[core] add nccl symmetric memory for all reduce

.buildkite/test-pipeline.yaml

@@ -1037,3 +1037,4 @@ steps:
   num_gpus: 2
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
+    - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py

benchmarks/kernels/benchmark_device_communicators.py

@@ -7,6 +7,10 @@
 CustomAllreduce (oneshot, twoshot), PyNcclCommunicator,
 and SymmMemCommunicator (multimem, two-shot).
 
+for NCCL symmetric memory you need to set the environment variables
+NCCL_NVLS_ENABLE=1 NCCL_CUMEM_ENABLE=1 VLLM_USE_NCCL_SYMM_MEM=1, otherwise NCCL does
+not use fast NVLS implementation for all reduce.
+
 Usage:
     torchrun --nproc_per_node=<N> benchmark_device_communicators.py [options]
 
@@ -26,7 +30,13 @@
 from torch.distributed import ProcessGroup
 
 from vllm.distributed.device_communicators.custom_all_reduce import CustomAllreduce
-from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator
+from vllm.distributed.device_communicators.pynccl import (
+    PyNcclCommunicator,
+    register_nccl_symmetric_ops,
+)
+from vllm.distributed.device_communicators.pynccl_allocator import (
+    set_graph_pool_id,
+)
 from vllm.distributed.device_communicators.symm_mem import SymmMemCommunicator
 from vllm.logger import init_logger
 from vllm.utils import FlexibleArgumentParser
@@ -98,6 +108,7 @@ def _init_communicators(self):
             )
             if not self.pynccl_comm.disabled:
                 logger.info("Rank %s: PyNcclCommunicator initialized", self.rank)
+                register_nccl_symmetric_ops(self.pynccl_comm)
             else:
                 logger.info("Rank %s: PyNcclCommunicator disabled", self.rank)
                 self.pynccl_comm = None
@@ -194,6 +205,15 @@ def benchmark_allreduce(
                     None,  # no env variable needed
                 )
             )
+            communicators.append(
+                (
+                    "pynccl-symm",
+                    lambda t: torch.ops.vllm.all_reduce_symmetric_with_copy(t),
+                    lambda t: True,  # Always available if initialized
+                    nullcontext(),
+                    None,  # no env variable needed
+                )
+            )
 
         if self.symm_mem_comm_multimem is not None:
             comm = self.symm_mem_comm_multimem
@@ -271,7 +291,9 @@ def benchmark_allreduce_single(
                 # Capture the graph using context manager
                 with context:
                     graph = torch.cuda.CUDAGraph()
-                    with torch.cuda.graph(graph):
+                    graph_pool = torch.cuda.graph_pool_handle()
+                    set_graph_pool_id(graph_pool)
+                    with torch.cuda.graph(graph, pool=graph_pool):
                         for _ in range(CUDA_GRAPH_CAPTURE_CYCLES):
                             allreduce_fn(graph_input)
 

tests/distributed/test_nccl_symm_mem_allreduce.py

@@ -0,0 +1,94 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import random
+import typing
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+import vllm.envs as envs
+from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.distributed.device_communicators.cuda_communicator import (
+    CudaCommunicator)
+from vllm.distributed.device_communicators.pynccl import (
+    register_nccl_symmetric_ops)
+from vllm.distributed.device_communicators.pynccl_allocator import (
+    get_nccl_mem_pool, is_symmetric_memory_enabled)
+from vllm.distributed.parallel_state import (get_tp_group,
+                                             init_distributed_environment,
+                                             initialize_model_parallel)
+from vllm.platforms import current_platform
+from vllm.utils import update_environment_variables
+
+torch.manual_seed(42)
+random.seed(44)
+
+test_size_elements = 4 * 1024 * 1024
+
+
+def nccl_symm_mem_allreduce_worker(local_rank: int, world_size: int):
+    monkeypatch = pytest.MonkeyPatch()
+    with monkeypatch.context() as m:
+        m.delenv("CUDA_VISIBLE_DEVICES", raising=False)
+        dtype = torch.bfloat16
+        device = torch.device(f"cuda:{local_rank}")
+        torch.cuda.set_device(device)
+        torch.set_default_device(device)
+        torch.set_default_dtype(dtype)
+        update_environment_variables({
+            "RANK": str(local_rank),
+            "LOCAL_RANK": str(local_rank),
+            "WORLD_SIZE": str(world_size),
+            "MASTER_ADDR": "localhost",
+            "MASTER_PORT": "12345",
+        })
+
+        init_distributed_environment()
+        initialize_model_parallel(tensor_model_parallel_size=world_size)
+
+        cuda_communicator = typing.cast(CudaCommunicator,
+                                        get_tp_group().device_communicator)
+        pynccl_comm = cuda_communicator.pynccl_comm
+        if get_nccl_mem_pool() is None:
+            pytest.skip("NCCL allocator compilation failed "
+                        "(probably missing NCCL headers).")
+        if not is_symmetric_memory_enabled():
+            pytest.skip("NCCL symmetric memory allreduce is disabled.")
+
+        register_nccl_symmetric_ops(pynccl_comm)
+        input = torch.randint(1,
+                              23, (test_size_elements, ),
+                              dtype=dtype,
+                              device=device)
+        input_clone = input.clone()
+        output = torch.ops.vllm.all_reduce_symmetric_with_copy(input)
+        assert output is not None
+
+        group = get_tp_group().device_group
+        dist.all_reduce(input_clone, group=group)
+        torch.testing.assert_close(output, input_clone, atol=2.5, rtol=0.1)
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda(),
+    reason="NCCLSymmMemAllreduce is only available for CUDA platforms.",
+)
+@pytest.mark.parametrize("world_size", [2])
+@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"],
+                    reason="Only test on CUDA")
+def test_nccl_symm_mem_allreduce(monkeypatch: pytest.MonkeyPatch, world_size):
+    if world_size > torch.cuda.device_count():
+        pytest.skip("Not enough GPUs to run the test.")
+
+    # Enable SymmMemCommunicator
+    monkeypatch.setenv("VLLM_USE_NCCL_SYMM_MEM", "1")
+    monkeypatch.setenv("NCCL_NVLS_ENABLE", "1")
+    monkeypatch.setenv("NCCL_CUMEM_ENABLE", "1")
+
+    mp.spawn(nccl_symm_mem_allreduce_worker,
+             args=(world_size, ),
+             nprocs=world_size)
+    cleanup_dist_env_and_memory()

vllm/compilation/cuda_graph.py

@@ -12,6 +12,8 @@
 from vllm.compilation.counter import compilation_counter
 from vllm.compilation.monitor import validate_cudagraph_capturing_enabled
 from vllm.config import CUDAGraphMode, VllmConfig
+from vllm.distributed.device_communicators.pynccl_allocator import (
+    set_graph_pool_id)
 from vllm.forward_context import BatchDescriptor, get_forward_context
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
@@ -154,6 +156,10 @@ def __call__(self, *args, **kwargs):
                     stack.enter_context(
                         patch("torch.cuda.empty_cache", lambda: None))
 
+                if self.graph_pool is not None:
+                    set_graph_pool_id(self.graph_pool)
+                else:
+                    set_graph_pool_id(current_platform.graph_pool_handle())
                 # mind-exploding: carefully manage the reference and memory.
                 with torch.cuda.graph(cudagraph, pool=self.graph_pool):
                     # `output` is managed by pytorch's cudagraph pool

vllm/distributed/device_communicators/all_reduce_utils.py

@@ -10,8 +10,9 @@
 import tempfile
 from collections.abc import Sequence
 from itertools import product
-from typing import Optional
+from typing import Any, Optional
 
+import torch
 import torch.distributed as dist
 import torch.multiprocessing as mp
 
@@ -56,6 +57,30 @@
     }
 }
 
+NCCL_SYMM_MEM_ALL_REDUCE_CONFIG: dict[str, Any] = {
+    "min_world_size": 4,
+    "thresholds": {
+        4: 2 * MiB,  # 2 MB
+        8: 1 * MiB,  # 1 MB
+    },
+    "always_use_above_world_size": 8  # Always use symm mem for world_size > 8
+}
+
+
+def should_nccl_symm_mem_allreduce(world_size: int,
+                                   input_tensor: torch.Tensor) -> bool:
+    from vllm.distributed.device_communicators.pynccl_allocator import (
+        is_symmetric_memory_enabled)
+    if not is_symmetric_memory_enabled():
+        return False
+    if world_size < NCCL_SYMM_MEM_ALL_REDUCE_CONFIG["min_world_size"]:
+        return False
+    threshold = NCCL_SYMM_MEM_ALL_REDUCE_CONFIG["thresholds"].get(world_size)
+    if threshold is not None and input_tensor.nbytes >= threshold:
+        return True
+    return (world_size
+            > NCCL_SYMM_MEM_ALL_REDUCE_CONFIG["always_use_above_world_size"])
+
 
 def producer(batch_src: Sequence[int],
              producer_queue,

vllm/distributed/device_communicators/cuda_communicator.py

@@ -7,6 +7,12 @@
 from torch.distributed import ProcessGroup
 
 import vllm.envs as envs
+from vllm.distributed.device_communicators.all_reduce_utils import (
+    should_nccl_symm_mem_allreduce)
+from vllm.distributed.device_communicators.pynccl import (
+    register_nccl_symmetric_ops)
+from vllm.distributed.device_communicators.pynccl_allocator import (
+    is_symmetric_memory_enabled)
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 
@@ -53,6 +59,8 @@ def __init__(self,
                 group=self.cpu_group,
                 device=self.device,
             )
+            if is_symmetric_memory_enabled():
+                register_nccl_symmetric_ops(self.pynccl_comm)
 
         self.ca_comm: Optional[CustomAllreduce] = None
         self.qr_comm: Optional[QuickAllReduce] = None
@@ -107,6 +115,13 @@ def __init__(self,
                 raise ValueError(f"Unknown all2all backend: {all2all_backend}")
 
     def all_reduce(self, input_):
+        # since currently we perform copy input -> symm_input -> out-of-place AR
+        # return symm_output, we don't need to check if input is symmetric
+        if self.pynccl_comm is not None and \
+            should_nccl_symm_mem_allreduce(self.pynccl_comm.world_size,input_):
+            out = torch.ops.vllm.all_reduce_symmetric_with_copy(input_)
+            if out is not None:
+                return out
         # always try quick reduce first, then custom allreduce,
         # and then pynccl. (quick reduce just for ROCM MI3*)
         qr_comm = self.qr_comm

vllm/distributed/device_communicators/pynccl.py

@@ -17,6 +17,39 @@
 
 logger = init_logger(__name__)
 
+_NCCL_SYMM_OPS_REGISTERED = False
+
+
+def register_nccl_symmetric_ops(pynccl_comm):
+    from vllm.distributed.device_communicators.pynccl_allocator import (
+        nccl_symm_mem_context)
+    from vllm.utils import direct_register_custom_op
+
+    global _NCCL_SYMM_OPS_REGISTERED
+    if _NCCL_SYMM_OPS_REGISTERED:
+        return
+    _NCCL_SYMM_OPS_REGISTERED = True
+
+    def all_reduce_symmetric_with_copy_impl(
+            input_tensor: torch.Tensor) -> torch.Tensor:
+        with nccl_symm_mem_context(pynccl_comm):
+            symm_input = torch.empty_like(input_tensor)
+            symm_output = torch.empty_like(input_tensor)
+        symm_input.copy_(input_tensor)
+        symm_output = pynccl_comm.all_reduce(symm_input, symm_output)
+        return symm_output
+
+    def all_reduce_symmetric_with_copy_fake(
+            input_tensor: torch.Tensor) -> torch.Tensor:
+        return torch.empty_like(input_tensor)
+
+    direct_register_custom_op(
+        op_name="all_reduce_symmetric_with_copy",
+        op_func=all_reduce_symmetric_with_copy_impl,
+        mutates_args=[],
+        fake_impl=all_reduce_symmetric_with_copy_fake,
+    )
+
 
 class PyNcclCommunicator:
 
@@ -67,6 +100,7 @@ def __init__(
         self.available = True
         self.disabled = False
 
+        self.nccl_version = self.nccl.ncclGetRawVersion()
         logger.info("vLLM is using nccl==%s", self.nccl.ncclGetVersion())
 
         if self.rank == 0:
@@ -109,6 +143,7 @@ def __init__(
 
     def all_reduce(self,
                    in_tensor: torch.Tensor,
+                   out_tensor: torch.Tensor = None,
                    op: ReduceOp = ReduceOp.SUM,
                    stream=None) -> torch.Tensor:
         if self.disabled:
@@ -120,7 +155,8 @@ def all_reduce(self,
             f"this nccl communicator is created to work on {self.device}, "
             f"but the input tensor is on {in_tensor.device}")
 
-        out_tensor = torch.empty_like(in_tensor)
+        if out_tensor is None:
+            out_tensor = torch.empty_like(in_tensor)
 
         if stream is None:
             stream = current_stream()
@@ -288,3 +324,18 @@ def group_start(self):
 
     def group_end(self):
         self.nccl.ncclGroupEnd()
+
+    def register_comm_window(self, tensor: torch.Tensor):
+        return self.nccl.ncclCommWindowRegister(
+            self.comm,
+            buffer_type(tensor.data_ptr()),
+            tensor.numel() * tensor.element_size(),
+            1,
+        )
+
+    def register_comm_window_raw(self, ptr: int, size: int):
+        return self.nccl.ncclCommWindowRegister(self.comm, buffer_type(ptr),
+                                                size, 1)
+
+    def deregister_comm_window(self, window):
+        return self.nccl.ncclCommWindowDeregister(self.comm, window)