[None][fix] AD test_trtllm_bench to use small model config and skip loading weights #8149
Conversation
📝 WalkthroughWalkthroughAdded a TinyLlama model entry to small model configs. Updated a TRT-LLM benchmark unit test to use get_small_model_config for constructing test options and dataset path, replacing direct hub ID usage and explicit options. Adjusted test parameters to include model_name. Changes
Sequence Diagram(s)sequenceDiagram
participant PyTest as PyTest test_ad_trtllm_bench
participant Utils as _model_test_utils.get_small_model_config
participant Bench as TRT-LLM bench runner
PyTest->>Utils: get_small_model_config(model_name)
Utils-->>PyTest: returns config (args, paths)
PyTest->>PyTest: derive options from config["args"]<br/>dataset_path <- config["args"]["model"]
PyTest->>Bench: run benchmark with options
Bench-->>PyTest: results
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes Pre-merge checks and finishing touches❌ Failed checks (3 warnings)
✨ Finishing touches
🧪 Generate unit tests (beta)
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 0
🧹 Nitpick comments (1)
tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py (1)
7-7: Consider following module namespace import convention.The coding guidelines prefer maintaining module namespace when importing:
from . import _model_test_utils # then use config = _model_test_utils.get_small_model_config(model_name)However, the current direct import is acceptable for test code and improves readability.
Based on coding guidelines.
📜 Review details
Configuration used: Path: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (2)
tests/unittest/_torch/auto_deploy/_utils_test/_model_test_utils.py(1 hunks)tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.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:
tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.pytests/unittest/_torch/auto_deploy/_utils_test/_model_test_utils.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.
Functions and methods use snake_case names.
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:
tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.pytests/unittest/_torch/auto_deploy/_utils_test/_model_test_utils.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:
tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.pytests/unittest/_torch/auto_deploy/_utils_test/_model_test_utils.py
🧠 Learnings (1)
📚 Learning: 2025-07-28T17:06:08.621Z
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.
Applied to files:
tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py
🧬 Code graph analysis (1)
tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py (1)
tests/unittest/_torch/auto_deploy/_utils_test/_model_test_utils.py (1)
get_small_model_config(492-533)
⏰ 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 (4)
tests/unittest/_torch/auto_deploy/_utils_test/_model_test_utils.py (1)
483-488: LGTM! TinyLlama config correctly added.The new model configuration follows the established pattern and will enable the benchmark test to use a small model configuration with minimal layers. The
get_small_model_configfunction will automatically add the necessary defaults includingskip_loading_weightsto optimize test performance.tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py (3)
70-72: LGTM! Test refactored to use get_small_model_config.The test now uses
get_small_model_configto obtain model configuration, which aligns with the PR objective to use small model configs and skip loading weights. The parametrization by model name makes the test more maintainable.
79-79: LGTM! Config args correctly unpacked.The configuration from
get_small_model_configis properly unpacked into the YAML options, including theskip_loading_weightsflag that optimizes test performance.
84-85: Model identifiers are consistent
In this test,config["args"]["model"]is set tomodel_name, so bothprepare_datasetandrun_benchmarkuse the same identifier; no changes required.Likely an incorrect or invalid review comment.
MrGeva
changed the title
MrGeva
changed the title
|
/bot run |
|
PR_Github #20648 [ run ] triggered by Bot |
|
PR_Github #20648 [ run ] completed with state |
|
/bot run |
|
PR_Github #20669 [ run ] triggered by Bot |
|
PR_Github #20669 [ run ] completed with state |
Signed-off-by: Eran Geva <[email protected]>
|
/bot run |
|
PR_Github #21086 [ run ] triggered by Bot |
|
PR_Github #21086 [ run ] completed with state |
…oading weights (NVIDIA#8149) Signed-off-by: Eran Geva <[email protected]> Signed-off-by: yufeiwu-nv <[email protected]>
…oading weights (NVIDIA#8149) Signed-off-by: Eran Geva <[email protected]>
…oading weights (NVIDIA#8149) Signed-off-by: Eran Geva <[email protected]>
…oading weights (NVIDIA#8149) Signed-off-by: Eran Geva <[email protected]>
…oading weights (NVIDIA#8149) Signed-off-by: Eran Geva <[email protected]>
3 participants
Refactored test_trtllm_bench to use get_small_model_config to load the tiny llama params, and start to skip loading weights.
Summary by CodeRabbit
Description
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)
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.
GitHub Bot Help
/bot [-h] ['run', 'kill', 'skip', 'reuse-pipeline'] ...Provide a user friendly way for developers to interact with a Jenkins server.
Run
/bot [-h|--help]to print this help message.See details below for each supported subcommand.
run [--reuse-test (optional)pipeline-id --disable-fail-fast --skip-test --stage-list "A10-PyTorch-1, xxx" --gpu-type "A30, H100_PCIe" --test-backend "pytorch, cpp" --add-multi-gpu-test --only-multi-gpu-test --disable-multi-gpu-test --post-merge --extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx" --detailed-log --debug(experimental)]Launch build/test pipelines. All previously running jobs will be killed.
--reuse-test (optional)pipeline-id(OPTIONAL) : Allow the new pipeline to reuse build artifacts and skip successful test stages from a specified pipeline or the last pipeline if no pipeline-id is indicated. If the Git commit ID has changed, this option will be always ignored. The DEFAULT behavior of the bot is to reuse build artifacts and successful test results from the last pipeline.--disable-reuse-test(OPTIONAL) : Explicitly prevent the pipeline from reusing build artifacts and skipping successful test stages from a previous pipeline. Ensure that all builds and tests are run regardless of previous successes.--disable-fail-fast(OPTIONAL) : Disable fail fast on build/tests/infra failures.--skip-test(OPTIONAL) : Skip all test stages, but still run build stages, package stages and sanity check stages. Note: Does NOT update GitHub check status.--stage-list "A10-PyTorch-1, xxx"(OPTIONAL) : Only run the specified test stages. Examples: "A10-PyTorch-1, xxx". Note: Does NOT update GitHub check status.--gpu-type "A30, H100_PCIe"(OPTIONAL) : Only run the test stages on the specified GPU types. Examples: "A30, H100_PCIe". Note: Does NOT update GitHub check status.--test-backend "pytorch, cpp"(OPTIONAL) : Skip test stages which don't match the specified backends. Only support [pytorch, cpp, tensorrt, triton]. Examples: "pytorch, cpp" (does not run test stages with tensorrt or triton backend). Note: Does NOT update GitHub pipeline status.--only-multi-gpu-test(OPTIONAL) : Only run the multi-GPU tests. Note: Does NOT update GitHub check status.--disable-multi-gpu-test(OPTIONAL) : Disable the multi-GPU tests. Note: Does NOT update GitHub check status.--add-multi-gpu-test(OPTIONAL) : Force run the multi-GPU tests in addition to running L0 pre-merge pipeline.--post-merge(OPTIONAL) : Run the L0 post-merge pipeline instead of the ordinary L0 pre-merge pipeline.--extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx"(OPTIONAL) : Run the ordinary L0 pre-merge pipeline and specified test stages. Examples: --extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx".--detailed-log(OPTIONAL) : Enable flushing out all logs to the Jenkins console. This will significantly increase the log volume and may slow down the job.--debug(OPTIONAL) : Experimental feature. Enable access to the CI container for debugging purpose. Note: Specify exactly one stage in thestage-listparameter to access the appropriate container environment. Note: Does NOT update GitHub check status.For guidance on mapping tests to stage names, see
docs/source/reference/ci-overview.mdand the
scripts/test_to_stage_mapping.pyhelper.kill
killKill all running builds associated with pull request.
skip
skip --comment COMMENTSkip testing for latest commit on pull request.
--comment "Reason for skipping build/test"is required. IMPORTANT NOTE: This is dangerous since lack of user care and validation can cause top of tree to break.reuse-pipeline
reuse-pipelineReuse a previous pipeline to validate current commit. This action will also kill all currently running builds associated with the pull request. IMPORTANT NOTE: This is dangerous since lack of user care and validation can cause top of tree to break.