commux 0.2.1

UCX tag-matching (MPI-style tags) c10d backend for torch.distributed, on CPU and CUDA

commux

A custom PyTorch c10d backend over UCX that gives real MPI-style (sender, tag) point-to-point matching — the thing NCCL cannot do — plus allreduce / reduce / broadcast / barrier, on CPU host tensors and CUDA device tensors (UCX cuda_ipc / cuda_copy / gdr_copy).

Motivation: codes that drive stencil/halo exchange through tagged c10d send/recv (pg.send(bufs, dst, tag)) can't use NCCL for it — NCCL ignores tags and matches only by stream/communicator ordering. commux honors tags via UCX's ucp_tag_send_nbx / ucp_tag_recv_nbx.

Install

commux is Linux-only (it builds on UCX, which does not compile on macOS) and needs UCX — but you do not install UCX yourself: every wheel vendors UCX (+ the gdrcopy userspace lib), so a pip install is self-contained.

# Released wheel (self-contained: bundled UCX, no system UCX needed):
pip install commux
pip install "commux @ git+https://github.com/zoeyzyhu/commux"

# From a source checkout -- builds against your active torch, so no isolation:
pip install . --no-build-isolation

# Use a preinstalled / module UCX instead of building one from source:
UCX_ROOT=/path/to/ucx pip install . --no-build-isolation \
  -C cmake.define.COMMUX_UCX_PROVIDER=system

# Force building UCX(+gdrcopy) from source even if a system one exists:
pip install . --no-build-isolation -C cmake.define.COMMUX_UCX_PROVIDER=bundled

The installed package under site-packages/commux/ is fully self-contained: _C.so (the extension), lib/libcommux.so plus the vendored lib/libuc*.so / libgdrapi.so (relocatable — repointed to $ORIGIN), and include/ with the commux and UCX C++ headers for downstream C++ consumers (see below).

gdr_copy only engages at runtime when the gdrdrv kernel module is loaded (/dev/gdrdrv) and an RDMA NIC is present; otherwise UCX uses cuda_copy/cuda_ipc. The kernel module is a host/driver prerequisite that a wheel cannot provide.

Use with torch.distributed

import torch, torch.distributed as dist
import commux; commux.register()          # registers backends "ucx" and "commux"

dist.init_process_group(backend="ucx", init_method="env://")
dist.all_reduce(t)                          # CPU or CUDA
dist.isend(x, dst=1, tag=100); dist.irecv(y, src=0, tag=100)

Launch with torchrun --nproc-per-node=N ... as usual.

Out-of-order tag matching must use non-blocking isend/irecv (post all, then wait). Blocking out-of-order send/recv deadlocks under the CUDA rendezvous protocol, exactly as with real MPI.

Picking a backend portably (replace Gloo where UCX exists)

commux works on CPU as well as CUDA, so on Linux you can use it instead of Gloo for CPU collectives/p2p. Since commux installs only where UCX is supported (Linux), the import itself is the availability check — prefer it, and fall back to Gloo elsewhere (e.g. macOS):

try:
    from commux import preferred_backend
    backend = preferred_backend()   # registers commux, returns "ucx"
except ImportError:
    backend = "gloo"                # macOS / no UCX
dist.init_process_group(backend=backend, init_method="env://")

The fallback lives in the consumer because commux is absent on the unsupported platform; commux itself is the UCX provider on both CPU and CUDA.

Use from C++

Because the wheel ships the C++ library + headers (and bundles UCX), a C++ project can just pip install commux and link the installed package — no source build, no UCX of its own. Locate it from CMake by probing the Python package:

execute_process(
  COMMAND ${Python3_EXECUTABLE} -c "import commux,os;print(os.path.dirname(commux.__file__))"
  OUTPUT_VARIABLE COMMUX_DIR OUTPUT_STRIP_TRAILING_WHITESPACE)
find_library(COMMUX_LIBRARY commux HINTS ${COMMUX_DIR}/lib)
include_directories(${COMMUX_DIR}/include)  # commux/*.hpp + bundled ucp/*.h
link_directories(${COMMUX_DIR}/lib)         # lets the linker resolve bundled UCX
target_link_libraries(your_lib PUBLIC ${COMMUX_LIBRARY})

#include <commux/process_group_ucx.hpp>
auto backend = c10::make_intrusive<commux::ProcessGroupUCX>(store, rank, size);
pg->setBackend(c10::DeviceType::CPU,  c10d::ProcessGroup::BackendType::CUSTOM, backend);
pg->setBackend(c10::DeviceType::CUDA, c10d::ProcessGroup::BackendType::CUSTOM, backend);

Prefer to build from source? commux also exports the CMake target commux::commux, so FetchContent works too:

include(FetchContent)
FetchContent_Declare(commux GIT_REPOSITORY https://github.com/zoeyzyhu/commux GIT_TAG main)
FetchContent_MakeAvailable(commux)
target_link_libraries(your_lib PUBLIC commux::commux)

Build the C++ tests

cmake -S . -B build -DCOMMUX_BUILD_TESTS=ON      # add -DUCX_ROOT=~/ucx-install to use a prebuilt UCX
cmake --build build -j
for r in 0 1; do ./build/test_commux $r 2 127.0.0.1 29581 & done; wait        # CPU
UCXPG_DEVICE=cuda ./build/test_commux 0 2 127.0.0.1 29592 & \
UCXPG_DEVICE=cuda ./build/test_commux 1 2 127.0.0.1 29592 & wait              # CUDA

test_commux also covers the coalescing features: it exercises the IOV path under COMMUX_COALESCE=1 and the coalescing window under COMMUX_GROUP=1. Set COMMUX_BENCH=1 COMMUX_BENCH_V=<N> for a V-tensor ping-pong latency benchmark (compare COMMUX_COALESCE=0 vs 1).

CMake options

option	default	meaning
COMMUX_UCX_PROVIDER	auto	auto (system, else build) / system / bundled
COMMUX_WITH_GDRCOPY	auto	build gdrcopy into bundled UCX (auto/on/off)
COMMUX_UCX_VERSION	1.18.0	UCX release to bundle
COMMUX_CUDA	ON	enable CUDA path if c10_cuda is present
COMMUX_BUILD_TESTS	OFF	build test_commux
COMMUX_BUILD_PYTHON	OFF	build the commux._C extension (set by the wheel)

scripts/build_ucx.sh [PREFIX] [UCX_VER] [GDR_VER] builds UCX+gdrcopy once with the same flags, for pointing UCX_ROOT at.

Runtime tuning (environment variables)

Read once at process start; opt-in and off by default, so default behavior is unchanged.

variable	default	meaning
COMMUX_COALESCE	off	Merge a multi-tensor send()/recv() vector (tensors.size() > 1, all on one memory type) into one UCX message via the IOV datatype — one rendezvous / tag-match / stream-sync instead of V. Changes the wire format, so every rank must set it the same way. 1/on to enable.
COMMUX_GROUP	off	Enable the coalescing window — the c10d startCoalescing/endCoalescing hooks, i.e. the analog of ncclGroupStart/ncclGroupEnd. Between the markers, send/recv defer their posts and are flushed together with a single stream-sync at endCoalescing. Wire-compatible with a non-grouping peer. 1/on to enable.

Always on (no flag): wait paths are event-driven — a Work::wait() sleeps on the worker's wakeup fd (UCP_FEATURE_WAKEUP + ucp_worker_wait) instead of busy-spinning ucp_worker_progress, so a rank blocked on a transfer no longer pins a CPU core. Falls back to a yielding poll if the active transports expose no wakeup fd.

When do these help?

• COMMUX_COALESCE (IOV packing) cuts per-message overhead, so it helps most when one send/recv carries many buffers over a latency-bound / high-latency link (InfiniBand). Caveat: UCX's IOV path is not zero-copy for CUDA (it gathers into a staging buffer), so for large vectors on intra-node cuda_ipc it can be slower than separate messages. Measured intra-node: faster at V≈2, slower at V≈16 — benchmark before enabling.
• COMMUX_GROUP (op-batching) collapses an exchange's many per-tensor posts into one stream-sync + one aggregate Work. Because the posts are already issued concurrently, the intra-node win is small; it is most useful on multi-node InfiniBand, where it reduces handshakes. Exposed through the standard c10d coalescing API, so a C++ consumer just calls pg->startCoalescing(dev) / pg->endCoalescing(dev)->wait() around its posts. The two flags compose: group the window, and IOV-pack any multi-tensor op in it.

Design

64-bit ucp_tag = [63:48] senderRank | [47:33] sub-index | [32] collective-bit | [31:0] userTag, so receivers match exactly on (sender, tag); recvAnysource wildcards the rank field. Endpoints bootstrap by exchanging worker addresses through the c10d::Store. send/recv are non-blocking and return a Work whose wait() is event-driven — it sleeps on the worker's wakeup fd rather than busy-spinning ucp_worker_progress; collectives run over tagged p2p with at::add/minimum/maximum so the same code reduces CPU and CUDA tensors. CUDA buffers are stream-synchronized before UCX touches them. Optional message/operation coalescing is available at runtime — see Runtime tuning.