[https://nvbugs/5649010][fix] use 0 port as arbitrary port when disagg service discovery is enabled #10383
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Signed-off-by: Lizhi Zhou <[email protected]>
📝 WalkthroughWalkthroughThis pull request refactors port binding logic in the TensorRT-LLM server launch mechanism and updates corresponding test infrastructure. The serve.py module now implements a retry-based port binding strategy with dynamic free port allocation for disaggregated clusters, while test utilities are consolidated to use a shared free port function and enhanced with new status-checking helpers. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
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Actionable comments posted: 2
🧹 Nitpick comments (1)
tests/integration/defs/disaggregated/test_auto_scaling.py (1)
297-321: Remove unusedrelease_portparameter.The
release_portparameter in theterminate()function is no longer used after the removal ofUSED_PORTStracking. This parameter should be removed to avoid confusion.🔎 Proposed cleanup
-def terminate(*args, show_log_lines=30, release_port=True): +def terminate(*args, show_log_lines=30): for arg in args:Also update the call site at line 453:
- terminate(gen_worker1, release_port=True) + terminate(gen_worker1)And line 479:
- terminate(ctx_worker1, release_port=True) + terminate(ctx_worker1)
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📒 Files selected for processing (2)
tensorrt_llm/commands/serve.pytests/integration/defs/disaggregated/test_auto_scaling.py
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📓 Path-based instructions (2)
**/*.py
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
**/*.py: Code developed for TensorRT-LLM should conform to Python 3.8+
Indent Python code with 4 spaces. Do not use tabs
Always maintain the namespace when importing in Python, even if only one class or function from a module is used
Python files should use snake_case naming:some_file.py
Python classes should use PascalCase naming:class SomeClass
Python functions and methods should use snake_case naming:def my_awesome_function():
Python local variables should use snake_case naming:my_variable = ...
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Python constants should use upper snake_case naming:MY_CONSTANT = ...
Avoid shadowing variables declared in an outer scope in Python
Initialize all externally visible members of a Python class in the constructor
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Files:
tensorrt_llm/commands/serve.pytests/integration/defs/disaggregated/test_auto_scaling.py
**/*.{cpp,h,cu,cuh,py}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the year of its latest meaningful modification
Files:
tensorrt_llm/commands/serve.pytests/integration/defs/disaggregated/test_auto_scaling.py
🧠 Learnings (12)
📓 Common learnings
Learnt from: Shixiaowei02
Repo: NVIDIA/TensorRT-LLM PR: 9582
File: tests/integration/defs/accuracy/test_disaggregated_serving.py:73-83
Timestamp: 2025-12-02T03:40:40.572Z
Learning: In the disaggregated serving tests (tests/integration/defs/accuracy/test_disaggregated_serving.py), calling get_free_port() multiple times in succession is acceptable because the tests run in a controlled single-process environment where race conditions for port allocation are not a concern.
📚 Learning: 2025-12-02T03:40:40.572Z
Learnt from: Shixiaowei02
Repo: NVIDIA/TensorRT-LLM PR: 9582
File: tests/integration/defs/accuracy/test_disaggregated_serving.py:73-83
Timestamp: 2025-12-02T03:40:40.572Z
Learning: In the disaggregated serving tests (tests/integration/defs/accuracy/test_disaggregated_serving.py), calling get_free_port() multiple times in succession is acceptable because the tests run in a controlled single-process environment where race conditions for port allocation are not a concern.
Applied to files:
tensorrt_llm/commands/serve.pytests/integration/defs/disaggregated/test_auto_scaling.py
📚 Learning: 2025-07-28T17:06:08.621Z
Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
Applied to files:
tensorrt_llm/commands/serve.py
📚 Learning: 2025-08-26T09:37:10.463Z
Learnt from: jiaganc
Repo: NVIDIA/TensorRT-LLM PR: 7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which can contain default `cuda_graph_config` values, so `llm_args` may already have this config before the extra options processing.
Applied to files:
tensorrt_llm/commands/serve.py
📚 Learning: 2025-09-16T09:30:09.716Z
Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 7763
File: cpp/tensorrt_llm/CMakeLists.txt:297-301
Timestamp: 2025-09-16T09:30:09.716Z
Learning: In the TensorRT-LLM project, NCCL libraries are loaded earlier by PyTorch libraries or the bindings library, so the main shared library doesn't need NCCL paths in its RPATH - the libraries will already be available in the process address space when needed.
Applied to files:
tensorrt_llm/commands/serve.py
📚 Learning: 2025-09-09T09:40:45.658Z
Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.
Applied to files:
tensorrt_llm/commands/serve.py
📚 Learning: 2025-08-26T09:37:10.463Z
Learnt from: jiaganc
Repo: NVIDIA/TensorRT-LLM PR: 7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM's bench configuration, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which is a Dict[str, Any] that can contain default values including `cuda_graph_config`, making the fallback `llm_args["cuda_graph_config"]` safe to use.
Applied to files:
tensorrt_llm/commands/serve.py
📚 Learning: 2025-08-27T14:23:55.566Z
Learnt from: ixlmar
Repo: NVIDIA/TensorRT-LLM PR: 7294
File: tensorrt_llm/_torch/modules/rms_norm.py:17-17
Timestamp: 2025-08-27T14:23:55.566Z
Learning: The TensorRT-LLM project requires Python 3.10+ as evidenced by the use of TypeAlias from typing module, match/case statements, and union type | syntax throughout the codebase, despite some documentation still mentioning Python 3.8+.
Applied to files:
tensorrt_llm/commands/serve.py
📚 Learning: 2025-08-19T12:45:11.997Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 7033
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:0-0
Timestamp: 2025-08-19T12:45:11.997Z
Learning: In tensorrt_llm/_torch/pyexecutor/model_engine.py, DoRA (Delta Orthogonal Rank Adaptation) functionality was removed from the PyTorch flow to eliminate issues with inverted DoRA detection logic. The original is_dora condition was checking if scaling_vec_pointer == 0, which was potentially incorrect.
Applied to files:
tensorrt_llm/commands/serve.py
📚 Learning: 2025-08-01T15:14:45.673Z
Learnt from: yibinl-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 6506
File: examples/models/core/mixtral/requirements.txt:3-3
Timestamp: 2025-08-01T15:14:45.673Z
Learning: In TensorRT-LLM, examples directory can have different dependency versions than the root requirements.txt file. Version conflicts between root and examples dependencies are acceptable because examples are designed to be standalone and self-contained.
Applied to files:
tensorrt_llm/commands/serve.py
📚 Learning: 2025-08-06T13:58:07.506Z
Learnt from: galagam
Repo: NVIDIA/TensorRT-LLM PR: 6487
File: tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py:1-12
Timestamp: 2025-08-06T13:58:07.506Z
Learning: In TensorRT-LLM, test files (files under tests/ directories) do not require NVIDIA copyright headers, unlike production source code files. Test files typically start directly with imports, docstrings, or code.
Applied to files:
tensorrt_llm/commands/serve.py
📚 Learning: 2025-07-22T09:22:14.726Z
Learnt from: yechank-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 6254
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:1201-1204
Timestamp: 2025-07-22T09:22:14.726Z
Learning: In TensorRT-LLM's multimodal processing pipeline, shared tensor recovery using `from_shared_tensor()` is only needed during the context phase. Generation requests reuse the already-recovered tensor data and only need to call `strip_for_generation()` to remove unnecessary multimodal data while preserving the recovered tensors. This avoids redundant tensor recovery operations during generation.
Applied to files:
tensorrt_llm/commands/serve.py
🧬 Code graph analysis (2)
tensorrt_llm/commands/serve.py (2)
tensorrt_llm/_utils.py (2)
get_free_port(477-478)mpi_rank(538-545)tests/integration/defs/disaggregated/test_auto_scaling.py (1)
disagg_cluster_config(63-71)
tests/integration/defs/disaggregated/test_auto_scaling.py (1)
tensorrt_llm/_utils.py (1)
get_free_port(477-478)
🪛 Ruff (0.14.10)
tensorrt_llm/commands/serve.py
197-198: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
197-198: Avoid specifying long messages outside the exception class
(TRY003)
tests/integration/defs/disaggregated/test_auto_scaling.py
234-234: Probable use of requests call without timeout
(S113)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Pre-commit Check
🔇 Additional comments (3)
tensorrt_llm/commands/serve.py (1)
21-21: LGTM!The import of
get_free_portandmpi_rankfromtensorrt_llm._utilsis correct and aligns with the new port allocation strategy.tests/integration/defs/disaggregated/test_auto_scaling.py (2)
38-38: LGTM!The simplified
disagg_portfixture now directly callsget_free_port(), which is cleaner and aligns with the learnings that port allocation race conditions are acceptable in controlled test environments.Based on learnings, calling
get_free_port()multiple times in test environments is acceptable.
170-173: LGTM!The comment clearly documents the new behavior where
port=0triggers dynamic port allocation whendisagg_cluster_configis provided, which aligns with the changes inserve.py.
reasonsolo
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/bot run --disable-fail-fast |
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PR_Github #30368 [ run ] triggered by Bot. Commit: |
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PR_Github #30368 [ run ] completed with state |
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/bot run --add-multi-gpu-test |
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PR_Github #30386 [ run ] triggered by Bot. Commit: |
| port_retries = 1 | ||
| else: | ||
| port_retries = 100 | ||
| port = get_free_port() |
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Does disagg server needs to know this arbitrary port?
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PR_Github #30386 [ run ] completed with state |
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