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This is to implement group_split as proposed in [docs.google.com/document/d/13R-1t_yESTvmAjcCN-wQjQQadIEu0JNIdS65uZawZzY/edit?tab=t.0#heading=h.3ctbqqopzc89](https://docs.google.com/document/d/13R-1t_yESTvmAjcCN-wQjQQadIEu0JNIdS65uZawZzY/edit?tab=t.0#heading=h.3ctbqqopzc89) Pull Request resolved: https://github.com/pytorch/pytorch/pull/157716 Approved by: https://github.com/d4l3k
398 lines
12 KiB
C++
398 lines
12 KiB
C++
#include <ATen/ThreadLocalState.h>
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#include <torch/csrc/distributed/c10d/ProcessGroup.hpp>
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#include <torch/csrc/distributed/c10d/RankLocal.hpp>
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#include <c10/util/Logging.h>
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#include <fmt/format.h>
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#include <fmt/ranges.h>
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#include <string_view>
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#include <torch/csrc/distributed/c10d/PrefixStore.hpp>
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#include <torch/csrc/distributed/c10d/ProcessGroupGloo.hpp>
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#include <torch/csrc/distributed/c10d/ProcessGroupMPI.hpp>
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#include <torch/csrc/distributed/c10d/ProcessGroupNCCL.hpp>
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#include <torch/csrc/distributed/c10d/ProcessGroupUCC.hpp>
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#include <torch/csrc/distributed/c10d/ProcessGroupWrapper.hpp>
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namespace c10d {
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std::string opTypeToString(OpType opType) {
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switch (opType) {
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case OpType::BROADCAST:
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return "BROADCAST";
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case OpType::ALLREDUCE:
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return "ALLREDUCE";
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case OpType::ALLREDUCE_COALESCED:
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return "ALLREDUCE_COALESCED";
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case OpType::REDUCE:
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return "REDUCE";
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case OpType::ALLGATHER:
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return "ALLGATHER";
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case OpType::_ALLGATHER_BASE:
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return "_ALLGATHER_BASE";
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case OpType::ALLGATHER_COALESCED:
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return "ALLGATHER_COALESCED";
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case OpType::GATHER:
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return "GATHER";
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case OpType::SCATTER:
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return "SCATTER";
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case OpType::REDUCE_SCATTER:
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return "REDUCE_SCATTER";
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case OpType::ALLTOALL_BASE:
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return "ALLTOALL_BASE";
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case OpType::ALLTOALL:
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return "ALLTOALL";
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case OpType::SEND:
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return "SEND";
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case OpType::RECV:
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return "RECV";
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case OpType::RECVANYSOURCE:
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return "RECVANYSOURCE";
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case OpType::BARRIER:
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return "BARRIER";
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case OpType::UNKNOWN:
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return "UNKNOWN";
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case OpType::_REDUCE_SCATTER_BASE:
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return "_REDUCE_SCATTER_BASE";
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case OpType::COALESCED:
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return "COALESCED";
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case OpType::_ALLREDUCE_SPARSE:
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return "_ALLREDUCE_SPARSE";
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default:
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TORCH_INTERNAL_ASSERT(false, "Unknown op type!");
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}
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return "UNKNOWN";
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}
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bool isP2POp(OpType opType, bool batchP2P /*= false*/) {
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if (batchP2P)
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return false;
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return opType == OpType::SEND || opType == OpType::RECV ||
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opType == OpType::RECVANYSOURCE;
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}
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c10::intrusive_ptr<Backend> ProcessGroup::getBackend(
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c10::DeviceType deviceType) {
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// If there is a backend associated with this device type then return it
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if (deviceTypeToBackend_.find(deviceType) != deviceTypeToBackend_.end()) {
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return deviceTypeToBackend_.at(deviceType);
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}
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// Get the backend type associated with the device
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ProcessGroup::BackendType backendType{ProcessGroup::BackendType::UNDEFINED};
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try {
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backendType = deviceTypeToBackendType_.at(deviceType);
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} catch (const std::out_of_range& e) {
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TORCH_CHECK(
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false, "No backend type associated with device type ", deviceType);
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}
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// Check if the backend has already been initialized
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if (backendTypeToBackend_.find(backendType) != backendTypeToBackend_.end()) {
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auto backend = backendTypeToBackend_.at(backendType);
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deviceTypeToBackend_[deviceType] = backend;
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return backend;
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}
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TORCH_CHECK(
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false,
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"Could not retrieve or create the backend ",
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backendType,
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" for device type ",
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deviceType);
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}
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ProcessGroup::ProcessGroup(
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c10::intrusive_ptr<::c10d::Store> store,
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int rank,
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int size)
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: store_(std::move(store)),
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rank_(rank),
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size_(size),
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backendType_(BackendType::UNDEFINED),
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dist_debug_level_(debug_level()) {
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C10_LOG_API_USAGE_ONCE("c10d.process_group");
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}
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ProcessGroup::ProcessGroup(int rank, int size)
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: rank_(rank), size_(size), backendType_(BackendType::UNDEFINED) {}
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ProcessGroup::~ProcessGroup() = default;
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void ProcessGroup::init() {
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C10_LOG_API_USAGE_ONCE(
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fmt::format("c10d.process_group_{}", getBackendName()));
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}
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const std::string& ProcessGroup::getGroupName() const {
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TORCH_CHECK(!deviceTypeToBackend_.empty(), "ProcessGroup name not set");
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return deviceTypeToBackend_.begin()->second->getGroupUid();
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}
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void ProcessGroup::setGroupName(const std::string& name) {
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for (auto& kv : deviceTypeToBackend_) {
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kv.second->setGroupUid(name);
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}
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}
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const std::string& ProcessGroup::getGroupDesc() const {
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return pg_desc_;
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}
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void ProcessGroup::setGroupDesc(const std::string& name) {
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pg_desc_ = name;
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// Also set the group desc for all backends
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for (auto& kv : deviceTypeToBackend_) {
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kv.second->setGroupDesc(name);
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}
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}
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void ProcessGroup::enableCollectivesTiming() {
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for (auto& kv : deviceTypeToBackend_) {
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kv.second->enableCollectivesTiming();
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}
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}
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void ProcessGroup::release_resources() {
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store_.reset();
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deviceTypeToBackend_.clear();
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backendTypeToBackend_.clear();
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}
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c10::intrusive_ptr<ProcessGroup> ProcessGroup::splitGroup(
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const std::vector<int>& ranks,
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const std::optional<std::chrono::milliseconds> timeout,
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const std::optional<c10::intrusive_ptr<Backend::Options>> opts,
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const std::optional<std::string>& desc) {
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TORCH_CHECK(
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ranks.size() > 0,
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"Split ranks cannot be empty. Please provide a non-empty list of ranks to split the group.");
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TORCH_CHECK(
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ranks.size() < static_cast<size_t>(size_),
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"the split group's size should be less than the world_size set by init_process_group");
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std::set<int> ranks_set(ranks.begin(), ranks.end());
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TORCH_CHECK(
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ranks_set.size() == ranks.size(),
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"Split ranks should not have duplicates. Please provide a list of unique ranks to split the group.");
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std::vector<int> sorted_ranks = ranks;
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std::sort(sorted_ranks.begin(), sorted_ranks.end());
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c10::intrusive_ptr<ProcessGroup> newGroup;
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// TODO: Figure out a better way for split group name.
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std::string groupName =
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c10::str(getGroupName(), ":split:", fmt::format("{}", sorted_ranks));
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for (const auto& pair : deviceTypeToBackendType_) {
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c10::DeviceType deviceType = pair.first;
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BackendType backendType = pair.second;
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auto parentBackend = getBackend(deviceType);
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auto backendOpts =
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opts.has_value() ? opts.value() : parentBackend->getBackendOptions();
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backendOpts->group_name = groupName;
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backendOpts->timeout =
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timeout.has_value() ? timeout.value() : backendOpts->timeout;
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auto splitBackend = parentBackend->splitBackend(sorted_ranks, backendOpts);
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if (splitBackend == nullptr) {
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continue;
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}
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// TODO: Figure out a better way for split group desc.
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// TODO: We can add a new field in Backend::Options to specify the group
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// desc
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std::string groupDesc = desc.has_value()
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? desc.value()
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: c10::str(getGroupDesc(), ":split:", incrementSplitCount());
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splitBackend->setGroupDesc(groupDesc);
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if (!newGroup) {
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newGroup = c10::make_intrusive<ProcessGroup>(
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store_->clone(), splitBackend->getRank(), splitBackend->getSize());
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newGroup->setDefaultBackend(backendType_);
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newGroup->setGroupName(groupName);
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newGroup->setGroupDesc(groupDesc);
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}
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newGroup->setBackend(deviceType, backendType, splitBackend);
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}
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return newGroup;
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}
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} // namespace c10d
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namespace {
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class WorkRegistry {
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public:
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void register_work(
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const at::Tensor& tensor,
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const c10::intrusive_ptr<c10d::Work>& work) {
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if (!tensor.has_storage()) {
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TORCH_WARN_ONCE(
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"Registering collective work for tensor without storage is not supported. "
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"Calling c10d_functional.wait_tensor() on this tensor will not wait for the collective to complete. "
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"Unsupported tensor type: " +
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tensor.toString());
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return;
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}
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auto storage = tensor.storage().getWeakStorageImpl();
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std::unique_lock lock(lock_);
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auto it = registry_.find(storage);
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if (it == registry_.end()) {
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registry_.emplace(
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std::move(storage),
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std::vector<c10::intrusive_ptr<c10d::Work>>{work});
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} else {
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// There is no guarantee that the previous work object for this
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// tensor storage is completed before the new work object is registered.
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// Therefore we need to maintain a list of work objects for each tensor
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// storage.
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// Check if work is already in the list
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bool work_exists = false;
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for (const auto& existing_work : it->second) {
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if (existing_work == work) {
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work_exists = true;
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break;
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}
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}
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// Only append if work is not already in the list
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if (!work_exists) {
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it->second.push_back(work);
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}
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}
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}
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std::vector<c10::intrusive_ptr<c10d::Work>> pop_works(
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const at::Tensor& tensor) {
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const auto storage = tensor.storage().getWeakStorageImpl();
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std::unique_lock lock(lock_);
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auto it = registry_.find(storage);
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if (it == registry_.end()) {
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return {};
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}
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auto works = it->second;
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registry_.erase(it);
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return works;
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}
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void unregister_work(const c10::intrusive_ptr<c10d::Work>& work) {
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std::unique_lock lock(lock_);
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for (auto it = registry_.begin(); it != registry_.end();) {
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std::vector<c10::intrusive_ptr<c10d::Work>> nonmatching_works;
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for (const auto& _work : it->second) {
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if (_work != work) {
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nonmatching_works.push_back(_work);
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}
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}
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if (nonmatching_works.empty()) {
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it = registry_.erase(it);
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} else {
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it->second = std::move(nonmatching_works);
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++it;
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}
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}
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}
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size_t get_work_registry_size() {
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std::unique_lock lock(lock_);
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size_t total_size = 0;
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for (const auto& [storage, works] : registry_) {
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total_size += works.size();
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}
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return total_size;
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}
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void set_allow_inflight_collective_as_graph_input(bool value) {
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std::unique_lock lock(lock_);
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allow_inflight_collective_as_graph_input_ = value;
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}
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bool allow_inflight_collective_as_graph_input() {
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std::unique_lock lock(lock_);
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return allow_inflight_collective_as_graph_input_;
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}
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~WorkRegistry() {
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// If there are still unwaited work objects, their corresponding process
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// groups should have already been destroyed at this stage. Any attempts to
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// wait for these work objects or to destroy them will only result in
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// confusing errors. Therefore, we simply issue a warning and intentionally
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// allow the unwaited work objects to leak.
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size_t registry_size = get_work_registry_size();
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if (registry_size > 0) {
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TORCH_WARN(
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"At the time of process termination, there are still ",
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registry_size,
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" unwaited collective calls. "
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"Please review your program to ensure that:\n"
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"1. c10d_functional.wait_tensor() is invoked on all tensors returned from c10d_functional collective,\n"
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"2. c10d_functional.wait_tensor() is invoked on all output tensors of async_op=True torch.distributed collective "
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"called under `with allow_inflight_collective_as_graph_input_ctx():`,\n"
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"before the output tensors of the collective are used.");
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}
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for (auto& it : registry_) {
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for (auto& work : it.second) {
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work.release();
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}
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}
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}
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private:
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std::unordered_map<
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c10::weak_intrusive_ptr<c10::StorageImpl>,
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std::vector<c10::intrusive_ptr<c10d::Work>>>
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registry_;
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bool allow_inflight_collective_as_graph_input_ = false;
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std::mutex lock_;
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};
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static WorkRegistry process_registry;
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} // namespace
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namespace c10d {
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void register_work(
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const at::Tensor& tensor,
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const c10::intrusive_ptr<c10d::Work>& work) {
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RankLocal<WorkRegistry>::get().register_work(tensor, work);
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}
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at::Tensor wait_tensor(const at::Tensor& tensor) {
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auto works = RankLocal<WorkRegistry>::get().pop_works(tensor);
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for (const auto& work : works) {
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work->wait();
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}
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return tensor;
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}
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void unregister_work(const c10::intrusive_ptr<c10d::Work>& work) {
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RankLocal<WorkRegistry>::get().unregister_work(work);
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}
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size_t get_work_registry_size() {
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return RankLocal<WorkRegistry>::get().get_work_registry_size();
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}
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void set_allow_inflight_collective_as_graph_input(bool value) {
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return RankLocal<WorkRegistry>::get()
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.set_allow_inflight_collective_as_graph_input(value);
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}
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bool allow_inflight_collective_as_graph_input() {
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return RankLocal<WorkRegistry>::get()
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.allow_inflight_collective_as_graph_input();
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}
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c10::intrusive_ptr<ProcessGroup>& currentProcessGroup() {
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thread_local static c10::intrusive_ptr<ProcessGroup> pg = nullptr;
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return pg;
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}
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void setProcessGroup(c10::intrusive_ptr<ProcessGroup> pg) {
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currentProcessGroup() = std::move(pg);
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}
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} // namespace c10d
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