1148 lines
39 KiB
C++
1148 lines
39 KiB
C++
/**
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* Copyright 2017-2023 XGBoost contributors
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*/
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#pragma once
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#include <thrust/binary_search.h> // thrust::upper_bound
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#include <thrust/device_malloc_allocator.h>
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#include <thrust/device_ptr.h>
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#include <thrust/device_vector.h>
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#include <thrust/execution_policy.h> // thrust::seq
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#include <thrust/gather.h> // gather
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#include <thrust/iterator/discard_iterator.h>
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#include <thrust/iterator/transform_output_iterator.h> // make_transform_output_iterator
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#include <thrust/logical.h>
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#include <thrust/sequence.h>
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#include <thrust/sort.h>
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#include <thrust/system/hip/error.h>
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#include <thrust/system_error.h>
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#include <thrust/transform_scan.h>
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#include <thrust/unique.h>
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#include <hip/hip_runtime.h>
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#include <thrust/system/hip/execution_policy.h>
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#include <thrust/system/hip/detail/get_value.h>
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#include <algorithm>
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#include <chrono>
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#include <cstddef> // for size_t
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#include <hipcub/hipcub.hpp>
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#include <hipcub/util_allocator.hpp>
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#include <rocprim/rocprim.hpp>
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#include <numeric>
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#include <sstream>
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#include <string>
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#include <tuple>
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#include <vector>
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#include "cuda_to_hip.h"
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#include "../collective/communicator-inl.h"
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#include "common.h"
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#include "xgboost/global_config.h"
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#include "xgboost/host_device_vector.h"
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#include "xgboost/logging.h"
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#include "xgboost/span.h"
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#if defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
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#include "rmm/mr/device/per_device_resource.hpp"
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#include "rmm/mr/device/thrust_allocator_adaptor.hpp"
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#include "rmm/version_config.hpp"
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#if !defined(RMM_VERSION_MAJOR) || !defined(RMM_VERSION_MINOR)
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#error "Please use RMM version 0.18 or later"
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#elif RMM_VERSION_MAJOR == 0 && RMM_VERSION_MINOR < 18
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#error "Please use RMM version 0.18 or later"
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#endif // !defined(RMM_VERSION_MAJOR) || !defined(RMM_VERSION_MINOR)
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#endif // defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
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namespace dh {
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// FIXME(jiamingy): Remove this once we get rid of cub submodule.
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constexpr bool BuildWithCUDACub() {
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#if defined(THRUST_IGNORE_CUB_VERSION_CHECK) && THRUST_IGNORE_CUB_VERSION_CHECK == 1
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return false;
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#else
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return true;
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#endif // defined(THRUST_IGNORE_CUB_VERSION_CHECK) && THRUST_IGNORE_CUB_VERSION_CHECK == 1
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}
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namespace detail {
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template <size_t size>
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struct AtomicDispatcher;
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template <>
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struct AtomicDispatcher<sizeof(uint32_t)> {
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using Type = unsigned int; // NOLINT
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static_assert(sizeof(Type) == sizeof(uint32_t), "Unsigned should be of size 32 bits.");
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};
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template <>
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struct AtomicDispatcher<sizeof(uint64_t)> {
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using Type = unsigned long long; // NOLINT
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static_assert(sizeof(Type) == sizeof(uint64_t), "Unsigned long long should be of size 64 bits.");
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};
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} // namespace detail
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} // namespace dh
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// atomicAdd is not defined for size_t.
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template <typename T = size_t,
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std::enable_if_t<std::is_same<size_t, T>::value &&
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!std::is_same<size_t, unsigned long long>::value> * = // NOLINT
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nullptr>
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XGBOOST_DEV_INLINE T atomicAdd(T *addr, T v) { // NOLINT
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using Type = typename dh::detail::AtomicDispatcher<sizeof(T)>::Type;
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Type ret = ::atomicAdd(reinterpret_cast<Type *>(addr), static_cast<Type>(v));
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return static_cast<T>(ret);
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}
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namespace dh {
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inline int32_t CudaGetPointerDevice(void const *ptr) {
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int32_t device = -1;
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hipPointerAttribute_t attr;
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dh::safe_cuda(hipPointerGetAttributes(&attr, ptr));
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device = attr.device;
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return device;
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}
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inline size_t AvailableMemory(int device_idx) {
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size_t device_free = 0;
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size_t device_total = 0;
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safe_cuda(hipSetDevice(device_idx));
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dh::safe_cuda(hipMemGetInfo(&device_free, &device_total));
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return device_free;
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}
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inline int32_t CurrentDevice() {
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int32_t device = 0;
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safe_cuda(hipGetDevice(&device));
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return device;
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}
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inline size_t TotalMemory(int device_idx) {
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size_t device_free = 0;
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size_t device_total = 0;
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safe_cuda(hipSetDevice(device_idx));
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dh::safe_cuda(hipMemGetInfo(&device_free, &device_total));
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return device_total;
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}
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/**
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* \fn inline int MaxSharedMemory(int device_idx)
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*
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* \brief Maximum shared memory per block on this device.
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*
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* \param device_idx Zero-based index of the device.
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*/
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inline size_t MaxSharedMemory(int device_idx) {
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int max_shared_memory = 0;
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dh::safe_cuda(hipDeviceGetAttribute
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(&max_shared_memory, hipDeviceAttributeMaxSharedMemoryPerBlock,
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device_idx));
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return static_cast<std::size_t>(max_shared_memory);
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}
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/**
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* \fn inline int MaxSharedMemoryOptin(int device_idx)
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*
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* \brief Maximum dynamic shared memory per thread block on this device
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that can be opted into when using hipFuncSetAttribute().
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*
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* \param device_idx Zero-based index of the device.
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*/
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inline size_t MaxSharedMemoryOptin(int device_idx) {
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int max_shared_memory = 0;
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#if 0 /* CUDA Only */
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dh::safe_cuda(hipDeviceGetAttribute
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(&max_shared_memory, hipDeviceAttributeSharedMemPerBlockOptin,
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device_idx));
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#endif
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return static_cast<std::size_t>(max_shared_memory);
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}
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inline void CheckComputeCapability() {
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for (int d_idx = 0; d_idx < xgboost::common::AllVisibleGPUs(); ++d_idx) {
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hipDeviceProp_t prop;
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safe_cuda(hipGetDeviceProperties(&prop, d_idx));
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std::ostringstream oss;
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oss << "CUDA Capability Major/Minor version number: " << prop.major << "."
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<< prop.minor << " is insufficient. Need >=3.5";
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int failed = prop.major < 3 || (prop.major == 3 && prop.minor < 5);
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if (failed) LOG(WARNING) << oss.str() << " for device: " << d_idx;
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}
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}
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XGBOOST_DEV_INLINE void AtomicOrByte(unsigned int *__restrict__ buffer,
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size_t ibyte, unsigned char b) {
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atomicOr(&buffer[ibyte / sizeof(unsigned int)],
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static_cast<unsigned int>(b)
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<< (ibyte % (sizeof(unsigned int)) * 8));
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}
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template <typename T>
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__device__ xgboost::common::Range GridStrideRange(T begin, T end) {
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begin += blockDim.x * blockIdx.x + threadIdx.x;
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xgboost::common::Range r(begin, end);
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r.Step(gridDim.x * blockDim.x);
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return r;
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}
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template <typename T>
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__device__ xgboost::common::Range BlockStrideRange(T begin, T end) {
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begin += threadIdx.x;
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xgboost::common::Range r(begin, end);
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r.Step(blockDim.x);
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return r;
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}
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// Threadblock iterates over range, filling with value. Requires all threads in
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// block to be active.
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template <typename IterT, typename ValueT>
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__device__ void BlockFill(IterT begin, size_t n, ValueT value) {
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for (auto i : BlockStrideRange(static_cast<size_t>(0), n)) {
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begin[i] = value;
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}
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}
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/*
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* Kernel launcher
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*/
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template <typename L>
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__global__ void LaunchNKernel(size_t begin, size_t end, L lambda) {
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for (auto i : GridStrideRange(begin, end)) {
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lambda(i);
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}
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}
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template <typename L>
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__global__ void LaunchNKernel(int device_idx, size_t begin, size_t end,
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L lambda) {
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for (auto i : GridStrideRange(begin, end)) {
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lambda(i, device_idx);
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}
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}
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/* \brief A wrapper around kernel launching syntax, used to guard against empty input.
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*
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* - nvcc fails to deduce template argument when kernel is a template accepting __device__
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* function as argument. Hence functions like `LaunchN` cannot use this wrapper.
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*
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* - With c++ initialization list `{}` syntax, you are forced to comply with the CUDA type
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* specification.
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*/
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class LaunchKernel {
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size_t shmem_size_;
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hipStream_t stream_;
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dim3 grids_;
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dim3 blocks_;
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public:
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LaunchKernel(uint32_t _grids, uint32_t _blk, size_t _shmem=0, hipStream_t _s=nullptr) :
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grids_{_grids, 1, 1}, blocks_{_blk, 1, 1}, shmem_size_{_shmem}, stream_{_s} {}
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LaunchKernel(dim3 _grids, dim3 _blk, size_t _shmem=0, hipStream_t _s=nullptr) :
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grids_{_grids}, blocks_{_blk}, shmem_size_{_shmem}, stream_{_s} {}
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template <typename K, typename... Args>
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void operator()(K kernel, Args... args) {
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if (XGBOOST_EXPECT(grids_.x * grids_.y * grids_.z == 0, false)) {
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LOG(DEBUG) << "Skipping empty CUDA kernel.";
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return;
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}
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kernel<<<grids_, blocks_, shmem_size_, stream_>>>(args...); // NOLINT
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}
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};
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template <int ITEMS_PER_THREAD = 8, int BLOCK_THREADS = 256, typename L>
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inline void LaunchN(size_t n, hipStream_t stream, L lambda) {
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if (n == 0) {
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return;
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}
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const int GRID_SIZE =
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static_cast<int>(xgboost::common::DivRoundUp(n, ITEMS_PER_THREAD * BLOCK_THREADS));
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LaunchNKernel<<<GRID_SIZE, BLOCK_THREADS, 0, stream>>>( // NOLINT
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static_cast<size_t>(0), n, lambda);
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}
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// Default stream version
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template <int ITEMS_PER_THREAD = 8, int BLOCK_THREADS = 256, typename L>
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inline void LaunchN(size_t n, L lambda) {
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LaunchN<ITEMS_PER_THREAD, BLOCK_THREADS>(n, nullptr, lambda);
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}
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template <typename Container>
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void Iota(Container array, cudaStream_t stream) {
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LaunchN(array.size(), stream, [=] __device__(size_t i) { array[i] = i; });
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}
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namespace detail {
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/** \brief Keeps track of global device memory allocations. Thread safe.*/
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class MemoryLogger {
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// Information for a single device
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struct DeviceStats {
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size_t currently_allocated_bytes{ 0 };
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size_t peak_allocated_bytes{ 0 };
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size_t num_allocations{ 0 };
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size_t num_deallocations{ 0 };
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std::map<void *, size_t> device_allocations;
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void RegisterAllocation(void *ptr, size_t n) {
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device_allocations[ptr] = n;
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currently_allocated_bytes += n;
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peak_allocated_bytes =
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std::max(peak_allocated_bytes, currently_allocated_bytes);
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num_allocations++;
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CHECK_GT(num_allocations, num_deallocations);
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}
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void RegisterDeallocation(void *ptr, size_t n, int current_device) {
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auto itr = device_allocations.find(ptr);
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if (itr == device_allocations.end()) {
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LOG(WARNING) << "Attempting to deallocate " << n << " bytes on device "
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<< current_device << " that was never allocated ";
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}
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num_deallocations++;
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CHECK_LE(num_deallocations, num_allocations);
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currently_allocated_bytes -= itr->second;
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device_allocations.erase(itr);
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}
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};
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DeviceStats stats_;
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std::mutex mutex_;
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public:
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void RegisterAllocation(void *ptr, size_t n) {
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if (!xgboost::ConsoleLogger::ShouldLog(xgboost::ConsoleLogger::LV::kDebug)) {
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return;
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}
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std::lock_guard<std::mutex> guard(mutex_);
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int current_device;
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safe_cuda(hipGetDevice(¤t_device));
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stats_.RegisterAllocation(ptr, n);
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}
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void RegisterDeallocation(void *ptr, size_t n) {
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if (!xgboost::ConsoleLogger::ShouldLog(xgboost::ConsoleLogger::LV::kDebug)) {
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return;
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}
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std::lock_guard<std::mutex> guard(mutex_);
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int current_device;
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safe_cuda(hipGetDevice(¤t_device));
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stats_.RegisterDeallocation(ptr, n, current_device);
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}
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size_t PeakMemory() const {
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return stats_.peak_allocated_bytes;
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}
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size_t CurrentlyAllocatedBytes() const {
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return stats_.currently_allocated_bytes;
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}
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void Clear()
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{
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stats_ = DeviceStats();
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}
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void Log() {
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if (!xgboost::ConsoleLogger::ShouldLog(xgboost::ConsoleLogger::LV::kDebug)) {
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return;
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}
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std::lock_guard<std::mutex> guard(mutex_);
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int current_device;
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safe_cuda(hipGetDevice(¤t_device));
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LOG(CONSOLE) << "======== Device " << current_device << " Memory Allocations: "
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<< " ========";
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LOG(CONSOLE) << "Peak memory usage: "
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<< stats_.peak_allocated_bytes / 1048576 << "MiB";
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LOG(CONSOLE) << "Number of allocations: " << stats_.num_allocations;
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}
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};
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} // namespace detail
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inline detail::MemoryLogger &GlobalMemoryLogger() {
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static detail::MemoryLogger memory_logger;
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return memory_logger;
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}
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// dh::DebugSyncDevice(__FILE__, __LINE__);
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inline void DebugSyncDevice(std::string file="", int32_t line = -1) {
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if (file != "" && line != -1) {
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auto rank = xgboost::collective::GetRank();
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LOG(DEBUG) << "R:" << rank << ": " << file << ":" << line;
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}
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safe_cuda(hipDeviceSynchronize());
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safe_cuda(hipGetLastError());
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}
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namespace detail {
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#if defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
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template <typename T>
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using XGBBaseDeviceAllocator = rmm::mr::thrust_allocator<T>;
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#else // defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
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template <typename T>
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using XGBBaseDeviceAllocator = thrust::device_malloc_allocator<T>;
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#endif // defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
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inline void ThrowOOMError(std::string const& err, size_t bytes) {
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auto device = CurrentDevice();
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auto rank = xgboost::collective::GetRank();
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std::stringstream ss;
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ss << "Memory allocation error on worker " << rank << ": " << err << "\n"
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<< "- Free memory: " << AvailableMemory(device) << "\n"
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<< "- Requested memory: " << bytes << std::endl;
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LOG(FATAL) << ss.str();
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}
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/**
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* \brief Default memory allocator, uses hipMalloc/Free and logs allocations if verbose.
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*/
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template <class T>
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struct XGBDefaultDeviceAllocatorImpl : XGBBaseDeviceAllocator<T> {
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using SuperT = XGBBaseDeviceAllocator<T>;
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using pointer = thrust::device_ptr<T>;
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template<typename U>
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struct rebind {
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using other = XGBDefaultDeviceAllocatorImpl<U>;
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};
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pointer allocate(size_t n) {
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pointer ptr;
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try {
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ptr = SuperT::allocate(n);
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dh::safe_cuda(hipGetLastError());
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} catch (const std::exception &e) {
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ThrowOOMError(e.what(), n * sizeof(T));
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}
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std::cerr << "XGBDefaultDeviceAllocatorImpl: Allocated " << n * sizeof(T)
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<< " bytes at " << ptr.get() << std::endl;
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GlobalMemoryLogger().RegisterAllocation(ptr.get(), n * sizeof(T));
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return ptr;
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}
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void deallocate(pointer ptr, size_t n) {
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std::cerr << "XGBDefaultDeviceAllocatorImpl: Deallocating " << n * sizeof(T)
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<< " bytes at " << ptr.get() << std::endl;
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GlobalMemoryLogger().RegisterDeallocation(ptr.get(), n * sizeof(T));
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SuperT::deallocate(ptr, n);
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}
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#if defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
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XGBDefaultDeviceAllocatorImpl() : SuperT(rmm::cuda_stream_default, rmm::mr::get_current_device_resource()) {}
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#endif
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};
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/**
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* \brief Caching memory allocator, uses hipcub::CachingDeviceAllocator as a back-end, unless
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* RMM pool allocator is enabled. Does not initialise memory on construction.
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*/
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template <class T>
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struct XGBCachingDeviceAllocatorImpl : XGBBaseDeviceAllocator<T> {
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using SuperT = XGBBaseDeviceAllocator<T>;
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using pointer = thrust::device_ptr<T>; // NOLINT
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template<typename U>
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struct rebind // NOLINT
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{
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using other = XGBCachingDeviceAllocatorImpl<U>; // NOLINT
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};
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hipcub::CachingDeviceAllocator& GetGlobalCachingAllocator() {
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// Configure allocator with maximum cached bin size of ~1GB and no limit on
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// maximum cached bytes
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thread_local std::unique_ptr<hipcub::CachingDeviceAllocator> allocator{
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std::make_unique<hipcub::CachingDeviceAllocator>(2, 9, 29)};
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return *allocator;
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}
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pointer allocate(size_t n) {
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pointer thrust_ptr;
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if (use_cub_allocator_) {
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T* raw_ptr{nullptr};
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auto errc = GetGlobalCachingAllocator().DeviceAllocate(reinterpret_cast<void **>(&raw_ptr), n * sizeof(T));
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if (errc != hipSuccess) {
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ThrowOOMError("Caching allocator", n * sizeof(T));
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}
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thrust_ptr = pointer(raw_ptr);
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} else {
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try {
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thrust_ptr = SuperT::allocate(n);
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dh::safe_cuda(hipGetLastError());
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} catch (const std::exception &e) {
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ThrowOOMError(e.what(), n * sizeof(T));
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}
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}
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std::cerr << "XGBCachingDeviceAllocatorImpl: Allocated " << n * sizeof(T)
|
|
<< " bytes at " << thrust_ptr.get() << std::endl;
|
|
GlobalMemoryLogger().RegisterAllocation(thrust_ptr.get(), n * sizeof(T));
|
|
return thrust_ptr;
|
|
}
|
|
void deallocate(pointer ptr, size_t n) {
|
|
std::cerr << "XGBCachingDeviceAllocatorImpl: Deallocating " << n * sizeof(T)
|
|
<< " bytes at " << ptr.get() << std::endl;
|
|
GlobalMemoryLogger().RegisterDeallocation(ptr.get(), n * sizeof(T));
|
|
if (use_cub_allocator_) {
|
|
GetGlobalCachingAllocator().DeviceFree(ptr.get());
|
|
} else {
|
|
SuperT::deallocate(ptr, n);
|
|
}
|
|
}
|
|
#if defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
|
|
XGBCachingDeviceAllocatorImpl()
|
|
: SuperT(rmm::cuda_stream_default, rmm::mr::get_current_device_resource()),
|
|
use_cub_allocator_(!xgboost::GlobalConfigThreadLocalStore::Get()->use_rmm) {}
|
|
#endif // defined(XGBOOST_USE_RMM) && XGBOOST_USE_RMM == 1
|
|
XGBOOST_DEVICE void construct(T *) {} // NOLINT
|
|
private:
|
|
bool use_cub_allocator_{true};
|
|
};
|
|
} // namespace detail
|
|
|
|
// Declare xgboost allocators
|
|
// Replacement of allocator with custom backend should occur here
|
|
template <typename T>
|
|
using XGBDeviceAllocator = detail::XGBDefaultDeviceAllocatorImpl<T>;
|
|
/*! Be careful that the initialization constructor is a no-op, which means calling
|
|
* `vec.resize(n)` won't initialize the memory region to 0. Instead use
|
|
* `vec.resize(n, 0)`*/
|
|
template <typename T>
|
|
using XGBCachingDeviceAllocator = detail::XGBCachingDeviceAllocatorImpl<T>;
|
|
/** \brief Specialisation of thrust device vector using custom allocator. */
|
|
template <typename T>
|
|
using device_vector = thrust::device_vector<T, XGBDeviceAllocator<T>>; // NOLINT
|
|
template <typename T>
|
|
using caching_device_vector = thrust::device_vector<T, XGBCachingDeviceAllocator<T>>; // NOLINT
|
|
|
|
// Faster to instantiate than caching_device_vector and invokes no synchronisation
|
|
// Use this where vector functionality (e.g. resize) is not required
|
|
template <typename T>
|
|
class TemporaryArray {
|
|
public:
|
|
using AllocT = XGBCachingDeviceAllocator<T>;
|
|
using value_type = T; // NOLINT
|
|
explicit TemporaryArray(size_t n) : size_(n) { ptr_ = AllocT().allocate(n); }
|
|
TemporaryArray(size_t n, T val) : size_(n) {
|
|
ptr_ = AllocT().allocate(n);
|
|
this->fill(val);
|
|
}
|
|
~TemporaryArray() { AllocT().deallocate(ptr_, this->size()); }
|
|
void fill(T val) // NOLINT
|
|
{
|
|
int device = 0;
|
|
dh::safe_cuda(hipGetDevice(&device));
|
|
auto d_data = ptr_.get();
|
|
LaunchN(this->size(), [=] __device__(size_t idx) { d_data[idx] = val; });
|
|
}
|
|
thrust::device_ptr<T> data() { return ptr_; } // NOLINT
|
|
size_t size() { return size_; } // NOLINT
|
|
|
|
private:
|
|
thrust::device_ptr<T> ptr_;
|
|
size_t size_;
|
|
};
|
|
|
|
/**
|
|
* \brief A double buffer, useful for algorithms like sort.
|
|
*/
|
|
template <typename T>
|
|
class DoubleBuffer {
|
|
public:
|
|
hipcub::DoubleBuffer<T> buff;
|
|
xgboost::common::Span<T> a, b;
|
|
DoubleBuffer() = default;
|
|
template <typename VectorT>
|
|
DoubleBuffer(VectorT *v1, VectorT *v2) {
|
|
a = xgboost::common::Span<T>(v1->data().get(), v1->size());
|
|
b = xgboost::common::Span<T>(v2->data().get(), v2->size());
|
|
buff = hipcub::DoubleBuffer<T>(a.data(), b.data());
|
|
}
|
|
|
|
size_t Size() const {
|
|
CHECK_EQ(a.size(), b.size());
|
|
return a.size();
|
|
}
|
|
hipcub::DoubleBuffer<T> &CubBuffer() { return buff; }
|
|
|
|
T *Current() { return buff.Current(); }
|
|
xgboost::common::Span<T> CurrentSpan() {
|
|
return xgboost::common::Span<T>{buff.Current(), Size()};
|
|
}
|
|
|
|
T *Other() { return buff.Alternate(); }
|
|
};
|
|
|
|
template <typename T>
|
|
xgboost::common::Span<T> LazyResize(xgboost::Context const *ctx,
|
|
xgboost::HostDeviceVector<T> *buffer, std::size_t n) {
|
|
buffer->SetDevice(ctx->Device());
|
|
if (buffer->Size() < n) {
|
|
buffer->Resize(n);
|
|
}
|
|
return buffer->DeviceSpan().subspan(0, n);
|
|
}
|
|
|
|
/**
|
|
* \brief Copies device span to std::vector.
|
|
*
|
|
* \tparam T Generic type parameter.
|
|
* \param [in,out] dst Copy destination.
|
|
* \param src Copy source. Must be device memory.
|
|
*/
|
|
template <typename T>
|
|
void CopyDeviceSpanToVector(std::vector<T> *dst, xgboost::common::Span<T> src) {
|
|
CHECK_EQ(dst->size(), src.size());
|
|
std::cerr << "CopyDeviceSpanToVector: Copying " << src.size() * sizeof(T)
|
|
<< " bytes from device to host" << std::endl;
|
|
dh::safe_cuda(hipMemcpyAsync(dst->data(), src.data(), dst->size() * sizeof(T),
|
|
hipMemcpyDeviceToHost));
|
|
}
|
|
|
|
/**
|
|
* \brief Copies const device span to std::vector.
|
|
*
|
|
* \tparam T Generic type parameter.
|
|
* \param [in,out] dst Copy destination.
|
|
* \param src Copy source. Must be device memory.
|
|
*/
|
|
template <typename T>
|
|
void CopyDeviceSpanToVector(std::vector<T> *dst, xgboost::common::Span<const T> src) {
|
|
CHECK_EQ(dst->size(), src.size());
|
|
dh::safe_cuda(hipMemcpyAsync(dst->data(), src.data(), dst->size() * sizeof(T),
|
|
hipMemcpyDeviceToHost));
|
|
}
|
|
|
|
template <class HContainer, class DContainer>
|
|
void CopyToD(HContainer const &h, DContainer *d) {
|
|
if (h.empty()) {
|
|
d->clear();
|
|
return;
|
|
}
|
|
d->resize(h.size());
|
|
using HVT = std::remove_cv_t<typename HContainer::value_type>;
|
|
using DVT = std::remove_cv_t<typename DContainer::value_type>;
|
|
static_assert(std::is_same<HVT, DVT>::value, "Host and device containers must have same value type.");
|
|
std::cerr << "CopyToD: Copying " << h.size() * sizeof(HVT)
|
|
<< " bytes from host to device" << std::endl;
|
|
dh::safe_cuda(hipMemcpyAsync(d->data().get(), h.data(), h.size() * sizeof(HVT),
|
|
hipMemcpyHostToDevice));
|
|
}
|
|
|
|
// Keep track of pinned memory allocation
|
|
struct PinnedMemory {
|
|
void *temp_storage{nullptr};
|
|
size_t temp_storage_bytes{0};
|
|
|
|
~PinnedMemory() { Free(); }
|
|
|
|
template <typename T>
|
|
xgboost::common::Span<T> GetSpan(size_t size) {
|
|
size_t num_bytes = size * sizeof(T);
|
|
if (num_bytes > temp_storage_bytes) {
|
|
Free();
|
|
safe_cuda(hipHostMalloc(&temp_storage, num_bytes));
|
|
temp_storage_bytes = num_bytes;
|
|
}
|
|
return xgboost::common::Span<T>(static_cast<T *>(temp_storage), size);
|
|
}
|
|
|
|
template <typename T>
|
|
xgboost::common::Span<T> GetSpan(size_t size, T init) {
|
|
auto result = this->GetSpan<T>(size);
|
|
for (auto &e : result) {
|
|
e = init;
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void Free() {
|
|
if (temp_storage != nullptr) {
|
|
safe_cuda(hipHostFree(temp_storage));
|
|
}
|
|
}
|
|
};
|
|
|
|
/*
|
|
* Utility functions
|
|
*/
|
|
|
|
/**
|
|
* @brief Helper function to perform device-wide sum-reduction, returns to the
|
|
* host
|
|
* @param in the input array to be reduced
|
|
* @param nVals number of elements in the input array
|
|
*/
|
|
template <typename T>
|
|
typename std::iterator_traits<T>::value_type SumReduction(T in, int nVals) {
|
|
std::cerr << "Entering SumReduction, nVals: " << nVals << std::endl;
|
|
using ValueT = typename std::iterator_traits<T>::value_type;
|
|
|
|
size_t tmpSize {0};
|
|
ValueT *dummy_out = nullptr;
|
|
|
|
try {
|
|
dh::safe_cuda(hipcub::DeviceReduce::Sum(nullptr, tmpSize, in, dummy_out, nVals));
|
|
std::cerr << "Temporary storage size: " << tmpSize << std::endl;
|
|
|
|
TemporaryArray<char> temp(tmpSize + sizeof(ValueT));
|
|
auto ptr = reinterpret_cast<ValueT *>(temp.data().get()) + 1;
|
|
|
|
dh::safe_cuda(hipcub::DeviceReduce::Sum(
|
|
reinterpret_cast<void *>(ptr), tmpSize, in, reinterpret_cast<ValueT *>(temp.data().get()), nVals));
|
|
|
|
ValueT sum;
|
|
dh::safe_cuda(hipMemcpy(&sum, temp.data().get(), sizeof(ValueT), hipMemcpyDeviceToHost));
|
|
|
|
std::cerr << "SumReduction completed successfully" << std::endl;
|
|
return sum;
|
|
} catch (const std::exception& e) {
|
|
std::cerr << "Exception in SumReduction: " << e.what() << std::endl;
|
|
throw;
|
|
}
|
|
}
|
|
|
|
constexpr std::pair<int, int> CUDAVersion() {
|
|
return std::make_pair(HIP_VERSION_MAJOR, HIP_VERSION_MINOR);
|
|
}
|
|
|
|
constexpr std::pair<int32_t, int32_t> ThrustVersion() {
|
|
return std::make_pair(THRUST_MAJOR_VERSION, THRUST_MINOR_VERSION);
|
|
}
|
|
// Whether do we have thrust 1.x with x >= minor
|
|
template <int32_t minor>
|
|
constexpr bool HasThrustMinorVer() {
|
|
return (ThrustVersion().first == 1 && ThrustVersion().second >= minor) ||
|
|
ThrustVersion().first > 1;
|
|
}
|
|
|
|
namespace detail {
|
|
template <typename T>
|
|
using TypedDiscardCTK114 = thrust::discard_iterator<T>;
|
|
|
|
template <typename T>
|
|
class TypedDiscard : public thrust::discard_iterator<T> {
|
|
public:
|
|
using value_type = T; // NOLINT
|
|
};
|
|
} // namespace detail
|
|
|
|
template <typename T>
|
|
using TypedDiscard =
|
|
std::conditional_t<HasThrustMinorVer<12>(), detail::TypedDiscardCTK114<T>,
|
|
detail::TypedDiscard<T>>;
|
|
|
|
template <typename VectorT, typename T = typename VectorT::value_type,
|
|
typename IndexT = typename xgboost::common::Span<T>::index_type>
|
|
xgboost::common::Span<T> ToSpan(
|
|
VectorT &vec,
|
|
IndexT offset = 0,
|
|
IndexT size = std::numeric_limits<size_t>::max()) {
|
|
size = size == std::numeric_limits<size_t>::max() ? vec.size() : size;
|
|
CHECK_LE(offset + size, vec.size());
|
|
return {vec.data().get() + offset, size};
|
|
}
|
|
|
|
template <typename T>
|
|
xgboost::common::Span<T> ToSpan(thrust::device_vector<T>& vec,
|
|
size_t offset, size_t size) {
|
|
return ToSpan(vec, offset, size);
|
|
}
|
|
|
|
// thrust begin, similiar to std::begin
|
|
template <typename T>
|
|
thrust::device_ptr<T> tbegin(xgboost::HostDeviceVector<T>& vector) { // NOLINT
|
|
return thrust::device_ptr<T>(vector.DevicePointer());
|
|
}
|
|
|
|
template <typename T>
|
|
thrust::device_ptr<T> tend(xgboost::HostDeviceVector<T>& vector) { // // NOLINT
|
|
return tbegin(vector) + vector.Size();
|
|
}
|
|
|
|
template <typename T>
|
|
thrust::device_ptr<T const> tcbegin(xgboost::HostDeviceVector<T> const& vector) { // NOLINT
|
|
return thrust::device_ptr<T const>(vector.ConstDevicePointer());
|
|
}
|
|
|
|
template <typename T>
|
|
thrust::device_ptr<T const> tcend(xgboost::HostDeviceVector<T> const& vector) { // NOLINT
|
|
return tcbegin(vector) + vector.Size();
|
|
}
|
|
|
|
template <typename T>
|
|
XGBOOST_DEVICE thrust::device_ptr<T> tbegin(xgboost::common::Span<T>& span) { // NOLINT
|
|
return thrust::device_ptr<T>(span.data());
|
|
}
|
|
|
|
template <typename T>
|
|
XGBOOST_DEVICE thrust::device_ptr<T> tbegin(xgboost::common::Span<T> const& span) { // NOLINT
|
|
return thrust::device_ptr<T>(span.data());
|
|
}
|
|
|
|
template <typename T>
|
|
XGBOOST_DEVICE thrust::device_ptr<T> tend(xgboost::common::Span<T>& span) { // NOLINT
|
|
return tbegin(span) + span.size();
|
|
}
|
|
|
|
template <typename T>
|
|
XGBOOST_DEVICE thrust::device_ptr<T> tend(xgboost::common::Span<T> const& span) { // NOLINT
|
|
return tbegin(span) + span.size();
|
|
}
|
|
|
|
template <typename T>
|
|
XGBOOST_DEVICE auto trbegin(xgboost::common::Span<T> &span) { // NOLINT
|
|
return thrust::make_reverse_iterator(span.data() + span.size());
|
|
}
|
|
|
|
template <typename T>
|
|
XGBOOST_DEVICE auto trend(xgboost::common::Span<T> &span) { // NOLINT
|
|
return trbegin(span) + span.size();
|
|
}
|
|
|
|
template <typename T>
|
|
XGBOOST_DEVICE thrust::device_ptr<T const> tcbegin(xgboost::common::Span<T> const& span) { // NOLINT
|
|
return thrust::device_ptr<T const>(span.data());
|
|
}
|
|
|
|
template <typename T>
|
|
XGBOOST_DEVICE thrust::device_ptr<T const> tcend(xgboost::common::Span<T> const& span) { // NOLINT
|
|
return tcbegin(span) + span.size();
|
|
}
|
|
|
|
template <typename T>
|
|
XGBOOST_DEVICE auto tcrbegin(xgboost::common::Span<T> const &span) { // NOLINT
|
|
return thrust::make_reverse_iterator(span.data() + span.size());
|
|
}
|
|
|
|
template <typename T>
|
|
XGBOOST_DEVICE auto tcrend(xgboost::common::Span<T> const &span) { // NOLINT
|
|
return tcrbegin(span) + span.size();
|
|
}
|
|
|
|
// Atomic add function for gradients
|
|
template <typename OutputGradientT, typename InputGradientT>
|
|
XGBOOST_DEV_INLINE void AtomicAddGpair(OutputGradientT* dest,
|
|
const InputGradientT& gpair) {
|
|
auto dst_ptr = reinterpret_cast<typename OutputGradientT::ValueT*>(dest);
|
|
|
|
atomicAdd(dst_ptr,
|
|
static_cast<typename OutputGradientT::ValueT>(gpair.GetGrad()));
|
|
atomicAdd(dst_ptr + 1,
|
|
static_cast<typename OutputGradientT::ValueT>(gpair.GetHess()));
|
|
}
|
|
|
|
|
|
// Thrust version of this function causes error on Windows
|
|
template <typename ReturnT, typename IterT, typename FuncT>
|
|
XGBOOST_DEVICE thrust::transform_iterator<FuncT, IterT, ReturnT> MakeTransformIterator(
|
|
IterT iter, FuncT func) {
|
|
return thrust::transform_iterator<FuncT, IterT, ReturnT>(iter, func);
|
|
}
|
|
|
|
template <typename It>
|
|
size_t XGBOOST_DEVICE SegmentId(It first, It last, size_t idx) {
|
|
size_t segment_id = thrust::upper_bound(thrust::seq, first, last, idx) - 1 - first;
|
|
return segment_id;
|
|
}
|
|
|
|
template <typename T>
|
|
size_t XGBOOST_DEVICE SegmentId(xgboost::common::Span<T> segments_ptr, size_t idx) {
|
|
return SegmentId(segments_ptr.cbegin(), segments_ptr.cend(), idx);
|
|
}
|
|
|
|
namespace detail {
|
|
template <typename Key, typename KeyOutIt>
|
|
struct SegmentedUniqueReduceOp {
|
|
KeyOutIt key_out;
|
|
__device__ Key const& operator()(Key const& key) const {
|
|
auto constexpr kOne = static_cast<std::remove_reference_t<decltype(*(key_out + key.first))>>(1);
|
|
atomicAdd(&(*(key_out + key.first)), kOne);
|
|
return key;
|
|
}
|
|
};
|
|
} // namespace detail
|
|
|
|
/* \brief Segmented unique function. Keys are pointers to segments with key_segments_last -
|
|
* key_segments_first = n_segments + 1.
|
|
*
|
|
* \pre Input segment and output segment must not overlap.
|
|
*
|
|
* \param key_segments_first Beginning iterator of segments.
|
|
* \param key_segments_last End iterator of segments.
|
|
* \param val_first Beginning iterator of values.
|
|
* \param val_last End iterator of values.
|
|
* \param key_segments_out Output iterator of segments.
|
|
* \param val_out Output iterator of values.
|
|
*
|
|
* \return Number of unique values in total.
|
|
*/
|
|
template <typename DerivedPolicy, typename KeyInIt, typename KeyOutIt, typename ValInIt,
|
|
typename ValOutIt, typename CompValue, typename CompKey>
|
|
size_t
|
|
SegmentedUnique(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
|
KeyInIt key_segments_first, KeyInIt key_segments_last, ValInIt val_first,
|
|
ValInIt val_last, KeyOutIt key_segments_out, ValOutIt val_out,
|
|
CompValue comp, CompKey comp_key=thrust::equal_to<size_t>{}) {
|
|
using Key = thrust::pair<size_t, typename thrust::iterator_traits<ValInIt>::value_type>;
|
|
auto unique_key_it = dh::MakeTransformIterator<Key>(
|
|
thrust::make_counting_iterator(static_cast<size_t>(0)),
|
|
[=] __device__(size_t i) {
|
|
size_t seg = dh::SegmentId(key_segments_first, key_segments_last, i);
|
|
return thrust::make_pair(seg, *(val_first + i));
|
|
});
|
|
size_t segments_len = key_segments_last - key_segments_first;
|
|
thrust::fill(thrust::device, key_segments_out, key_segments_out + segments_len, 0);
|
|
size_t n_inputs = std::distance(val_first, val_last);
|
|
// Reduce the number of uniques elements per segment, avoid creating an intermediate
|
|
// array for `reduce_by_key`. It's limited by the types that atomicAdd supports. For
|
|
// example, size_t is not supported as of CUDA 10.2.
|
|
auto reduce_it = thrust::make_transform_output_iterator(
|
|
thrust::make_discard_iterator(),
|
|
detail::SegmentedUniqueReduceOp<Key, KeyOutIt>{key_segments_out});
|
|
auto uniques_ret = thrust::unique_by_key_copy(
|
|
exec, unique_key_it, unique_key_it + n_inputs,
|
|
val_first, reduce_it, val_out,
|
|
[=] __device__(Key const &l, Key const &r) {
|
|
if (comp_key(l.first, r.first)) {
|
|
// In the same segment.
|
|
return comp(l.second, r.second);
|
|
}
|
|
return false;
|
|
});
|
|
auto n_uniques = uniques_ret.second - val_out;
|
|
CHECK_LE(n_uniques, n_inputs);
|
|
thrust::exclusive_scan(exec, key_segments_out,
|
|
key_segments_out + segments_len, key_segments_out, 0);
|
|
return n_uniques;
|
|
}
|
|
|
|
template <typename... Inputs,
|
|
std::enable_if_t<std::tuple_size<std::tuple<Inputs...>>::value == 7>
|
|
* = nullptr>
|
|
size_t SegmentedUnique(Inputs &&...inputs) {
|
|
dh::XGBCachingDeviceAllocator<char> alloc;
|
|
return SegmentedUnique(thrust::hip::par(alloc),
|
|
std::forward<Inputs &&>(inputs)...,
|
|
thrust::equal_to<size_t>{});
|
|
}
|
|
|
|
/**
|
|
* \brief Unique by key for many groups of data. Has same constraint as `SegmentedUnique`.
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*
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* \tparam exec thrust execution policy
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* \tparam key_segments_first start iter to segment pointer
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* \tparam key_segments_last end iter to segment pointer
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* \tparam key_first start iter to key for comparison
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* \tparam key_last end iter to key for comparison
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* \tparam val_first start iter to values
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* \tparam key_segments_out output iterator for new segment pointer
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* \tparam val_out output iterator for values
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* \tparam comp binary comparison operator
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*/
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template <typename DerivedPolicy, typename SegInIt, typename SegOutIt,
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typename KeyInIt, typename ValInIt, typename ValOutIt, typename Comp>
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size_t SegmentedUniqueByKey(
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const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
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SegInIt key_segments_first, SegInIt key_segments_last, KeyInIt key_first,
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KeyInIt key_last, ValInIt val_first, SegOutIt key_segments_out,
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ValOutIt val_out, Comp comp) {
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using Key =
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thrust::pair<size_t,
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typename thrust::iterator_traits<KeyInIt>::value_type>;
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auto unique_key_it = dh::MakeTransformIterator<Key>(
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thrust::make_counting_iterator(static_cast<size_t>(0)),
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[=] __device__(size_t i) {
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size_t seg = dh::SegmentId(key_segments_first, key_segments_last, i);
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return thrust::make_pair(seg, *(key_first + i));
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});
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size_t segments_len = key_segments_last - key_segments_first;
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thrust::fill(thrust::device, key_segments_out,
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key_segments_out + segments_len, 0);
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size_t n_inputs = std::distance(key_first, key_last);
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// Reduce the number of uniques elements per segment, avoid creating an
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// intermediate array for `reduce_by_key`. It's limited by the types that
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// atomicAdd supports. For example, size_t is not supported as of CUDA 10.2.
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auto reduce_it = thrust::make_transform_output_iterator(
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thrust::make_discard_iterator(),
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detail::SegmentedUniqueReduceOp<Key, SegOutIt>{key_segments_out});
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auto uniques_ret = thrust::unique_by_key_copy(
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exec, unique_key_it, unique_key_it + n_inputs, val_first, reduce_it,
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val_out, [=] __device__(Key const &l, Key const &r) {
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if (l.first == r.first) {
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// In the same segment.
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return comp(thrust::get<1>(l), thrust::get<1>(r));
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}
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return false;
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});
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auto n_uniques = uniques_ret.second - val_out;
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CHECK_LE(n_uniques, n_inputs);
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thrust::exclusive_scan(exec, key_segments_out,
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key_segments_out + segments_len, key_segments_out, 0);
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return n_uniques;
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}
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template <typename Policy, typename InputIt, typename Init, typename Func>
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auto Reduce(Policy policy, InputIt first, InputIt second, Init init, Func reduce_op) {
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std::cerr << "Entering Reduce function" << std::endl;
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size_t constexpr kLimit = std::numeric_limits<int32_t>::max() / 2;
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size_t size = std::distance(first, second);
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std::cerr << "Total size for reduction: " << size << std::endl;
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using Ty = std::remove_cv_t<Init>;
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Ty aggregate = init;
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for (size_t offset = 0; offset < size; offset += kLimit) {
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auto begin_it = first + offset;
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auto end_it = first + std::min(offset + kLimit, size);
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size_t batch_size = std::distance(begin_it, end_it);
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CHECK_LE(batch_size, size);
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std::cerr << "Processing batch: offset=" << offset << ", batch_size=" << batch_size << std::endl;
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try {
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// Print the iterator types
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std::cerr << "Iterator types - begin: " << typeid(begin_it).name()
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<< ", end: " << typeid(end_it).name() << std::endl;
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auto ret = thrust::reduce(policy, begin_it, end_it, init, reduce_op);
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aggregate = reduce_op(aggregate, ret);
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std::cerr << "Batch reduction completed successfully" << std::endl;
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} catch (const thrust::system_error& e) {
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std::cerr << "Thrust system error in reduce: " << e.what() << std::endl;
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std::cerr << "Error code: " << e.code() << std::endl;
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throw;
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} catch (const std::exception& e) {
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std::cerr << "Exception in thrust::reduce: " << e.what() << std::endl;
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throw;
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}
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// Check for any HIP errors after the reduction
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hipError_t hip_err = hipGetLastError();
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if (hip_err != hipSuccess) {
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std::cerr << "HIP error after reduction: " << hipGetErrorString(hip_err) << std::endl;
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}
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}
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std::cerr << "Exiting Reduce function" << std::endl;
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return aggregate;
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}
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// wrapper to avoid integer `num_items`.
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template <typename InputIteratorT, typename OutputIteratorT, typename ScanOpT,
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typename OffsetT>
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void InclusiveScan(InputIteratorT d_in, OutputIteratorT d_out, ScanOpT scan_op,
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OffsetT num_items) {
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|
size_t bytes = 0;
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|
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safe_cuda((rocprim::inclusive_scan(nullptr, bytes, d_in, d_out, (size_t) num_items, scan_op)));
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TemporaryArray<char> storage(bytes);
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safe_cuda((rocprim::inclusive_scan(storage.data().get(), bytes, d_in, d_out, (size_t) num_items, scan_op)));
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}
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template <typename InIt, typename OutIt, typename Predicate>
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void CopyIf(InIt in_first, InIt in_second, OutIt out_first, Predicate pred) {
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|
// We loop over batches because thrust::copy_if can't deal with sizes > 2^31
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// See thrust issue #1302, XGBoost #6822
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size_t constexpr kMaxCopySize = std::numeric_limits<int>::max() / 2;
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|
size_t length = std::distance(in_first, in_second);
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|
XGBCachingDeviceAllocator<char> alloc;
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|
for (size_t offset = 0; offset < length; offset += kMaxCopySize) {
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|
auto begin_input = in_first + offset;
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auto end_input = in_first + std::min(offset + kMaxCopySize, length);
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out_first = thrust::copy_if(thrust::hip::par(alloc), begin_input,
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|
end_input, out_first, pred);
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}
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}
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template <typename InputIteratorT, typename OutputIteratorT, typename OffsetT>
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void InclusiveSum(InputIteratorT d_in, OutputIteratorT d_out, OffsetT num_items) {
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InclusiveScan(d_in, d_out, hipcub::Sum(), num_items);
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}
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|
|
|
class CUDAStreamView;
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|
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|
class CUDAEvent {
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|
hipEvent_t event_{nullptr};
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|
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public:
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CUDAEvent() { dh::safe_cuda(hipEventCreateWithFlags(&event_, hipEventDisableTiming)); }
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|
~CUDAEvent() {
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|
if (event_) {
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|
dh::safe_cuda(hipEventDestroy(event_));
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|
}
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|
}
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|
|
|
CUDAEvent(CUDAEvent const &that) = delete;
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|
CUDAEvent &operator=(CUDAEvent const &that) = delete;
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|
|
|
inline void Record(CUDAStreamView stream); // NOLINT
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|
|
|
operator hipEvent_t() const { return event_; } // NOLINT
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|
};
|
|
|
|
class CUDAStreamView {
|
|
hipStream_t stream_{nullptr};
|
|
|
|
public:
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|
explicit CUDAStreamView(hipStream_t s) : stream_{s} {}
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|
void Wait(CUDAEvent const &e) {
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|
dh::safe_cuda(hipStreamWaitEvent(stream_, hipEvent_t{e}, hipEventDefault));
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|
}
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operator hipStream_t() const { // NOLINT
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|
return stream_;
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|
}
|
|
hipError_t Sync(bool error = true) {
|
|
if (error) {
|
|
dh::safe_cuda(hipStreamSynchronize(stream_));
|
|
return hipSuccess;
|
|
}
|
|
return hipStreamSynchronize(stream_);
|
|
}
|
|
};
|
|
|
|
inline void CUDAEvent::Record(CUDAStreamView stream) { // NOLINT
|
|
dh::safe_cuda(hipEventRecord(event_, hipStream_t{stream}));
|
|
}
|
|
|
|
// Changing this has effect on prediction return, where we need to pass the pointer to
|
|
// third-party libraries like cuPy
|
|
inline CUDAStreamView DefaultStream() {
|
|
#ifdef HIP_API_PER_THREAD_DEFAULT_STREAM
|
|
return CUDAStreamView{hipStreamPerThread};
|
|
#else
|
|
return CUDAStreamView{hipStreamDefault};
|
|
#endif
|
|
}
|
|
|
|
class CUDAStream {
|
|
hipStream_t stream_;
|
|
|
|
public:
|
|
CUDAStream() { dh::safe_cuda(hipStreamCreateWithFlags(&stream_, hipStreamNonBlocking)); }
|
|
~CUDAStream() { dh::safe_cuda(hipStreamDestroy(stream_)); }
|
|
|
|
[[nodiscard]] CUDAStreamView View() const { return CUDAStreamView{stream_}; }
|
|
[[nodiscard]] hipStream_t Handle() const { return stream_; }
|
|
|
|
void Sync() { this->View().Sync(); }
|
|
};
|
|
|
|
// Force nvcc to load data as constant
|
|
template <typename T>
|
|
class LDGIterator {
|
|
using DeviceWordT = typename hipcub::UnitWord<T>::DeviceWord;
|
|
static constexpr std::size_t kNumWords = sizeof(T) / sizeof(DeviceWordT);
|
|
|
|
const T *ptr_;
|
|
|
|
public:
|
|
XGBOOST_DEVICE explicit LDGIterator(const T *ptr) : ptr_(ptr) {}
|
|
__device__ T operator[](std::size_t idx) const {
|
|
DeviceWordT tmp[kNumWords];
|
|
static_assert(sizeof(tmp) == sizeof(T), "Expect sizes to be equal.");
|
|
|
|
for (int i = 0; i < kNumWords; i++) {
|
|
tmp[i] = __ldg(reinterpret_cast<const DeviceWordT *>(ptr_ + idx) + i);
|
|
}
|
|
return *reinterpret_cast<const T *>(tmp);
|
|
}
|
|
};
|
|
} // namespace dh
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