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Enable distributed GPU training over Rabit (#7930)

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Rong Ou 2022-05-30 13:09:45 -07:00 committed by GitHub
parent 6275cdc486
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9 changed files with 458 additions and 129 deletions
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4
doc/build.rst
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@ -136,9 +136,9 @@ From the command line on Linux starting from the XGBoost directory:
To speed up compilation, the compute version specific to your GPU could be passed to cmake as, e.g., ``-DGPU_COMPUTE_VER=50``. A quick explanation and numbers for some architectures can be found `in this page <https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/>`_.
.. note:: Enabling distributed GPU training
.. note:: Faster distributed GPU training with NCCL
By default, distributed GPU training is disabled and only a single GPU will be used. To enable distributed GPU training, set the option ``USE_NCCL=ON``. Distributed GPU training depends on NCCL2, available at `this link <https://developer.nvidia.com/nccl>`_. Since NCCL2 is only available for Linux machines, **distributed GPU training is available only for Linux**.
By default, distributed GPU training is enabled and uses Rabit for communication. For faster training, set the option ``USE_NCCL=ON``. Faster distributed GPU training depends on NCCL2, available at `this link <https://developer.nvidia.com/nccl>`_. Since NCCL2 is only available for Linux machines, **faster distributed GPU training is available only for Linux**.
.. code-block:: bash

18
src/common/common.h
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@ -274,6 +274,24 @@ template <typename Indexable>
XGBOOST_DEVICE size_t LastOf(size_t group, Indexable const &indptr) {
return indptr[group + 1] - 1;
}
/**
* @brief A CRTP (curiously recurring template pattern) helper function.
*
* https://www.fluentcpp.com/2017/05/19/crtp-helper/
*
* Does two things:
* 1. Makes "crtp" explicit in the inheritance structure of a CRTP base class.
* 2. Avoids having to `static_cast` in a lot of places.
*
* @tparam T The derived class in a CRTP hierarchy.
*/
template <typename T>
struct Crtp {
T &Underlying() { return static_cast<T &>(*this); }
T const &Underlying() const { return static_cast<T const &>(*this); }
};
} // namespace common
} // namespace xgboost
#endif // XGBOOST_COMMON_COMMON_H_

63
src/common/device_helpers.cu
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@ -30,19 +30,15 @@ std::string PrintUUID(xgboost::common::Span<uint64_t, kUuidLength> uuid) {
return ss.str();
}
void AllReducer::Init(int _device_ordinal) {
#ifdef XGBOOST_USE_NCCL
device_ordinal_ = _device_ordinal;
dh::safe_cuda(cudaSetDevice(device_ordinal_));
void NcclAllReducer::DoInit(int _device_ordinal) {
int32_t const rank = rabit::GetRank();
int32_t const world = rabit::GetWorldSize();
std::vector<uint64_t> uuids(world * kUuidLength, 0);
auto s_uuid = xgboost::common::Span<uint64_t>{uuids.data(), uuids.size()};
auto s_this_uuid = s_uuid.subspan(rank * kUuidLength, kUuidLength);
GetCudaUUID(world, rank, device_ordinal_, s_this_uuid);
GetCudaUUID(world, rank, _device_ordinal, s_this_uuid);
// No allgather yet.
rabit::Allreduce<rabit::op::Sum, uint64_t>(uuids.data(), uuids.size());
@ -66,20 +62,11 @@ void AllReducer::Init(int _device_ordinal) {
id_ = GetUniqueId();
dh::safe_nccl(ncclCommInitRank(&comm_, rabit::GetWorldSize(), id_, rank));
safe_cuda(cudaStreamCreate(&stream_));
initialised_ = true;
#else
if (rabit::IsDistributed()) {
LOG(FATAL) << "XGBoost is not compiled with NCCL.";
}
#endif // XGBOOST_USE_NCCL
}
void AllReducer::AllGather(void const *data, size_t length_bytes,
std::vector<size_t> *segments,
dh::caching_device_vector<char> *recvbuf) {
#ifdef XGBOOST_USE_NCCL
CHECK(initialised_);
dh::safe_cuda(cudaSetDevice(device_ordinal_));
void NcclAllReducer::DoAllGather(void const *data, size_t length_bytes,
std::vector<size_t> *segments,
dh::caching_device_vector<char> *recvbuf) {
size_t world = rabit::GetWorldSize();
segments->clear();
segments->resize(world, 0);
@ -98,11 +85,9 @@ void AllReducer::AllGather(void const *data, size_t length_bytes,
offset += as_bytes;
}
safe_nccl(ncclGroupEnd());
#endif // XGBOOST_USE_NCCL
}
AllReducer::~AllReducer() {
#ifdef XGBOOST_USE_NCCL
NcclAllReducer::~NcclAllReducer() {
if (initialised_) {
dh::safe_cuda(cudaStreamDestroy(stream_));
ncclCommDestroy(comm_);
@ -112,7 +97,41 @@ AllReducer::~AllReducer() {
LOG(CONSOLE) << "AllReduce calls: " << allreduce_calls_;
LOG(CONSOLE) << "AllReduce total MiB communicated: " << allreduce_bytes_/1048576;
}
#endif // XGBOOST_USE_NCCL
}
#else
void RabitAllReducer::DoInit(int _device_ordinal) {
#if !defined(XGBOOST_USE_FEDERATED)
if (rabit::IsDistributed()) {
LOG(CONSOLE) << "XGBoost is not compiled with NCCL, falling back to Rabit.";
}
#endif
}
void RabitAllReducer::DoAllGather(void const *data, size_t length_bytes,
std::vector<size_t> *segments,
dh::caching_device_vector<char> *recvbuf) {
size_t world = rabit::GetWorldSize();
segments->clear();
segments->resize(world, 0);
segments->at(rabit::GetRank()) = length_bytes;
rabit::Allreduce<rabit::op::Max>(segments->data(), segments->size());
auto total_bytes = std::accumulate(segments->cbegin(), segments->cend(), 0UL);
recvbuf->resize(total_bytes);
sendrecvbuf_.reserve(total_bytes);
auto rank = rabit::GetRank();
size_t offset = 0;
for (int32_t i = 0; i < world; ++i) {
size_t as_bytes = segments->at(i);
if (i == rank) {
safe_cuda(
cudaMemcpy(sendrecvbuf_.data() + offset, data, segments->at(rank), cudaMemcpyDefault));
}
rabit::Broadcast(sendrecvbuf_.data() + offset, as_bytes, i);
offset += as_bytes;
}
safe_cuda(cudaMemcpy(recvbuf->data().get(), sendrecvbuf_.data(), total_bytes, cudaMemcpyDefault));
}
#endif // XGBOOST_USE_NCCL
} // namespace dh

466
src/common/device_helpers.cuh
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@ -738,71 +738,55 @@ using TypedDiscard =
* \class AllReducer
*
* \brief All reducer class that manages its own communication group and
* streams. Must be initialised before use. If XGBoost is compiled without NCCL
* this is a dummy class that will error if used with more than one GPU.
* streams. Must be initialised before use. If XGBoost is compiled without NCCL,
* this falls back to use Rabit.
*/
class AllReducer {
bool initialised_ {false};
size_t allreduce_bytes_ {0}; // Keep statistics of the number of bytes communicated
size_t allreduce_calls_ {0}; // Keep statistics of the number of reduce calls
#ifdef XGBOOST_USE_NCCL
ncclComm_t comm_;
cudaStream_t stream_;
int device_ordinal_;
ncclUniqueId id_;
#endif
template <typename AllReducer>
class AllReducerBase : public xgboost::common::Crtp<AllReducer> {
public:
AllReducer() = default;
virtual ~AllReducerBase() = default;
/**
* \brief Initialise with the desired device ordinal for this communication
* group.
* \brief Initialise with the desired device ordinal for this allreducer.
*
* \param device_ordinal The device ordinal.
*/
void Init(int _device_ordinal);
~AllReducer();
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void AllReduceSum(const double *sendbuff, double *recvbuff, int count) {
#ifdef XGBOOST_USE_NCCL
CHECK(initialised_);
void Init(int _device_ordinal) {
device_ordinal_ = _device_ordinal;
dh::safe_cuda(cudaSetDevice(device_ordinal_));
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclDouble, ncclSum, comm_, stream_));
allreduce_bytes_ += count * sizeof(double);
allreduce_calls_ += 1;
#endif
this->Underlying().DoInit(_device_ordinal);
initialised_ = true;
}
/**
* \brief Allgather implemented as grouped calls to Broadcast. This way we can accept
* \brief Allgather implemented as grouped calls to Broadcast. This way we can accept
* different size of data on different workers.
*
* \param data Buffer storing the input data.
* \param length_bytes Size of input data in bytes.
* \param segments Size of data on each worker.
* \param recvbuf Buffer storing the result of data from all workers.
*/
void AllGather(void const* data, size_t length_bytes,
std::vector<size_t>* segments, dh::caching_device_vector<char>* recvbuf);
void AllGather(uint32_t const* data, size_t length,
dh::caching_device_vector<uint32_t>* recvbuf) {
#ifdef XGBOOST_USE_NCCL
void AllGather(void const *data, size_t length_bytes, std::vector<size_t> *segments,
dh::caching_device_vector<char> *recvbuf) {
CHECK(initialised_);
size_t world = rabit::GetWorldSize();
recvbuf->resize(length * world);
safe_nccl(ncclAllGather(data, recvbuf->data().get(), length, ncclUint32,
comm_, stream_));
#endif // XGBOOST_USE_NCCL
dh::safe_cuda(cudaSetDevice(device_ordinal_));
this->Underlying().DoAllGather(data, length_bytes, segments, recvbuf);
}
/**
* \brief Allgather. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* \param data Buffer storing the input data.
* \param length Size of input data in bytes.
* \param recvbuf Buffer storing the result of data from all workers.
*/
void AllGather(uint32_t const *data, size_t length,
dh::caching_device_vector<uint32_t> *recvbuf) {
CHECK(initialised_);
dh::safe_cuda(cudaSetDevice(device_ordinal_));
this->Underlying().DoAllGather(data, length, recvbuf);
}
/**
@ -813,15 +797,28 @@ class AllReducer {
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void AllReduceSum(const float *sendbuff, float *recvbuff, int count) {
#ifdef XGBOOST_USE_NCCL
void AllReduceSum(const double *sendbuff, double *recvbuff, int count) {
CHECK(initialised_);
dh::safe_cuda(cudaSetDevice(device_ordinal_));
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclFloat, ncclSum, comm_, stream_));
this->Underlying().DoAllReduceSum(sendbuff, recvbuff, count);
allreduce_bytes_ += count * sizeof(double);
allreduce_calls_ += 1;
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void AllReduceSum(const float *sendbuff, float *recvbuff, int count) {
CHECK(initialised_);
dh::safe_cuda(cudaSetDevice(device_ordinal_));
this->Underlying().DoAllReduceSum(sendbuff, recvbuff, count);
allreduce_bytes_ += count * sizeof(float);
allreduce_calls_ += 1;
#endif
}
/**
@ -833,48 +830,68 @@ class AllReducer {
* \param recvbuff The recvbuff.
* \param count Number of.
*/
void AllReduceSum(const int64_t *sendbuff, int64_t *recvbuff, int count) {
#ifdef XGBOOST_USE_NCCL
CHECK(initialised_);
dh::safe_cuda(cudaSetDevice(device_ordinal_));
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclInt64, ncclSum, comm_, stream_));
#endif
this->Underlying().DoAllReduceSum(sendbuff, recvbuff, count);
allreduce_bytes_ += count * sizeof(int64_t);
allreduce_calls_ += 1;
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void AllReduceSum(const uint32_t *sendbuff, uint32_t *recvbuff, int count) {
#ifdef XGBOOST_USE_NCCL
CHECK(initialised_);
dh::safe_cuda(cudaSetDevice(device_ordinal_));
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclUint32, ncclSum, comm_, stream_));
#endif
this->Underlying().DoAllReduceSum(sendbuff, recvbuff, count);
allreduce_bytes_ += count * sizeof(uint32_t);
allreduce_calls_ += 1;
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void AllReduceSum(const uint64_t *sendbuff, uint64_t *recvbuff, int count) {
#ifdef XGBOOST_USE_NCCL
CHECK(initialised_);
dh::safe_cuda(cudaSetDevice(device_ordinal_));
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclUint64, ncclSum, comm_, stream_));
#endif
this->Underlying().DoAllReduceSum(sendbuff, recvbuff, count);
allreduce_bytes_ += count * sizeof(uint64_t);
allreduce_calls_ += 1;
}
// Specialization for size_t, which is implementation defined so it might or might not
// be one of uint64_t/uint32_t/unsigned long long/unsigned long.
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* Specialization for size_t, which is implementation defined so it might or might not
* be one of uint64_t/uint32_t/unsigned long long/unsigned long.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
template <typename T = size_t,
std::enable_if_t<std::is_same<size_t, T>::value &&
!std::is_same<size_t, unsigned long long>::value> // NOLINT
* = nullptr>
void AllReduceSum(const T *sendbuff, T *recvbuff, int count) { // NOLINT
#ifdef XGBOOST_USE_NCCL
void AllReduceSum(const T *sendbuff, T *recvbuff, int count) { // NOLINT
CHECK(initialised_);
dh::safe_cuda(cudaSetDevice(device_ordinal_));
static_assert(sizeof(unsigned long long) == sizeof(uint64_t), ""); // NOLINT
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclUint64, ncclSum, comm_, stream_));
#endif
static_assert(sizeof(unsigned long long) == sizeof(uint64_t), ""); // NOLINT
this->Underlying().DoAllReduceSum(sendbuff, recvbuff, count);
allreduce_bytes_ += count * sizeof(T);
allreduce_calls_ += 1;
}
/**
@ -883,13 +900,148 @@ class AllReducer {
* \brief Synchronizes the entire communication group.
*/
void Synchronize() {
#ifdef XGBOOST_USE_NCCL
CHECK(initialised_);
dh::safe_cuda(cudaSetDevice(device_ordinal_));
dh::safe_cuda(cudaStreamSynchronize(stream_));
#endif
};
this->Underlying().DoSynchronize();
}
protected:
bool initialised_{false};
size_t allreduce_bytes_{0}; // Keep statistics of the number of bytes communicated.
size_t allreduce_calls_{0}; // Keep statistics of the number of reduce calls.
private:
int device_ordinal_{-1};
};
#ifdef XGBOOST_USE_NCCL
class NcclAllReducer : public AllReducerBase<NcclAllReducer> {
public:
friend class AllReducerBase<NcclAllReducer>;
~NcclAllReducer() override;
private:
/**
* \brief Initialise with the desired device ordinal for this communication
* group.
*
* \param device_ordinal The device ordinal.
*/
void DoInit(int _device_ordinal);
/**
* \brief Allgather implemented as grouped calls to Broadcast. This way we can accept
* different size of data on different workers.
*
* \param data Buffer storing the input data.
* \param length_bytes Size of input data in bytes.
* \param segments Size of data on each worker.
* \param recvbuf Buffer storing the result of data from all workers.
*/
void DoAllGather(void const *data, size_t length_bytes, std::vector<size_t> *segments,
dh::caching_device_vector<char> *recvbuf);
/**
* \brief Allgather. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* \param data Buffer storing the input data.
* \param length Size of input data in bytes.
* \param recvbuf Buffer storing the result of data from all workers.
*/
void DoAllGather(uint32_t const *data, size_t length,
dh::caching_device_vector<uint32_t> *recvbuf) {
size_t world = rabit::GetWorldSize();
recvbuf->resize(length * world);
safe_nccl(ncclAllGather(data, recvbuf->data().get(), length, ncclUint32, comm_, stream_));
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void DoAllReduceSum(const double *sendbuff, double *recvbuff, int count) {
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclDouble, ncclSum, comm_, stream_));
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void DoAllReduceSum(const float *sendbuff, float *recvbuff, int count) {
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclFloat, ncclSum, comm_, stream_));
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing streams or comms.
*
* \param count Number of.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of.
*/
void DoAllReduceSum(const int64_t *sendbuff, int64_t *recvbuff, int count) {
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclInt64, ncclSum, comm_, stream_));
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void DoAllReduceSum(const uint32_t *sendbuff, uint32_t *recvbuff, int count) {
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclUint32, ncclSum, comm_, stream_));
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void DoAllReduceSum(const uint64_t *sendbuff, uint64_t *recvbuff, int count) {
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclUint64, ncclSum, comm_, stream_));
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL but without needing
* streams or comms.
*
* Specialization for size_t, which is implementation defined so it might or might not
* be one of uint64_t/uint32_t/unsigned long long/unsigned long.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
template <typename T = size_t,
std::enable_if_t<std::is_same<size_t, T>::value &&
!std::is_same<size_t, unsigned long long>::value> // NOLINT
* = nullptr>
void DoAllReduceSum(const T *sendbuff, T *recvbuff, int count) { // NOLINT
dh::safe_nccl(ncclAllReduce(sendbuff, recvbuff, count, ncclUint64, ncclSum, comm_, stream_));
}
/**
* \brief Synchronizes the entire communication group.
*/
void DoSynchronize() { dh::safe_cuda(cudaStreamSynchronize(stream_)); }
/**
* \fn ncclUniqueId GetUniqueId()
*
@ -904,15 +1056,163 @@ class AllReducer {
if (rabit::GetRank() == kRootRank) {
dh::safe_nccl(ncclGetUniqueId(&id));
}
rabit::Broadcast(
static_cast<void*>(&id),
sizeof(ncclUniqueId),
static_cast<int>(kRootRank));
rabit::Broadcast(static_cast<void *>(&id), sizeof(ncclUniqueId), static_cast<int>(kRootRank));
return id;
}
#endif
ncclComm_t comm_;
cudaStream_t stream_;
ncclUniqueId id_;
};
using AllReducer = NcclAllReducer;
#else
class RabitAllReducer : public AllReducerBase<RabitAllReducer> {
public:
friend class AllReducerBase<RabitAllReducer>;
private:
/**
* \brief Initialise with the desired device ordinal for this allreducer.
*
* \param device_ordinal The device ordinal.
*/
static void DoInit(int _device_ordinal);
/**
* \brief Allgather implemented as grouped calls to Broadcast. This way we can accept
* different size of data on different workers.
*
* \param data Buffer storing the input data.
* \param length_bytes Size of input data in bytes.
* \param segments Size of data on each worker.
* \param recvbuf Buffer storing the result of data from all workers.
*/
void DoAllGather(void const *data, size_t length_bytes, std::vector<size_t> *segments,
dh::caching_device_vector<char> *recvbuf);
/**
* \brief Allgather. Use in exactly the same way as NCCL.
*
* \param data Buffer storing the input data.
* \param length Size of input data in bytes.
* \param recvbuf Buffer storing the result of data from all workers.
*/
void DoAllGather(uint32_t *data, size_t length, dh::caching_device_vector<uint32_t> *recvbuf) {
size_t world = rabit::GetWorldSize();
auto total_size = length * world;
recvbuf->resize(total_size);
sendrecvbuf_.reserve(total_size);
auto rank = rabit::GetRank();
safe_cuda(cudaMemcpy(sendrecvbuf_.data() + rank * length, data, length, cudaMemcpyDefault));
rabit::Allgather(sendrecvbuf_.data(), total_size, rank * length, length, length);
safe_cuda(cudaMemcpy(data, sendrecvbuf_.data(), total_size, cudaMemcpyDefault));
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void DoAllReduceSum(const double *sendbuff, double *recvbuff, int count) {
RabitAllReduceSum(sendbuff, recvbuff, count);
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void DoAllReduceSum(const float *sendbuff, float *recvbuff, int count) {
RabitAllReduceSum(sendbuff, recvbuff, count);
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void DoAllReduceSum(const int64_t *sendbuff, int64_t *recvbuff, int count) {
RabitAllReduceSum(sendbuff, recvbuff, count);
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void DoAllReduceSum(const uint32_t *sendbuff, uint32_t *recvbuff, int count) {
RabitAllReduceSum(sendbuff, recvbuff, count);
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
void DoAllReduceSum(const uint64_t *sendbuff, uint64_t *recvbuff, int count) {
RabitAllReduceSum(sendbuff, recvbuff, count);
}
/**
* \brief Allreduce. Use in exactly the same way as NCCL.
*
* Specialization for size_t, which is implementation defined so it might or might not
* be one of uint64_t/uint32_t/unsigned long long/unsigned long.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
template <typename T = size_t,
std::enable_if_t<std::is_same<size_t, T>::value &&
!std::is_same<size_t, unsigned long long>::value> // NOLINT
* = nullptr>
void DoAllReduceSum(const T *sendbuff, T *recvbuff, int count) { // NOLINT
RabitAllReduceSum(sendbuff, recvbuff, count);
}
/**
* \brief Synchronizes the allreducer.
*/
void DoSynchronize() {}
/**
* \brief Allreduce. Use in exactly the same way as NCCL.
*
* Copy the device buffer to host, call rabit allreduce, then copy the buffer back
* to device.
*
* \param sendbuff The sendbuff.
* \param recvbuff The recvbuff.
* \param count Number of elements.
*/
template <typename T>
void RabitAllReduceSum(const T *sendbuff, T *recvbuff, int count) {
auto total_size = count * sizeof(T);
sendrecvbuf_.reserve(total_size);
safe_cuda(cudaMemcpy(sendrecvbuf_.data(), sendbuff, total_size, cudaMemcpyDefault));
rabit::Allreduce<rabit::op::Sum>(reinterpret_cast<T*>(sendrecvbuf_.data()), count);
safe_cuda(cudaMemcpy(recvbuff, sendrecvbuf_.data(), total_size, cudaMemcpyDefault));
}
/// Host buffer used to call rabit functions.
std::vector<char> sendrecvbuf_{};
};
using AllReducer = RabitAllReducer;
#endif
template <typename VectorT, typename T = typename VectorT::value_type,
typename IndexT = typename xgboost::common::Span<T>::index_type>
xgboost::common::Span<T> ToSpan(

10
tests/cpp/common/test_quantile.cu
View File

@ -339,7 +339,6 @@ TEST(GPUQuantile, MultiMerge) {
TEST(GPUQuantile, AllReduceBasic) {
// This test is supposed to run by a python test that setups the environment.
std::string msg {"Skipping AllReduce test"};
#if defined(__linux__) && defined(XGBOOST_USE_NCCL)
auto n_gpus = AllVisibleGPUs();
InitRabitContext(msg, n_gpus);
auto world = rabit::GetWorldSize();
@ -420,15 +419,10 @@ TEST(GPUQuantile, AllReduceBasic) {
}
});
rabit::Finalize();
#else
LOG(WARNING) << msg;
return;
#endif // !defined(__linux__) && defined(XGBOOST_USE_NCCL)
}
TEST(GPUQuantile, SameOnAllWorkers) {
std::string msg {"Skipping SameOnAllWorkers test"};
#if defined(__linux__) && defined(XGBOOST_USE_NCCL)
auto n_gpus = AllVisibleGPUs();
InitRabitContext(msg, n_gpus);
auto world = rabit::GetWorldSize();
@ -495,10 +489,6 @@ TEST(GPUQuantile, SameOnAllWorkers) {
offset += size_as_float;
}
});
#else
LOG(WARNING) << msg;
return;
#endif // !defined(__linux__) && defined(XGBOOST_USE_NCCL)
}
TEST(GPUQuantile, Push) {

4
tests/cpp/common/test_transform_range.cu
View File

@ -4,7 +4,6 @@
*/
#include "test_transform_range.cc"
#if defined(XGBOOST_USE_NCCL)
namespace xgboost {
namespace common {
@ -15,7 +14,7 @@ TEST(Transform, MGPU_SpecifiedGpuId) { // NOLINT
}
// Use 1 GPU, Numbering of GPU starts from 1
auto device = 1;
const size_t size {256};
auto const size {256};
std::vector<bst_float> h_in(size);
std::vector<bst_float> h_out(size);
std::iota(h_in.begin(), h_in.end(), 0);
@ -34,4 +33,3 @@ TEST(Transform, MGPU_SpecifiedGpuId) { // NOLINT
} // namespace common
} // namespace xgboost
#endif

4
tests/cpp/metric/test_multiclass_metric.cc
View File

@ -85,7 +85,7 @@ TEST(Metric, DeclareUnifiedTest(MultiClassLogLoss)) {
xgboost::CheckDeterministicMetricMultiClass(xgboost::StringView{"mlogloss"}, GPUIDX);
}
#if defined(XGBOOST_USE_NCCL) && defined(__CUDACC__)
#if defined(__CUDACC__)
namespace xgboost {
namespace common {
TEST(Metric, MGPU_MultiClassError) {
@ -109,4 +109,4 @@ TEST(Metric, MGPU_MultiClassError) {
}
} // namespace common
} // namespace xgboost
#endif // defined(XGBOOST_USE_NCCL)
#endif // defined(__CUDACC__)

8
tests/distributed/runtests-federated.sh
View File

@ -4,14 +4,14 @@ set -e
rm -f ./*.model* ./agaricus* ./*.pem
world_size=3
world_size=$(nvidia-smi -L | wc -l)
# Generate server and client certificates.
openssl req -x509 -newkey rsa:2048 -days 7 -nodes -keyout server-key.pem -out server-cert.pem -subj "/C=US/CN=localhost"
openssl req -x509 -newkey rsa:2048 -days 7 -nodes -keyout client-key.pem -out client-cert.pem -subj "/C=US/CN=localhost"
# Split train and test files manually to simulate a federated environment.
split -n l/${world_size} -d ../../demo/data/agaricus.txt.train agaricus.txt.train-
split -n l/${world_size} -d ../../demo/data/agaricus.txt.test agaricus.txt.test-
split -n l/"${world_size}" -d ../../demo/data/agaricus.txt.train agaricus.txt.train-
split -n l/"${world_size}" -d ../../demo/data/agaricus.txt.test agaricus.txt.test-
python test_federated.py ${world_size}
python test_federated.py "${world_size}"

10
tests/distributed/test_federated.py
View File

@ -17,7 +17,7 @@ def run_server(port: int, world_size: int) -> None:
CLIENT_CERT)
def run_worker(port: int, world_size: int, rank: int) -> None:
def run_worker(port: int, world_size: int, rank: int, with_gpu: bool) -> None:
# Always call this before using distributed module
rabit_env = [
f'federated_server_address=localhost:{port}',
@ -34,6 +34,9 @@ def run_worker(port: int, world_size: int, rank: int) -> None:
# Specify parameters via map, definition are same as c++ version
param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
if with_gpu:
param['tree_method'] = 'gpu_hist'
param['gpu_id'] = rank
# Specify validations set to watch performance
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
@ -49,7 +52,7 @@ def run_worker(port: int, world_size: int, rank: int) -> None:
xgb.rabit.tracker_print("Finished training\n")
def run_test() -> None:
def run_test(with_gpu: bool = False) -> None:
port = 9091
world_size = int(sys.argv[1])
@ -61,7 +64,7 @@ def run_test() -> None:
workers = []
for rank in range(world_size):
worker = multiprocessing.Process(target=run_worker, args=(port, world_size, rank))
worker = multiprocessing.Process(target=run_worker, args=(port, world_size, rank, with_gpu))
workers.append(worker)
worker.start()
for worker in workers:
@ -71,3 +74,4 @@ def run_test() -> None:
if __name__ == '__main__':
run_test()
run_test(with_gpu=True)