||
std::partial_sum(offset.begin(), offset.end(), offset.begin());
|
||||
@ -66,7 +80,7 @@ struct Cache {
|
||||
}
|
||||
};
|
||||
|
||||
// Prevents multi-threaded call.
|
||||
// Prevents multi-threaded call to `GetBatches`.
|
||||
class TryLockGuard {
|
||||
std::mutex& lock_;
|
||||
|
||||
@ -79,22 +93,25 @@ class TryLockGuard {
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
* @brief Base class for all page sources. Handles fetching, writing, and iteration.
|
||||
*/
|
||||
template <typename S>
|
||||
class SparsePageSourceImpl : public BatchIteratorImpl<S> {
|
||||
protected:
|
||||
// Prevents calling this iterator from multiple places(or threads).
|
||||
std::mutex single_threaded_;
|
||||
|
||||
// The current page.
|
||||
std::shared_ptr<S> page_;
|
||||
|
||||
bool at_end_ {false};
|
||||
float missing_;
|
||||
int nthreads_;
|
||||
std::int32_t nthreads_;
|
||||
bst_feature_t n_features_;
|
||||
|
||||
uint32_t count_{0};
|
||||
|
||||
uint32_t n_batches_ {0};
|
||||
// Index to the current page.
|
||||
std::uint32_t count_{0};
|
||||
// Total number of batches.
|
||||
std::uint32_t n_batches_{0};
|
||||
|
||||
std::shared_ptr<Cache> cache_info_;
|
||||
|
||||
@ -102,6 +119,9 @@ class SparsePageSourceImpl : public BatchIteratorImpl<S> {
|
||||
// A ring storing futures to data. Since the DMatrix iterator is forward only, so we
|
||||
// can pre-fetch data in a ring.
|
||||
std::unique_ptr<Ring> ring_{new Ring};
|
||||
// Catching exception in pre-fetch threads to prevent segfault. Not always work though,
|
||||
// OOM error can be delayed due to lazy commit. On the bright side, if mmap is used then
|
||||
// OOM error should be rare.
|
||||
dmlc::OMPException exec_;
|
||||
common::Monitor monitor_;
|
||||
|
||||
@ -123,7 +143,6 @@ class SparsePageSourceImpl : public BatchIteratorImpl<S> {
|
||||
|
||||
exec_.Rethrow();
|
||||
|
||||
monitor_.Start("launch");
|
||||
for (std::size_t i = 0; i < n_prefetch_batches; ++i, ++fetch_it) {
|
||||
fetch_it %= n_batches_; // ring
|
||||
if (ring_->at(fetch_it).valid()) {
|
||||
@ -134,33 +153,25 @@ class SparsePageSourceImpl : public BatchIteratorImpl<S> {
|
||||
ring_->at(fetch_it) = std::async(std::launch::async, [fetch_it, self, this]() {
|
||||
auto page = std::make_shared<S>();
|
||||
this->exec_.Run([&] {
|
||||
common::Timer timer;
|
||||
timer.Start();
|
||||
std::unique_ptr<SparsePageFormat<S>> fmt{CreatePageFormat<S>("raw")};
|
||||
auto n = self->cache_info_->ShardName();
|
||||
|
||||
std::uint64_t offset = self->cache_info_->offset.at(fetch_it);
|
||||
std::uint64_t length = self->cache_info_->offset.at(fetch_it + 1) - offset;
|
||||
|
||||
auto fi = std::make_unique<common::PrivateMmapConstStream>(n, offset, length);
|
||||
auto name = self->cache_info_->ShardName();
|
||||
auto [offset, length] = self->cache_info_->View(fetch_it);
|
||||
auto fi = std::make_unique<common::PrivateMmapConstStream>(name, offset, length);
|
||||
CHECK(fmt->Read(page.get(), fi.get()));
|
||||
timer.Stop();
|
||||
|
||||
LOG(INFO) << "Read a page `" << typeid(S).name() << "` in " << timer.ElapsedSeconds()
|
||||
<< " seconds.";
|
||||
});
|
||||
return page;
|
||||
});
|
||||
}
|
||||
monitor_.Stop("launch");
|
||||
|
||||
CHECK_EQ(std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }),
|
||||
n_prefetch_batches)
|
||||
<< "Sparse DMatrix assumes forward iteration.";
|
||||
|
||||
monitor_.Start("Wait");
|
||||
page_ = (*ring_)[count_].get();
|
||||
monitor_.Stop("Wait");
|
||||
CHECK(!(*ring_)[count_].valid());
|
||||
monitor_.Stop("Wait");
|
||||
|
||||
exec_.Rethrow();
|
||||
|
||||
return true;
|
||||
@ -183,6 +194,7 @@ class SparsePageSourceImpl : public BatchIteratorImpl<S> {
|
||||
auto bytes = fmt->Write(*page_, fo.get());
|
||||
|
||||
timer.Stop();
|
||||
// Not entirely accurate, the kernels doesn't have to flush the data.
|
||||
LOG(INFO) << static_cast<double>(bytes) / 1024.0 / 1024.0 << " MB written in "
|
||||
<< timer.ElapsedSeconds() << " seconds.";
|
||||
cache_info_->Push(bytes);
|
||||
@ -204,6 +216,7 @@ class SparsePageSourceImpl : public BatchIteratorImpl<S> {
|
||||
SparsePageSourceImpl(SparsePageSourceImpl const &that) = delete;
|
||||
|
||||
~SparsePageSourceImpl() override {
|
||||
// Don't orphan the threads.
|
||||
for (auto& fu : *ring_) {
|
||||
if (fu.valid()) {
|
||||
fu.get();
|
||||
@ -211,18 +224,18 @@ class SparsePageSourceImpl : public BatchIteratorImpl<S> {
|
||||
}
|
||||
}
|
||||
|
||||
uint32_t Iter() const { return count_; }
|
||||
[[nodiscard]] uint32_t Iter() const { return count_; }
|
||||
|
||||
const S &operator*() const override {
|
||||
CHECK(page_);
|
||||
return *page_;
|
||||
}
|
||||
|
||||
std::shared_ptr<S const> Page() const override {
|
||||
[[nodiscard]] std::shared_ptr<S const> Page() const override {
|
||||
return page_;
|
||||
}
|
||||
|
||||
bool AtEnd() const override {
|
||||
[[nodiscard]] bool AtEnd() const override {
|
||||
return at_end_;
|
||||
}
|
||||
|
||||
@ -230,20 +243,23 @@ class SparsePageSourceImpl : public BatchIteratorImpl<S> {
|
||||
TryLockGuard guard{single_threaded_};
|
||||
at_end_ = false;
|
||||
count_ = 0;
|
||||
// Pre-fetch for the next round of iterations.
|
||||
this->Fetch();
|
||||
}
|
||||
};
|
||||
|
||||
#if defined(XGBOOST_USE_CUDA)
|
||||
// Push data from CUDA.
|
||||
void DevicePush(DMatrixProxy* proxy, float missing, SparsePage* page);
|
||||
#else
|
||||
inline void DevicePush(DMatrixProxy*, float, SparsePage*) { common::AssertGPUSupport(); }
|
||||
#endif
|
||||
|
||||
class SparsePageSource : public SparsePageSourceImpl<SparsePage> {
|
||||
// This is the source from the user.
|
||||
DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext> iter_;
|
||||
DMatrixProxy* proxy_;
|
||||
size_t base_row_id_ {0};
|
||||
std::size_t base_row_id_{0};
|
||||
|
||||
void Fetch() final {
|
||||
page_ = std::make_shared<SparsePage>();
|
||||
|
||||
@ -439,7 +439,7 @@ struct ShapSplitCondition {
|
||||
if (isnan(x)) {
|
||||
return is_missing_branch;
|
||||
}
|
||||
if (categories.Size() != 0) {
|
||||
if (categories.Capacity() != 0) {
|
||||
auto cat = static_cast<uint32_t>(x);
|
||||
return categories.Check(cat);
|
||||
} else {
|
||||
@ -454,7 +454,7 @@ struct ShapSplitCondition {
|
||||
if (l.Data() == r.Data()) {
|
||||
return l;
|
||||
}
|
||||
if (l.Size() > r.Size()) {
|
||||
if (l.Capacity() > r.Capacity()) {
|
||||
thrust::swap(l, r);
|
||||
}
|
||||
for (size_t i = 0; i < r.Bits().size(); ++i) {
|
||||
@ -466,7 +466,7 @@ struct ShapSplitCondition {
|
||||
// Combine two split conditions on the same feature
|
||||
XGBOOST_DEVICE void Merge(ShapSplitCondition other) {
|
||||
// Combine duplicate features
|
||||
if (categories.Size() != 0 || other.categories.Size() != 0) {
|
||||
if (categories.Capacity() != 0 || other.categories.Capacity() != 0) {
|
||||
categories = Intersect(categories, other.categories);
|
||||
} else {
|
||||
feature_lower_bound = max(feature_lower_bound, other.feature_lower_bound);
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
/*!
|
||||
* Copyright 2019 XGBoost contributors
|
||||
/**
|
||||
* Copyright 2019-2023, XGBoost contributors
|
||||
*/
|
||||
#include <thrust/copy.h>
|
||||
#include <thrust/device_vector.h>
|
||||
@ -140,20 +140,20 @@ void FeatureInteractionConstraintDevice::Reset() {
|
||||
__global__ void ClearBuffersKernel(
|
||||
LBitField64 result_buffer_output, LBitField64 result_buffer_input) {
|
||||
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (tid < result_buffer_output.Size()) {
|
||||
if (tid < result_buffer_output.Capacity()) {
|
||||
result_buffer_output.Clear(tid);
|
||||
}
|
||||
if (tid < result_buffer_input.Size()) {
|
||||
if (tid < result_buffer_input.Capacity()) {
|
||||
result_buffer_input.Clear(tid);
|
||||
}
|
||||
}
|
||||
|
||||
void FeatureInteractionConstraintDevice::ClearBuffers() {
|
||||
CHECK_EQ(output_buffer_bits_.Size(), input_buffer_bits_.Size());
|
||||
CHECK_LE(feature_buffer_.Size(), output_buffer_bits_.Size());
|
||||
CHECK_EQ(output_buffer_bits_.Capacity(), input_buffer_bits_.Capacity());
|
||||
CHECK_LE(feature_buffer_.Capacity(), output_buffer_bits_.Capacity());
|
||||
uint32_t constexpr kBlockThreads = 256;
|
||||
auto const n_grids = static_cast<uint32_t>(
|
||||
common::DivRoundUp(input_buffer_bits_.Size(), kBlockThreads));
|
||||
common::DivRoundUp(input_buffer_bits_.Capacity(), kBlockThreads));
|
||||
dh::LaunchKernel {n_grids, kBlockThreads} (
|
||||
ClearBuffersKernel,
|
||||
output_buffer_bits_, input_buffer_bits_);
|
||||
@ -207,11 +207,11 @@ common::Span<bst_feature_t> FeatureInteractionConstraintDevice::Query(
|
||||
ClearBuffers();
|
||||
|
||||
LBitField64 node_constraints = s_node_constraints_[nid];
|
||||
CHECK_EQ(input_buffer_bits_.Size(), output_buffer_bits_.Size());
|
||||
CHECK_EQ(input_buffer_bits_.Capacity(), output_buffer_bits_.Capacity());
|
||||
|
||||
uint32_t constexpr kBlockThreads = 256;
|
||||
auto n_grids = static_cast<uint32_t>(
|
||||
common::DivRoundUp(output_buffer_bits_.Size(), kBlockThreads));
|
||||
common::DivRoundUp(output_buffer_bits_.Capacity(), kBlockThreads));
|
||||
dh::LaunchKernel {n_grids, kBlockThreads} (
|
||||
SetInputBufferKernel,
|
||||
feature_list, input_buffer_bits_);
|
||||
@ -274,13 +274,13 @@ __global__ void InteractionConstraintSplitKernel(LBitField64 feature,
|
||||
LBitField64 left,
|
||||
LBitField64 right) {
|
||||
auto tid = threadIdx.x + blockDim.x * blockIdx.x;
|
||||
if (tid > node.Size()) {
|
||||
if (tid > node.Capacity()) {
|
||||
return;
|
||||
}
|
||||
// enable constraints from feature
|
||||
node |= feature;
|
||||
// clear the buffer after use
|
||||
if (tid < feature.Size()) {
|
||||
if (tid < feature.Capacity()) {
|
||||
feature.Clear(tid);
|
||||
}
|
||||
|
||||
@ -323,7 +323,7 @@ void FeatureInteractionConstraintDevice::Split(
|
||||
s_sets_, s_sets_ptr_);
|
||||
|
||||
uint32_t constexpr kBlockThreads = 256;
|
||||
auto n_grids = static_cast<uint32_t>(common::DivRoundUp(node.Size(), kBlockThreads));
|
||||
auto n_grids = static_cast<uint32_t>(common::DivRoundUp(node.Capacity(), kBlockThreads));
|
||||
|
||||
dh::LaunchKernel {n_grids, kBlockThreads} (
|
||||
InteractionConstraintSplitKernel,
|
||||
|
||||
@ -213,7 +213,7 @@ std::vector<bst_cat_t> GetSplitCategories(RegTree const &tree, int32_t nidx) {
|
||||
auto split = common::KCatBitField{csr.categories.subspan(seg.beg, seg.size)};
|
||||
|
||||
std::vector<bst_cat_t> cats;
|
||||
for (size_t i = 0; i < split.Size(); ++i) {
|
||||
for (size_t i = 0; i < split.Capacity(); ++i) {
|
||||
if (split.Check(i)) {
|
||||
cats.push_back(static_cast<bst_cat_t>(i));
|
||||
}
|
||||
@ -1004,7 +1004,7 @@ void RegTree::SaveCategoricalSplit(Json* p_out) const {
|
||||
auto segment = split_categories_segments_[i];
|
||||
auto node_categories = this->GetSplitCategories().subspan(segment.beg, segment.size);
|
||||
common::KCatBitField const cat_bits(node_categories);
|
||||
for (size_t i = 0; i < cat_bits.Size(); ++i) {
|
||||
for (size_t i = 0; i < cat_bits.Capacity(); ++i) {
|
||||
if (cat_bits.Check(i)) {
|
||||
categories.GetArray().emplace_back(i);
|
||||
}
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
/*!
|
||||
* Copyright 2019 XGBoost contributors
|
||||
/**
|
||||
* Copyright 2019-2023, XGBoost contributors
|
||||
*/
|
||||
#include <gtest/gtest.h>
|
||||
#include "../../../src/common/bitfield.h"
|
||||
@ -14,7 +14,7 @@ TEST(BitField, Check) {
|
||||
static_cast<typename common::Span<LBitField64::value_type>::index_type>(
|
||||
storage.size())});
|
||||
size_t true_bit = 190;
|
||||
for (size_t i = true_bit + 1; i < bits.Size(); ++i) {
|
||||
for (size_t i = true_bit + 1; i < bits.Capacity(); ++i) {
|
||||
ASSERT_FALSE(bits.Check(i));
|
||||
}
|
||||
ASSERT_TRUE(bits.Check(true_bit));
|
||||
@ -34,7 +34,7 @@ TEST(BitField, Check) {
|
||||
ASSERT_FALSE(bits.Check(i));
|
||||
}
|
||||
ASSERT_TRUE(bits.Check(true_bit));
|
||||
for (size_t i = true_bit + 1; i < bits.Size(); ++i) {
|
||||
for (size_t i = true_bit + 1; i < bits.Capacity(); ++i) {
|
||||
ASSERT_FALSE(bits.Check(i));
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
/*!
|
||||
* Copyright 2019 XGBoost contributors
|
||||
/**
|
||||
* Copyright 2019-2023, XGBoost contributors
|
||||
*/
|
||||
#include <gtest/gtest.h>
|
||||
#include <thrust/copy.h>
|
||||
@ -12,7 +12,7 @@ namespace xgboost {
|
||||
|
||||
__global__ void TestSetKernel(LBitField64 bits) {
|
||||
auto tid = threadIdx.x + blockIdx.x * blockDim.x;
|
||||
if (tid < bits.Size()) {
|
||||
if (tid < bits.Capacity()) {
|
||||
bits.Set(tid);
|
||||
}
|
||||
}
|
||||
@ -36,20 +36,16 @@ TEST(BitField, GPUSet) {
|
||||
|
||||
std::vector<LBitField64::value_type> h_storage(storage.size());
|
||||
thrust::copy(storage.begin(), storage.end(), h_storage.begin());
|
||||
|
||||
LBitField64 outputs {
|
||||
common::Span<LBitField64::value_type>{h_storage.data(),
|
||||
h_storage.data() + h_storage.size()}};
|
||||
LBitField64 outputs{
|
||||
common::Span<LBitField64::value_type>{h_storage.data(), h_storage.data() + h_storage.size()}};
|
||||
for (size_t i = 0; i < kBits; ++i) {
|
||||
ASSERT_TRUE(outputs.Check(i));
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void TestOrKernel(LBitField64 lhs, LBitField64 rhs) {
|
||||
lhs |= rhs;
|
||||
}
|
||||
|
||||
TEST(BitField, GPUAnd) {
|
||||
namespace {
|
||||
template <bool is_and, typename Op>
|
||||
void TestGPULogic(Op op) {
|
||||
uint32_t constexpr kBits = 128;
|
||||
dh::device_vector<LBitField64::value_type> lhs_storage(kBits);
|
||||
dh::device_vector<LBitField64::value_type> rhs_storage(kBits);
|
||||
@ -57,13 +53,32 @@ TEST(BitField, GPUAnd) {
|
||||
auto rhs = LBitField64(dh::ToSpan(rhs_storage));
|
||||
thrust::fill(lhs_storage.begin(), lhs_storage.end(), 0UL);
|
||||
thrust::fill(rhs_storage.begin(), rhs_storage.end(), ~static_cast<LBitField64::value_type>(0UL));
|
||||
TestOrKernel<<<1, kBits>>>(lhs, rhs);
|
||||
dh::LaunchN(kBits, [=] __device__(auto) mutable { op(lhs, rhs); });
|
||||
|
||||
std::vector<LBitField64::value_type> h_storage(lhs_storage.size());
|
||||
thrust::copy(lhs_storage.begin(), lhs_storage.end(), h_storage.begin());
|
||||
LBitField64 outputs {{h_storage.data(), h_storage.data() + h_storage.size()}};
|
||||
for (size_t i = 0; i < kBits; ++i) {
|
||||
ASSERT_TRUE(outputs.Check(i));
|
||||
LBitField64 outputs{{h_storage.data(), h_storage.data() + h_storage.size()}};
|
||||
if (is_and) {
|
||||
for (size_t i = 0; i < kBits; ++i) {
|
||||
ASSERT_FALSE(outputs.Check(i));
|
||||
}
|
||||
} else {
|
||||
for (size_t i = 0; i < kBits; ++i) {
|
||||
ASSERT_TRUE(outputs.Check(i));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void TestGPUAnd() {
|
||||
TestGPULogic<true>([] XGBOOST_DEVICE(LBitField64 & lhs, LBitField64 const& rhs) { lhs &= rhs; });
|
||||
}
|
||||
|
||||
void TestGPUOr() {
|
||||
TestGPULogic<false>([] XGBOOST_DEVICE(LBitField64 & lhs, LBitField64 const& rhs) { lhs |= rhs; });
|
||||
}
|
||||
} // namespace
|
||||
|
||||
TEST(BitField, GPUAnd) { TestGPUAnd(); }
|
||||
|
||||
TEST(BitField, GPUOr) { TestGPUOr(); }
|
||||
} // namespace xgboost
|
||||
@ -83,7 +83,9 @@ template <typename BinIdxType>
|
||||
void CheckColumWithMissingValue(const DenseColumnIter<BinIdxType, true>& col,
|
||||
const GHistIndexMatrix& gmat) {
|
||||
for (auto i = 0ull; i < col.Size(); i++) {
|
||||
if (col.IsMissing(i)) continue;
|
||||
if (col.IsMissing(i)) {
|
||||
continue;
|
||||
}
|
||||
EXPECT_EQ(gmat.index[gmat.row_ptr[i]], col.GetGlobalBinIdx(i));
|
||||
}
|
||||
}
|
||||
|
||||
@ -285,8 +285,6 @@ TEST(GpuHist, PartitionTwoNodes) {
|
||||
dh::ToSpan(feature_histogram_b)};
|
||||
thrust::device_vector<GPUExpandEntry> results(2);
|
||||
evaluator.EvaluateSplits({0, 1}, 1, dh::ToSpan(inputs), shared_inputs, dh::ToSpan(results));
|
||||
GPUExpandEntry result_a = results[0];
|
||||
GPUExpandEntry result_b = results[1];
|
||||
EXPECT_EQ(std::bitset<32>(evaluator.GetHostNodeCats(0)[0]),
|
||||
std::bitset<32>("10000000000000000000000000000000"));
|
||||
EXPECT_EQ(std::bitset<32>(evaluator.GetHostNodeCats(1)[0]),
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
/*!
|
||||
* Copyright 2019 XGBoost contributors
|
||||
/**
|
||||
* Copyright 2019-2023, XGBoost contributors
|
||||
*/
|
||||
#include <gtest/gtest.h>
|
||||
#include <thrust/copy.h>
|
||||
@ -53,7 +53,7 @@ void CompareBitField(LBitField64 d_field, std::set<uint32_t> positions) {
|
||||
LBitField64 h_field{ {h_field_storage.data(),
|
||||
h_field_storage.data() + h_field_storage.size()} };
|
||||
|
||||
for (size_t i = 0; i < h_field.Size(); ++i) {
|
||||
for (size_t i = 0; i < h_field.Capacity(); ++i) {
|
||||
if (positions.find(i) != positions.cend()) {
|
||||
ASSERT_TRUE(h_field.Check(i));
|
||||
} else {
|
||||
@ -82,7 +82,7 @@ TEST(GPUFeatureInteractionConstraint, Init) {
|
||||
{h_node_storage.data(), h_node_storage.data() + h_node_storage.size()}
|
||||
};
|
||||
// no feature is attached to node.
|
||||
for (size_t i = 0; i < h_node.Size(); ++i) {
|
||||
for (size_t i = 0; i < h_node.Capacity(); ++i) {
|
||||
ASSERT_FALSE(h_node.Check(i));
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user