[None][fix] trtllm-serve yaml loading #7551
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📝 WalkthroughWalkthroughUpdates in Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant User
participant serve.py
participant YAML Loader as YAML
participant ArgsBuilder as get_llm_args
User->>serve.py: launch serve with options
serve.py->>ArgsBuilder: build llm_args from CLI/config
alt backend != "trt"
ArgsBuilder-->>serve.py: llm_args without build_config
else backend == "trt"
ArgsBuilder-->>serve.py: llm_args with build_config
end
opt extra_llm_api_options provided
serve.py->>YAML: load YAML file
YAML-->>serve.py: parsed_object
alt parsed_object is dict
serve.py->>serve.py: update llm_args with overrides
else not a dict
serve.py-->>User: raise ValueError
end
end
serve.py-->>User: start server with finalized llm_args
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes Possibly related PRs
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Actionable comments posted: 2
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Some comments are outside the diff and can’t be posted inline due to platform limitations.
⚠️ Outside diff range comments (2)
tensorrt_llm/commands/serve.py (2)
1-1: Missing required NVIDIA Apache-2.0 headerPer project guidelines, prepend the NVIDIA Apache-2.0 copyright header (current year) to all source files.
Example:
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License.
326-344: Addmoe_cluster_parallel_sizetoget_llm_argsand include it inllm_argsThe CLI arg
moe_cluster_parallel_sizeis passed intoget_llm_argsbut isn’t declared in its signature—so it’s captured by**llm_args_extra_dictand then discarded by the caller. Update the function to acceptmoe_cluster_parallel_size: Optional[int] = Noneand add
"moe_cluster_parallel_size": moe_cluster_parallel_size,to the
llm_argsdict so the cluster size value isn’t silently dropped.
🧹 Nitpick comments (3)
tensorrt_llm/commands/serve.py (3)
347-352: Make YAML validation precise and user-friendly (mapping-only + clear errors)Current message says “valid yaml file” even when YAML is valid but not a mapping (e.g., list). Also, YAML parse errors aren’t caught.
Proposed improvement:
- llm_args_extra_dict = {} - if extra_llm_api_options is not None: - with open(extra_llm_api_options, 'r') as f: - llm_args_extra_dict = yaml.safe_load(f) - if not isinstance(llm_args_extra_dict, dict): - raise ValueError("llm_args_extra_dict must be a valid yaml file") + llm_args_extra_dict = {} + if extra_llm_api_options is not None: + try: + with open(extra_llm_api_options, 'r') as f: + loaded = yaml.safe_load(f) + except yaml.YAMLError as e: + raise ValueError( + f"Invalid YAML in --extra_llm_api_options '{extra_llm_api_options}': {e}" + ) from e + if loaded is None: + loaded = {} + if not isinstance(loaded, dict): + raise ValueError( + f"--extra_llm_api_options must be a YAML mapping (dict) at top level, got {type(loaded).__name__}" + ) + llm_args_extra_dict = loaded
110-146: Backend sentinel inconsistency (None vs "trt") invites subtle bugsYou normalize to
'pytorch' | '_autodeploy' | None, then later compare against"trt"elsewhere. Prefer consistent explicit values to avoid future footguns like the one fixed above.Option: keep
"trt"instead ofNoneduring normalization and treat unknown values as an error.- backend = backend if backend in ["pytorch", "_autodeploy"] else None + if backend not in ("pytorch", "trt", "_autodeploy"): + raise ValueError(f"Invalid backend: {backend}") + # keep explicit string for downstream checks/routes
41-43: Comment mismatch with behaviorThe comment says “Using print for safety in signal handlers” but uses
logger.info. Minor clarity nit.Update the comment or switch to
print(...)as stated.
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📒 Files selected for processing (1)
tensorrt_llm/commands/serve.py(2 hunks)
🧰 Additional context used
📓 Path-based instructions (3)
**/*.{h,hpp,hh,hxx,cpp,cxx,cc,cu,cuh,py}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
Use only spaces, no tabs; indent with 4 spaces.
Files:
tensorrt_llm/commands/serve.py
**/*.py
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
**/*.py: Python code must target Python 3.8+.
Indent Python code with 4 spaces; do not use tabs.
Maintain module namespace when importing; prefer 'from package.subpackage import foo' then 'foo.SomeClass()' instead of importing the class directly.
Python filenames should be snake_case (e.g., some_file.py).
Python classes use PascalCase names.
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Local variables use snake_case; prefix 'k' for variables that start with a number (e.g., k_99th_percentile).
Global variables use upper SNAKE_CASE prefixed with 'G' (e.g., G_MY_GLOBAL).
Constants use upper SNAKE_CASE (e.g., MY_CONSTANT).
Avoid shadowing variables from an outer scope.
Initialize all externally visible members of a class in the constructor.
Prefer docstrings for interfaces that may be used outside a file; comments for in-function or file-local interfaces.
Use Google-style docstrings for classes and functions (Sphinx-parsable).
Document attributes and variables inline so they render under the class/function docstring.
Avoid reflection when a simpler, explicit approach suffices (e.g., avoid dict(**locals()) patterns).
In try/except, catch the most specific exceptions possible.
For duck-typing try/except, keep the try body minimal and use else for the main logic.
Files:
tensorrt_llm/commands/serve.py
**/*.{cpp,cxx,cc,h,hpp,hh,hxx,cu,cuh,py}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
Prepend the NVIDIA Apache-2.0 copyright header with current year to the top of all source files (e.g., .cpp, .h, .cu, .py).
Files:
tensorrt_llm/commands/serve.py
🧠 Learnings (3)
📓 Common learnings
Learnt from: jiaganc
PR: NVIDIA/TensorRT-LLM#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.
Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#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.
📚 Learning: 2025-08-26T09:37:10.463Z
Learnt from: jiaganc
PR: NVIDIA/TensorRT-LLM#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: 2025-08-26T09:37:10.463Z
Learnt from: jiaganc
PR: NVIDIA/TensorRT-LLM#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
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Signed-off-by: Yan Chunwei <[email protected]>
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Summary by CodeRabbit
Description
Fix the trtllm-serve yaml loading overwrite the CLI commands.
Test Coverage
PR Checklist
Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
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Please check this after reviewing the above items as appropriate for this PR.
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