[PD Disaggregation] Fix cache messager performance problem & add kv transfer benchmark tool

[RichardWooSJTU]

Motivation

1. This PR fixes a critical performance issue in KV cache transfer where unnecessary conversion of src_block_ids to paddle.Tensor in Python caused significant performance degradation in pybind parsing. The issue was identified during KV cache transfer optimization work.
2. This PR introduces a comprehensive benchmark tool for measuring KV cache transfer performance between prefill and decode stages in splitwise inference. The benchmark helps evaluate the throughput and latency of RDMA-based KV cache transfers, which is critical for optimizing distributed inference performance.

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Modifications

1. Performance Fix in cache_messager.py:

   • Removed unnecessary paddle.to_tensor() conversion for src_block_ids and dest_block_ids
   • Changed timing from time.time() to time.perf_counter() for more accurate performance measurement
   • These changes eliminate the performance bottleneck in pybind layer

2. Added Benchmark Tool:

   • Introduced benchmark.py for comprehensive KV cache transfer performance testing
   • Provides throughput measurement capabilities for RDMA-based transfers
   • Includes configurable parameters and detailed result reporting

Usage or Command

# Performance test after fix
python benchmark.py --splitwise_role prefill --decode_ip 127.0.0.1 --num_layers 61 --blocks_per_transfer 64

Accuracy Tests

Checklist

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  • Tag list: [[FDConfig],[APIServer],[Engine], [Scheduler], [PD Disaggregation], [Executor], [Graph Optimization], [Speculative Decoding], [RL], [Models], [Quantization], [Loader], [OP], [KVCache], [DataProcessor], [BugFix], [Docs], [CI], [Optimization], [Feature], [Benchmark], [Others], [XPU], [HPU], [GCU], [DCU], [Iluvatar], [Metax]]
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[paddle-bot]

Thanks for your contribution!