[TRTLLM-9160][doc] add doc to llm_runtime.py #9303
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Signed-off-by: Yan Chunwei <[email protected]>
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📝 WalkthroughWalkthroughAdded comprehensive docstring to Changes
Estimated code review effort🎯 1 (Trivial) | ⏱️ ~3 minutes
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examples/llm-api/llm_runtime.py (1)
51-60: Excellent documentation addition!The docstring clearly explains the purpose and benefits of KV cache configuration. The three key points effectively communicate the value to users.
Minor optional suggestion: Consider being more specific about the API reference, e.g., "Please refer to the
KvCacheConfigAPI reference for more details."- Please refer to the api reference for more details. + Please refer to the `KvCacheConfig` API reference for more details.
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examples/llm-api/llm_runtime.py(1 hunks)
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Repo: NVIDIA/TensorRT-LLM PR: 6029
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examples/llm-api/llm_runtime.py (1)
tensorrt_llm/llmapi/llm_args.py (1)
KvCacheConfig(1426-1570)
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🔇 Additional comments (1)
examples/llm-api/llm_runtime.py (1)
65-73: Great readability improvement!The multi-line formatting with inline comments makes the KV cache configuration much clearer for users learning from this example. The comments accurately describe each parameter's purpose.
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PR_Github #25039 [ run ] triggered by Bot. Commit: |
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| def example_kv_cache_config(): | ||
| """ |
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Could you please put the document to at the beginning of the llm_runtime.py? Here is a reference: https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/llm-api/llm_kv_cache_offloading.py
| kv_cache_config=KvCacheConfig( | ||
| free_gpu_memory_fraction=0.5, | ||
| enable_block_reuse=True)) | ||
| llm_advanced = LLM( |
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Please submit this PR to release/1.1 branch. Thanks
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PR_Github #25039 [ run ] completed with state |
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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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