[AutoDeploy] Tune KVCacheConfig of drafter for memory optimization

Proposal to improve performance

Currently, in DraftTarget speculative decoding, we pass along the KVCacheConfig that the target model is configured with to a separate draft model KV cache. This could lead to excessive memory being reserved for draft model KV cache, when the KV cache for the draft model can be made much smaller than for the target model (based on a ratio of number of attention layers between the two models, and number of draft tokens generated).

Report of performance regression

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Misc discussion on performance

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Your current environment (if you think it is necessary)

System Information:

• OS:
• Python version:
• CUDA version:
• GPU model(s):
• Driver version:
• TensorRT version:
• PyTorch version:
• TensorRT-LLM version:

Detailed output:

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