[None][chore] Use cached model in all ray tests #8962
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Signed-off-by: shuyix <219646547+shuyixiong@users.noreply.github.com>
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📝 WalkthroughWalkthroughThe PR generalizes hardcoded model paths in Ray orchestrator examples and tests by introducing configurable command-line options and utility-based path resolution, replacing direct TinyLlama model references with parameterized variables and dynamic path construction. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~12 minutes
Pre-merge checks and finishing touches❌ Failed checks (1 warning)
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📒 Files selected for processing (3)
examples/ray_orchestrator/disaggregated/disagg_serving_local.sh(5 hunks)tests/integration/defs/examples/test_ray.py(3 hunks)tests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.py(3 hunks)
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📓 Path-based instructions (3)
**/*.{h,hpp,hh,hxx,cpp,cxx,cc,cu,cuh,py}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
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Files:
tests/integration/defs/examples/test_ray.pytests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.py
**/*.py
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Files:
tests/integration/defs/examples/test_ray.pytests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.py
**/*.{cpp,cxx,cc,h,hpp,hh,hxx,cu,cuh,py}
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tests/integration/defs/examples/test_ray.pytests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.py
🧠 Learnings (6)
📓 Common learnings
Learnt from: achartier
Repo: NVIDIA/TensorRT-LLM PR: 6763
File: tests/integration/defs/triton_server/conftest.py:16-22
Timestamp: 2025-08-11T20:09:24.389Z
Learning: In the TensorRT-LLM test infrastructure, the team prefers simple, direct solutions (like hard-coding directory traversal counts) over more complex but robust approaches when dealing with stable directory structures. They accept the maintenance cost of updating tests if the layout changes.
📚 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:
tests/integration/defs/examples/test_ray.pytests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.py
📚 Learning: 2025-08-26T06:07:02.166Z
Learnt from: shaharmor98
Repo: NVIDIA/TensorRT-LLM PR: 7231
File: tensorrt_llm/_torch/pyexecutor/_util.py:504-509
Timestamp: 2025-08-26T06:07:02.166Z
Learning: In tensorrt_llm/_torch/pyexecutor/_util.py, when calling model_engine.set_lora_model_config(), pass model_binding_config.mlp_hidden_size directly without multiplying by mapping.tp_size, as the mlp_hidden_size from get_bindings_model_config() is already the per-TP rank value needed for LoRA weight packaging.
Applied to files:
tests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.py
📚 Learning: 2025-08-26T09:49:04.956Z
Learnt from: pengbowang-nv
Repo: NVIDIA/TensorRT-LLM PR: 7192
File: tests/integration/test_lists/test-db/l0_dgx_b200.yml:56-72
Timestamp: 2025-08-26T09:49:04.956Z
Learning: In TensorRT-LLM test configuration files, the test scheduling system handles wildcard matching with special rules that prevent duplicate test execution even when the same tests appear in multiple yaml files with overlapping GPU wildcards (e.g., "*b200*" and "*gb200*").
Applied to files:
tests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.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:
tests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.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:
tests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.py
🧬 Code graph analysis (1)
tests/integration/defs/examples/test_ray.py (2)
tensorrt_llm/llmapi/llm_args.py (2)
model_dir(1496-1498)model_dir(1501-1505)tests/integration/defs/conftest.py (1)
llm_models_root(80-94)
🪛 Ruff (0.14.3)
tests/integration/defs/examples/test_ray.py
65-65: subprocess call with shell=True seems safe, but may be changed in the future; consider rewriting without shell
(S602)
65-65: Starting a process with a partial executable path
(S607)
67-67: subprocess call: check for execution of untrusted input
(S603)
68-72: Starting a process with a partial executable path
(S607)
⏰ 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 (6)
tests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.py (1)
17-18: Good refactoring to use centralized model path resolution.Replacing the hardcoded model path with
llm_models_root()makes the tests more maintainable and aligns with the PR objective to use cached models in Ray tests.Also applies to: 30-31
examples/ray_orchestrator/disaggregated/disagg_serving_local.sh (2)
6-6: LGTM: Clean addition of configurable model path.The
--modeloption with a sensible default makes the script more flexible while maintaining backward compatibility.Also applies to: 20-23, 33-33
86-86: LGTM: Consistent usage of MODEL_DIR variable.All hardcoded model paths are correctly replaced with
$MODEL_DIR, making the script consistent with the new command-line option.Also applies to: 132-132, 139-139
tests/integration/defs/examples/test_ray.py (3)
49-51: LGTM: Explicit model path for TinyLlama case.Making the TinyLlama model path explicit in the else block improves consistency and aligns with the PR objective to use cached models.
64-64: LGTM: Correct integration with script's new --model option.The test now properly passes the model directory to the disaggregated serving script via the new
--modelflag.Also applies to: 68-72
85-85: Model name format change is correct.The server extracts the basename from directory paths using
Path(model).name, so the model identifier in the curl payload should use only the basename. The change from"TinyLlama/TinyLlama-1.1B-Chat-v1.0"to"TinyLlama-1.1B-Chat-v1.0"aligns with how the disaggregated server exposes the model name in responses.
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Summary by CodeRabbit
New Features
--modelcommand-line option to enable customizable model path configuration in serving scripts.Tests
Description
Test Coverage
PR Checklist
Please review the following before submitting your PR:
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PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
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