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[https://nvbugs/6426834][fix] Drop UCX_NET_DEVICES unsetting from transceiver unit tests
Keep only UCX_TLS pinning and the NIXL progress-thread cap; removing UCX_NET_DEVICES from the environment is not needed. Signed-off-by: Chuang Zhu <111838961+chuangz0@users.noreply.github.com>
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tests/unittest/disaggregated/test_agent.py

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# Force a deterministic UCX/NIXL config regardless of what the cluster/CI
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# injects; see test_kv_transfer.py for the full rationale.
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os.environ["UCX_TLS"] = "^ib,gdr_copy"
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os.environ.pop("UCX_NET_DEVICES", None)
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os.environ["TRTLLM_NIXL_NUM_THREADS"] = "1"
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from tensorrt_llm import logger

tests/unittest/disaggregated/test_agent_multi_backends.py

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# injects; see test_kv_transfer.py for the full rationale. The subprocesses
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# spawned below inherit these via os.environ.copy().
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os.environ["UCX_TLS"] = "^ib,gdr_copy"
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os.environ.pop("UCX_NET_DEVICES", None)
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os.environ["TRTLLM_NIXL_NUM_THREADS"] = "1"
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tests/unittest/disaggregated/test_cache_transceiver_single_process.py

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# Force a deterministic UCX/NIXL config regardless of what the cluster/CI
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# injects; see test_kv_transfer.py for the full rationale.
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os.environ["UCX_TLS"] = "^ib,gdr_copy"
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os.environ.pop("UCX_NET_DEVICES", None)
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os.environ["TRTLLM_NIXL_NUM_THREADS"] = "1"
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from dataclasses import dataclass
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from typing import Dict, List, Optional

tests/unittest/disaggregated/test_deepseek_v4_kv_transfer.py

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# default 8 NIXL progress threads per agent cause heavy contention when
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# creating multiple agents on a single GPU in the same process.
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os.environ["UCX_TLS"] = "^ib,gdr_copy"
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os.environ.pop("UCX_NET_DEVICES", None)
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os.environ["TRTLLM_NIXL_NUM_THREADS"] = "1"
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tests/unittest/disaggregated/test_kv_transfer.py

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import uuid
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# Force a deterministic UCX config regardless of what the cluster/CI injects
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# (enroot may inject UCX_TLS=rc_v,sm + UCX_NET_DEVICES=mlx5_*; the CI agent
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# bootstrap exports UCX_TLS=tcp,cuda_copy,cuda_ipc):
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# - exclude IB (no fabric assumed) and gdr_copy (UCX rcache SIGABRT at
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# teardown);
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# - drop UCX_NET_DEVICES, otherwise with ^ib the remaining transports cannot
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# use the injected mlx5_* device list and UCX fails with "no active
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# messages transport".
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# (the CI agent bootstrap exports UCX_TLS=tcp,cuda_copy,cuda_ipc before pytest
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# starts, which a setdefault would leave in place): exclude IB (no fabric
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# assumed) and gdr_copy (UCX rcache SIGABRT at teardown).
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os.environ["UCX_TLS"] = "^ib,gdr_copy"
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os.environ.pop("UCX_NET_DEVICES", None)
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# Each NIXL agent spawns TRTLLM_NIXL_NUM_THREADS (default 8) busy-polling
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# progress threads, and a single case builds up to 8 TransferWorkers (one per
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# rank). On CI nodes shared with other single-GPU jobs the resulting CPU

tests/unittest/disaggregated/test_kv_transfer_mp.py

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# Force a deterministic UCX config regardless of what the cluster/CI injects;
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# see test_kv_transfer.py for the full rationale.
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os.environ["UCX_TLS"] = "^ib,gdr_copy"
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os.environ.pop("UCX_NET_DEVICES", None)
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# Limit NIXL busy-polling progress threads; see test_kv_transfer.py for the
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# full rationale (intermittent 120s timeouts on shared CI nodes,
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# https://nvbugs/6426834).

tests/unittest/disaggregated/test_mamba_transfer.py

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# Force a deterministic UCX/NIXL config regardless of what the cluster/CI
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# injects; see test_kv_transfer.py for the full rationale.
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os.environ["UCX_TLS"] = "^ib,gdr_copy"
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os.environ.pop("UCX_NET_DEVICES", None)
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os.environ["TRTLLM_NIXL_NUM_THREADS"] = "1"
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import tensorrt_llm

tests/unittest/disaggregated/test_py_cache_transceiver_mp.py

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# Force a deterministic UCX/NIXL config regardless of what the cluster/CI
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# injects; see test_kv_transfer.py for the full rationale.
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os.environ["UCX_TLS"] = "^ib,gdr_copy"
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os.environ.pop("UCX_NET_DEVICES", None)
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os.environ["TRTLLM_NIXL_NUM_THREADS"] = "1"
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import tensorrt_llm

tests/unittest/others/test_kv_cache_transceiver.py

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# Force a deterministic UCX/NIXL config regardless of what the cluster/CI
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# injects; see disaggregated/test_kv_transfer.py for the full rationale.
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os.environ["UCX_TLS"] = "^ib,gdr_copy"
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os.environ.pop("UCX_NET_DEVICES", None)
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os.environ["TRTLLM_NIXL_NUM_THREADS"] = "1"
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import tensorrt_llm

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