[BugFix] Fix async EPLB hang with DeepEP LL all2all backend (#32860)

Signed-off-by: ilmarkov <markovilya197@gmail.com>

vllm/distributed/eplb/eplb_utils.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Utility functions for EPLB (Expert Parallel Load Balancing)."""
+
+import os
+
+from vllm.config import ParallelConfig
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+def override_envs_for_eplb(parallel_config: ParallelConfig) -> None:
+    """
+    Override environment variables for EPLB when specific conditions are met.
+
+    Args:
+        parallel_config: The parallel configuration object.
+    """
+    is_data_parallel = parallel_config.data_parallel_size > 1
+    is_eplb_enabled = parallel_config.enable_eplb
+    async_eplb = parallel_config.eplb_config.use_async
+    is_deepep_ll = parallel_config.all2all_backend == "deepep_low_latency"
+
+    # Override NCCL_MAX_CTAS to avoid hangs when using async EPLB with the
+    # DeepEP low-latency backend.
+    #
+    # The hang happens when two ranks interleave kernel launches differently
+    # between NCCL collectives (used by async EPLB weight exchange) and DeepEP
+    # low-latency (LL) kernels. DeepEP LL uses a cooperative launch and tries
+    # to reserve a large fraction of the GPU's SMs; if those SMs are currently
+    # occupied by NCCL, the DeepEP LL launch blocks until enough SMs are
+    # freed.
+    #
+    # If rank A enters DeepEP LL in main thread while rank B is still executing
+    # NCCL in async thread, rank A can block waiting for SMs, while rank B can
+    # block inside NCCL waiting for rank A to participate in the collective.
+    # This circular wait causes a deadlock.
+    # Limiting NCCL occupancy via NCCL_MAX_CTAS leaves space for the DeepEP
+    # cooperative kernel to launch and complete, breaking the deadlock.
+    # See: https://github.com/deepseek-ai/DeepEP/issues/496
+    if is_data_parallel and is_eplb_enabled and is_deepep_ll and async_eplb:
+        current_value_str = os.getenv("NCCL_MAX_CTAS")
+
+        if current_value_str and current_value_str.isdigit():
+            return
+
+        override_value = 8
+        os.environ["NCCL_MAX_CTAS"] = str(override_value)
+        logger.info_once(
+            f"EPLB: Setting NCCL_MAX_CTAS={override_value} "
+            "for expert parallel with EPLB and deepep_low_latency backend",
+            scope="global",
+        )

vllm/v1/worker/gpu_worker.py

@@ -22,6 +22,7 @@ from vllm.distributed import (
     set_custom_all_reduce,
 )
 from vllm.distributed.ec_transfer import ensure_ec_transfer_initialized
+from vllm.distributed.eplb.eplb_utils import override_envs_for_eplb
 from vllm.distributed.kv_transfer import (
     ensure_kv_transfer_initialized,
     ensure_kv_transfer_shutdown,
@@ -1035,6 +1036,7 @@ def init_worker_distributed_environment(
     from vllm.model_executor.layers.batch_invariant import init_batch_invariance
 
     init_batch_invariance(attention_config.backend)
+    override_envs_for_eplb(parallel_config)
     set_custom_all_reduce(not parallel_config.disable_custom_all_reduce)
 
     init_method = distributed_init_method or "env://"