[kv_offload] Session Aware Eviction Policy

[InbarShapira]

Purpose

Current vLLM v1 CPU KV-offload eviction policies (LRU, ARC) treat blocks as independent items. But a conversation's KV blocks are only useful as a whole chain — evicting one block in the middle forces recomputation of everything after it. SAE (Session-Aware Eviction) groups blocks stored by the same request into a session and evicts session-worst-first, tail-first, so shared prefixes outlive their suffixes. This targets the multi-turn / long-context workloads where cross-turn KV reuse matters and mid-chain evictions are the dominant TTFT tail source.

Benchmark Plan

Multi-turn benchmark exercising cross-turn KV reuse.

1. Build the workload. Resample 500 conversations with ≥ 6 turns from ShareGPT V3, seed pinned for reproducibility.

Source dataset:

wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

Resample to the benchmark workload:

python benchmarks/multi_turn/convert_sharegpt_to_openai.py \
  ShareGPT_V3_unfiltered_cleaned_split.json \
  sharegpt-full.json \
  --seed 42 \
  --max-items 500 \
  --min-turns 6 \
  --no-exclude-non-english

2. Configure the KV-offload connector.

export POLICY=sae   # or lru, arc

export KV_TRANSFER_CONFIG="{
    \"kv_connector\": \"OffloadingConnector\",
    \"kv_role\": \"kv_both\",
    \"kv_connector_extra_config\": {
      \"cpu_bytes_to_use\": 27917287424,
      \"eviction_policy\": \"$POLICY\"
    }
  }"

cpu_bytes_to_use=27917287424 = 26 GiB

eviction_policy selects the CPU-tier policy under test.

3. Start the vLLM server.

Run on: A100-SXM4-80GB, 1 gpu, 8 cpu, 32GB mem

vllm serve NousResearch/Hermes-3-Llama-3.1-8B \
  --port 8000 \
  --kv-transfer-config "$KV_TRANSFER_CONFIG" \
  --gpu-memory-utilization=0.5 \
  --disable-hybrid-kv-cache-manager

4. Run the multi-turn benchmark against the server.

python benchmarks/multi_turn/benchmark_serving_multi_turn.py \
  --url http://localhost:8000 \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --input-file sharegpt-full.json \
  --num-clients=4 \
  --max-active-conversations=128 \
  --output-file multi_turn_${POLICY}.json

Benchmark Results

Throughput and TTFT

Metric	LRU	ARC	SAE	SAE vs LRU	SAE vs ARC
Output throughput (tok/s)	297.0	302.8	309.1	+4.1%	+2.1%
Total throughput (tok/s)	2291.0	2347.6	2412.9	+5.3%	+2.8%
Requests/s	1.196	1.207	1.229	+2.8%	+1.8%
TTFT mean (ms)	156.19	145.28	131.73	-15.7%	-9.3%
TTFT p50 (ms)	85.04	87.88	88.86	+4.5%	+1.1%
TTFT p95 (ms)	683.40	608.45	501.94	-26.5%	-17.5%
TTFT p99 (ms)	947.86	840.81	694.21	-26.8%	-17.4%

Cache effectiveness

Metric	LRU	ARC	SAE	SAE vs LRU	SAE vs ARC
Lookups	27,287	79,823	146,273	—	—
Hits	23,894	76,421	142,846	—	—
Misses	3,393	3,402	3,427	—	—
Hit rate	87.6%	95.7%	97.7%	+11.5%	+2.1%
Evictions	299,738	219,937	49,175	-83.6%	-77.6%

Conclusion. vs LRU on ShareGPT V3 multi-turn: p95 TTFT −26.5%, p99 −26.8%, hit rate 87.6% → 97.7%, evictions 6.1× lower, +4.1% output throughput — whole-session-tail eviction holds shared prefixes across turns, so tail-latency users speed up, the cache stops thrashing, and throughput edges up rather than trading off.

Test Plan

Test Result

---

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @InbarShapira.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[mergify]

Documentation preview: https://vllm--50422.org.readthedocs.build/en/50422/

[InbarShapira]

Additional benchmark: gpt-oss-120b under GuideLLM

ref: #49152

Benchmark Plan

GuideLLM concurrent=64 sweep against a synthetic 4 k-prompt / 512-token
workload with a fixed prefix bucket, exercising the CPU KV-offload tier
under sustained back-pressure.

1. Configure the KV-offload connector.

export POLICY=sae   # or lru, arc

export KV_TRANSFER_CONFIG="{
    \"kv_connector\": \"OffloadingConnector\",
    \"kv_role\": \"kv_both\",
    \"kv_connector_extra_config\": {
      \"cpu_bytes_to_use\": 25769803776,
      \"eviction_policy\": \"$POLICY\"
    }
  }"

cpu_bytes_to_use=25769803776 = 25 GiB.

eviction_policy selects the CPU-tier policy under test.

2. Start the vLLM server.

Run on: 2× A100-SXM4-80GB, 1 gpu, 8 cpu, 32GB mem, --tensor-parallel-size=2.

vllm serve openai/gpt-oss-120b \
  --port 8000 \
  --kv-transfer-config "$KV_TRANSFER_CONFIG" \
  --tensor-parallel-size=2 \
  --gpu-memory-utilization=0.7 \
  --disable-hybrid-kv-cache-manager

3. Run GuideLLM against the server.

DATA='{"kind":"synthetic_text","prompt_tokens":4096,"output_tokens":512,"turns":5,"prefix_buckets":[{"bucket_weight":100,"prefix_count":256,"prefix_tokens":10000}]}'

guidellm benchmark \
  --target http://localhost:8000 \
  --backend "kind=openai_http,request_format=/v1/completions" \
  --profile "kind=concurrent,streams=64" \
  --constraint "kind=max_duration,seconds=700" \
  --seed "kind=static,value=889" \
  --data "$DATA" \
  --output-path guidellm_${POLICY}

Benchmark Results

Throughput and TTFT

Metric	LRU	ARC	SAE	SAE vs LRU	SAE vs ARC
Output throughput (tok/s)	195.8	190.5	211.7	+8.1%	+11.1%
Total throughput (tok/s)	8,336.5	8,039.0	9,396.1	+12.7%	+16.9%
Requests/s	0.379	0.369	0.410	+8.2%	+11.1%
TTFT mean (s)	81.377	83.478	77.232	-5.1%	-7.5%
TTFT p50 (s)	74.790	71.660	66.273	-11.4%	-7.5%
TTFT p95 (s)	150.041	152.615	148.829	-0.8%	-2.5%
TTFT p99 (s)	161.817	160.210	152.744	-5.6%	-4.7%

Cache effectiveness

Metric	LRU	ARC	SAE	SAE vs LRU	SAE vs ARC
Lookups	53,084	32,465	1,453,815	—	—
Hits	49,783	29,333	1,449,757	—	—
Misses	3,301	3,132	4,058	—	—
Hit rate	93.8%	90.4%	99.7%	+5.9 pp	+9.3 pp
Evictions	349,522	346,311	9,850	-97.2%	-97.2%

Conclusion. vs LRU on gpt-oss-120b + GuideLLM concurrent=64:
hit rate 93.8% → 99.7%, evictions 35× lower, +8.1% output throughput,
+12.7% total throughput, p99 TTFT −5.6%

[dannyharnik]

@InbarShapira Thanks for this contribution.
This PR is quite invasive in that it adds and changes APIs in the offloading manger and touches a significant number of files. In order to accept it we would like to consider adding the needed hooks in a way that will be less pervasive.

As for the value presented, I see modest improvements in a shareGPT based workload and more significant improvement in a synthetic multi-turn conversation workload. Questions about this:

• Is the ShareGPT workload the same as the one used in devising this method, or is it different?
• Do you think the synthetic workload benefits are due to the nature of the workload, or does it have something to do with the model (which is a hybrid model with SWA layers)?

[Etelis]

I agree with @dannyharnik,
The changes are invasive and modest improvements

[InbarShapira]

@dannyharnik

Is the ShareGPT workload the same as the one used in devising this method, or is it different?

It different - This method was devised using SWE-smith multi-turn coding workload (245 requests · 12 agents) via inference-perf
This workload is real SWE-agent coding trajectories replayed against a live inference endpoint. Each "agent" is a conversation: tool calls, growing context, typically ~19 turns. Agents run concurrently.

Do you think the synthetic workload benefits are due to the nature of the workload, or does it have something to do with the model (which is a hybrid model with SWA layers)?

I think its due to the nature of the workload
This is why I presented 2 different workloads

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @InbarShapira.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork