[TRTLLM-6092][doc] Add LoRA feature usage doc #6603
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shaharmor98
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nv-guomingz and
amitz-nv
📝 WalkthroughWalkthroughA new documentation file detailing LoRA (Low-Rank Adaptation) support in the PyTorch backend of the tensorrt_llm library has been added. The document provides background, usage instructions, advanced scenarios, code examples, and configuration guidance for integrating and deploying LoRA adapters within the framework. Changes
Sequence Diagram(s)Not applicable: Documentation-only addition. Estimated code review effort🎯 1 (Trivial) | ⏱️ ~2 minutes Possibly related PRs
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Actionable comments posted: 0
🧹 Nitpick comments (4)
docs/source/torch/features/lora.md (4)
14-19: Standardise “LoRA” casing in section header & ToCLine 18 spells “LORA” (all-caps) while the rest of the document uses “LoRA”. Fixing the header keeps anchors predictable (
#trtllm-bench-with-lora) and avoids broken in-page links generated by Sphinx/Markdown.
68-96: Multi-LoRA example missessampling_paramsand uses singular keywordThe snippet re-uses a
sampling_paramsvariable that is only defined in the previous code block and passes it via the singular keywordlora_request, even though a list of two adapters is supplied. This may confuse readers or fail if copy-pasted verbatim.@@ outputs = llm.generate( - prompts, - sampling_params, - lora_request=[lora_req1, lora_req2] + prompts, + SamplingParams(max_tokens=50), # make snippet self-contained + lora_requests=[lora_req1, lora_req2] # plural keyword for list )Please verify the actual API name (
lora_requestvslora_requests) inLLM.generate()and update both basic and multi-LoRA examples for consistency.
138-154: Add note about known CPU-cache race conditionInternal learning (#5616) flagged a race in the
is_adapter_in_cpu_cache()path. A short warning here (e.g., “Concurrent adapter uploads may hit a known cache race; see issue #5616”) will prevent users from mis-diagnosing sporadic failures when experimenting withpeft_cache_config.
168-174: Command snippet breaks when copy-pastedThe back-slash continuation is on the wrong line, producing:
python -m tensorrt_llm.commands.serve /path/to/model \which the shell interprets as two separate commands. Suggest:
-python -m tensorrt_llm.commands.serve - /path/to/model \ - --extra_llm_api_options extra_llm_api_options.yaml +python -m tensorrt_llm.commands.serve /path/to/model \ + --extra_llm_api_options extra_llm_api_options.yaml
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🧠 Learnings (4)
📓 Common learnings
Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Learnt from: yibinl-nvidia
PR: NVIDIA/TensorRT-LLM#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.
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: in tensorrt_llm/executor/worker.py, the lora adapter cache optimization logic that checks `is_adapte...
Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Applied to files:
docs/source/torch/features/lora.md
📚 Learning: in tensorrt-llm, examples directory can have different dependency versions than the root requirement...
Learnt from: yibinl-nvidia
PR: NVIDIA/TensorRT-LLM#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:
docs/source/torch/features/lora.md
📚 Learning: in tensorrt-llm testing, it's common to have both cli flow tests (test_cli_flow.py) and pytorch api ...
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:
docs/source/torch/features/lora.md
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🔇 Additional comments (1)
docs/source/torch/features/lora.md (1)
199-224: YAML filename mismatch may confuse usersThe config block is saved to
extra_llm_api_options.yaml, but the bench command referencesextra-llm-api-options.yaml(hyphens vs underscores). Align the names or explicitly mention that they are interchangeable.-trtllm-bench --model $model_path throughput --dataset $dataset_path --extra_llm_api_options extra-llm-api-options.yaml ... +trtllm-bench --model $model_path throughput --dataset $dataset_path --extra_llm_api_options extra_llm_api_options.yaml ...
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Signed-off-by: Shahar Mor <[email protected]>
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Signed-off-by: Shahar Mor <[email protected]>
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