[Feat] Add UCM store compression module and compression config parametersFeature store compress

[xwLearnsLLM]

Purpose

This PR introduces a configurable compression module for the UCM store, designed to reduce TTFT (Time-To-First-Token) under SSD hit. By compressing stored BF16 tensors and supporting multi-threaded decompression, aim to lower I/O overhead and speed up cache access, improving end-to-end inference latency.

Modifications

• Added three new configuration parameters under ucm_connector_config to control compression behavior:
  • compress_ratio: Compression ratio (currently only 2.0x is supported)
  • data_type: Target data type (currently only BF16 is supported)
  • decompress_thread_num: Number of threads used for parallel decompression
• Implemented full compression/decompression pipeline based on KVfold coding
• Integrated the compression module into the existing UCM store pipeline
• Added YAML configuration support for compression feature toggling and tuning

Test

• Model: Qwen2.5-14B-Instruct
• Hardware: Kunpeng 920 5250 + 4 × Ascend 910B4 (tensor parallel = 4)
• Test Config: 0% hit on DRAM cache, 50% / 80% / 100% hit on local SSD.

Layer_wise (use_layerwise: true) :

input tokens	output tokens	batch size	odirect	TTFT/ms, 50% hit, w/o compression	TTFT/ms, 50% hit, with compression	TTFT reduction	TTFT/ms, 80% hit, w/o compression	TTFT/ms, 80% hit, with compression	TTFT reduction	TTFT/ms, 100% hit, w/o compression	TTFT/ms, 100% hit, with compression	TTFT reduction
4000	1	1	TRUE	357.99	341.97	4.47%	207.02	225.74	-9.04%	208.37	193.22	7.27%
8000	1	1	TRUE	531.6	578.86	-8.89%	304.06	321.29	-5.67%	350.01	331.22	5.37%
16000	1	1	TRUE	1236.44	1284.19	-3.86%	800.25	720.4	9.98%	611.74	532.57	12.94%
32000	1	1	TRUE	3278.62	3152.37	3.85%	2077.46	1807.59	12.99%	1204.62	959.57	20.34%
4000	1	8	TRUE	1339.06	1396.03	-4.25%	868.46	839.52	3.33%	1056.99	829.89	21.49%
8000	1	8	TRUE	2353.23	2373.6	-0.87%	1436.33	1356.39	5.57%	2024.04	1421.53	29.77%
16000	1	8	TRUE	5311.28	5548.89	-4.47%	3433.49	2829.16	17.60%	3808.22	2906.72	23.67%
32000	1	8	TRUE	13796.04	13660.42	0.98%	8353.08	7851.27	6.01%	6939.55	5274.13	24.00%
4000	1	16	TRUE	2280.48	2343.98	-2.78%	1428.27	1232.16	13.73%	2154.65	1342.75	37.68%
8000	1	16	TRUE	4414.05	4436.29	-0.50%	2639.71	2388.07	9.53%	4170.57	2646	36.56%
16000	1	16	TRUE	10144.97	9960.42	1.82%	6167.56	5084.08	17.57%	7170.8	4005.21	44.15%
32000	1	16	TRUE	25967.02	24714.92	4.82%	15550.23	12950.47	16.72%	12261.77	7504.62	38.80%

Block_wise (use_layerwise: false) :

input tokens	output tokens	batch size	odirect	TTFT/ms, 50% hit, w/o compression	TTFT/ms, 50% hit, with compression	TTFT reduction	TTFT/ms, 80% hit, w/o compression	TTFT/ms, 80% hit, with compression	TTFT reduction	TTFT/ms, 100% hit, w/o compression	TTFT/ms, 100% hit, with compression	TTFT reduction
4000	1	1	TRUE	423.87	406.85	4.02%	307.49	253.9	17.43%	260.84	198.13	24.04%
8000	1	1	TRUE	688.38	665.83	3.28%	507.52	419.18	17.41%	460.22	352.85	23.33%
16000	1	1	TRUE	1528.11	1479.79	3.16%	1083.55	874.61	19.28%	617.35	446.64	27.65%
32000	1	1	TRUE	3678.05	3426.42	6.84%	2327.42	2039.48	12.37%	1387.85	832.33	40.03%
4000	1	8	TRUE	1801.28	1673.41	7.10%	1261.3	1025.12	18.73%	1117.65	745.11	33.33%
8000	1	8	TRUE	3262.38	2952.3	9.50%	2344.01	1880.82	19.76%	2381.43	1314.63	44.80%
16000	1	8	TRUE	6982.66	6343.26	9.16%	4897.66	3862.16	21.14%	4157.12	2060.17	50.44%
32000	1	8	TRUE	16615.58	14960.37	9.96%	10797.82	8443.65	21.80%	7872.14	3896.58	50.50%
4000	1	16	TRUE	3181.24	2928.33	7.95%	2228.06	1775.39	20.32%	2402.1	1152.3	52.03%
8000	1	16	TRUE	6082.47	5437.54	10.60%	4213.28	3197.17	24.12%	4381.51	2386.19	45.54%
16000	1	16	TRUE	12832.2	11805.53	8.00%	8489.46	6721.34	20.83%	6231.51	3555.04	42.95%
32000	1	16	TRUE	29728.23	28228.75	5.04%	18745.21	15293.23	18.42%	13077.52	6831.96	47.76%