test: modify max_lora_rank of phi4_multimodal to 320 #6474
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🧹 Nitpick comments (1)
tests/integration/defs/perf/pytorch_model_config.py (1)
202-202: LGTM! Consider documenting the rationale for the 5x increase.The change correctly updates
max_lora_rankfrom 64 to 320 for phi_4_multimodal_instruct models. This is a significant increase that will allow for higher-rank LoRA adaptations but may impact memory usage and performance.Consider adding a comment explaining why 320 was chosen as the new maximum rank value for future maintainers:
lora_config['lora_config']['trtllm_modules_to_hf_modules'] = { "attn_qkv": "qkv_proj", "attn_dense": "o_proj", "mlp_h_to_4h": "gate_up_proj", "mlp_4h_to_h": "down_proj" } + # Increased from 64 to 320 to support higher-rank LoRA adaptations for phi4_multimodal lora_config['lora_config']['max_lora_rank'] = 320
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tests/integration/defs/perf/pytorch_model_config.py
🧠 Learnings (1)
tests/integration/defs/perf/pytorch_model_config.py (1)
Learnt from: amitz-nv
PR: #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.
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Signed-off-by: ruodil <[email protected]>
Signed-off-by: ruodil <[email protected]> Co-authored-by: Larry <[email protected]> Signed-off-by: Lanyu Liao <[email protected]>
Signed-off-by: ruodil <[email protected]> Co-authored-by: Larry <[email protected]>
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