[core][distributed] add stateless_init_process_group

.buildkite/test-pipeline.yaml

@@ -120,6 +120,7 @@ steps:
   - tests/spec_decode/e2e/test_integration_dist_tp4
   - tests/compile
   commands:
+  - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
   - pytest -v -s spec_decode/e2e/test_integration_dist_tp4.py
@@ -431,7 +432,6 @@ steps:
   - pip install -e ./plugins/vllm_add_dummy_model
   - pytest -v -s distributed/test_distributed_oot.py
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s distributed/test_utils.py
 
 - label: Multi-step Tests (4 GPUs) # 36min
   working_dir: "/vllm-workspace/tests"

tests/distributed/test_utils.py

@@ -1,9 +1,15 @@
+import pytest
 import ray
+import torch
+import torch.distributed as dist
 
 import vllm.envs as envs
+from vllm.distributed.utils import stateless_init_process_group
 from vllm.utils import (cuda_device_count_stateless,
                         update_environment_variables)
 
+from ..utils import multi_gpu_test
+
 
 @ray.remote
 class _CUDADeviceCountStatelessTestActor:
@@ -24,10 +30,75 @@ def test_cuda_device_count_stateless():
     CUDA_VISIBLE_DEVICES is changed."""
     actor = _CUDADeviceCountStatelessTestActor.options(  # type: ignore
         num_gpus=2).remote()
-    assert sorted(ray.get(
-        actor.get_cuda_visible_devices.remote()).split(",")) == ["0", "1"]
+    assert len(
+        sorted(ray.get(
+            actor.get_cuda_visible_devices.remote()).split(","))) == 2
     assert ray.get(actor.get_count.remote()) == 2
     ray.get(actor.set_cuda_visible_devices.remote("0"))
     assert ray.get(actor.get_count.remote()) == 1
     ray.get(actor.set_cuda_visible_devices.remote(""))
     assert ray.get(actor.get_count.remote()) == 0
+
+
+def cpu_worker(rank, WORLD_SIZE):
+    pg1 = stateless_init_process_group(init_method="tcp://127.0.0.1:29500",
+                                       rank=rank,
+                                       world_size=WORLD_SIZE,
+                                       backend="gloo")
+    if rank <= 2:
+        pg2 = stateless_init_process_group(init_method="tcp://127.0.0.1:29501",
+                                           rank=rank,
+                                           world_size=3,
+                                           backend="gloo")
+    data = torch.tensor([rank])
+    dist.all_reduce(data, op=dist.ReduceOp.SUM, group=pg1)
+    if rank <= 2:
+        dist.all_reduce(data, op=dist.ReduceOp.SUM, group=pg2)
+    item = data[0].item()
+    print(f"rank: {rank}, item: {item}")
+    if rank == 3:
+        assert item == 6
+    else:
+        assert item == 18
+
+
+def gpu_worker(rank, WORLD_SIZE):
+    pg1 = stateless_init_process_group(init_method="tcp://127.0.0.1:29502",
+                                       rank=rank,
+                                       world_size=WORLD_SIZE,
+                                       backend="nccl")
+    if rank <= 2:
+        pg2 = stateless_init_process_group(init_method="tcp://127.0.0.1:29503",
+                                           rank=rank,
+                                           world_size=3,
+                                           backend="nccl")
+    torch.cuda.set_device(rank)
+    data = torch.tensor([rank]).cuda()
+    dist.all_reduce(data, op=dist.ReduceOp.SUM, group=pg1)
+    if rank <= 2:
+        dist.all_reduce(data, op=dist.ReduceOp.SUM, group=pg2)
+    item = data[0].item()
+    print(f"rank: {rank}, item: {item}")
+    if rank == 3:
+        assert item == 6
+    else:
+        assert item == 18
+
+
+@multi_gpu_test(num_gpus=4)
+@pytest.mark.parametrize("worker", [cpu_worker, gpu_worker])
+def test_stateless_init_process_group(worker):
+    WORLD_SIZE = 4
+    from multiprocessing import get_context
+    ctx = get_context("fork")
+    processes = []
+    for i in range(WORLD_SIZE):
+        rank = i
+        processes.append(ctx.Process(target=worker, args=(rank, WORLD_SIZE)))
+    for p in processes:
+        p.start()
+    for p in processes:
+        p.join()
+    for p in processes:
+        assert not p.exitcode
+    print("All processes finished.")

vllm/distributed/utils.py

@@ -5,6 +5,11 @@
 from typing import Sequence, Tuple
 
 import torch
+from torch.distributed import ProcessGroup
+from torch.distributed.distributed_c10d import (Backend, PrefixStore,
+                                                _get_default_timeout,
+                                                is_nccl_available)
+from torch.distributed.rendezvous import rendezvous
 
 import vllm.envs as envs
 from vllm.logger import init_logger
@@ -84,3 +89,71 @@ def get_pp_indices(num_hidden_layers: int, pp_rank: int,
             end_layer = num_hidden_layers
 
     return (start_layer, end_layer)
+
+
+def stateless_init_process_group(init_method: str, rank: int, world_size: int,
+                                 backend: str) -> ProcessGroup:
+    """A replacement for `torch.distributed.init_process_group` that does not
+    pollute the global state.
+
+    If we have process A and process B called `torch.distributed.init_process_group`
+    to form a group, and then we want to form another group with process A, B, C,
+    D, it is not possible in PyTorch, because process A and process B have already
+    formed a group, and process C and process D cannot join that group. This
+    function is a workaround for this issue.
+
+    `torch.distributed.init_process_group` is a global call, while this function
+    is a stateless call. It will return a `ProcessGroup` object that can be used
+    for collective communication. With this function, process A and process B
+    can call `stateless_init_process_group` to form a group, and then process A, B,
+    C, and D can call `stateless_init_process_group` to form another group.
+    """ # noqa
+
+    backend = Backend(backend)  # it is basically string
+    timeout = _get_default_timeout(backend)
+
+    store, rank, world_size = next(
+        rendezvous(init_method, rank, world_size, timeout=timeout))
+    store.set_timeout(timeout)
+
+    group_rank = rank
+    group_size = world_size
+
+    # Use a PrefixStore to avoid accidental overrides of keys used by
+    # different systems (e.g. RPC) in case the store is multi-tenant.
+    prefix_store = PrefixStore(init_method, store)
+
+    pg_options = ProcessGroup.Options(backend=backend, timeout=timeout)
+
+    pg: ProcessGroup = ProcessGroup(
+        prefix_store,
+        group_rank,
+        group_size,
+        pg_options,
+    )
+
+    if backend == "gloo":
+        from torch.distributed.distributed_c10d import ProcessGroupGloo
+        backend_class = ProcessGroupGloo(prefix_store,
+                                         group_rank,
+                                         group_size,
+                                         timeout=timeout)
+        backend_type = ProcessGroup.BackendType.GLOO
+        device = torch.device("cpu")
+    elif backend == "nccl":
+        assert is_nccl_available()
+        from torch.distributed.distributed_c10d import ProcessGroupNCCL
+
+        backend_options = ProcessGroupNCCL.Options()
+        backend_options._timeout = timeout
+
+        backend_class = ProcessGroupNCCL(prefix_store, group_rank, group_size,
+                                         backend_options)
+        backend_type = ProcessGroup.BackendType.NCCL
+        device = torch.device("cuda")
+
+    backend_class._set_sequence_number_for_group()
+
+    pg._register_backend(device, backend_type, backend_class)
+
+    return pg