[OLD] [KV offload] Align SWA store sparsity with local-cache retention semantics

[procr1337]

Included in v20-r16 via local-inference-lab#218

This version is tested on FF v15 branch

Also open against local-inference-lab#89

v20-r15 rebase: https://github.com/procr1337/vllm/tree/fix/dsv4-offload_v20-r15

Purpose

For SWA groups, the offloading connector stores only a small "tail" run of blocks per sparsity segment, because _sliding_window_lookup only ever needs a consecutive run of sliding_window_size_in_blocks stored blocks. This is the CPU-side analog of the local GPU prefix cache's sparse retention (SlidingWindowManager.reachable_block_mask in vllm/v1/core/single_type_kv_cache_manager.py).

The store-side skip had diverged from reachable_block_mask in three ways:

1. EAGLE/MTP peek block was never stored. _lookup() requires sliding_window_size_in_blocks + 1 consecutive stored blocks for eagle groups (the "+1 peek" block, which is matched and then dropped), but the store path only retained sliding_window_size_in_blocks blocks per segment. A run long enough for an eagle lookup literally never existed, so eagle-group lookups always returned 0 — and since _lookup() converges over all groups, the overall hit was always 0.

2. Segment size ignored the retention interval. The connector always derived its sparsity segment from the full-attention block size (256 tokens), while the local GPU cache uses VLLM_PREFIX_CACHE_RETENTION_INTERVAL (4096 tokens) when configured. The local cache only retains blocks at retention boundaries, so the two sides disagreed 16× on where usable prefixes live.

3. No replay-boundary tail. With sparse retention, segment tails exist only once per retention interval, so replaying a prompt whose length is not near a boundary would fall back up to a whole interval (4095 tokens) of otherwise-cached prefix.

The Fix

Make the store-side filter semantically identical to SlidingWindowManager.reachable_block_mask, which the local GPU cache has already proven out. Three coordinated changes, all on the store side — the lookup path needs no changes:

1. Shifted eagle tail runs (_build_store_jobs). For eagle groups, retain tail + 1 blocks per segment, ending one block past the segment boundary (mirroring reachable_block_mask's shift = 1). After _lookup() matches the run and drops the peek block, the hit lands exactly on the segment boundary. This keeps convergence boundary-aligned by construction, so every converged hit is serviceable by the sparse store and the load path can never request a never-stored block.

2. Retention-aware segments (SchedulerOffloadConfig.from_spec). Segment size now follows the exact expression used by reachable_block_mask: alignment_tokens if the interval is unset, the interval itself if positive, and no skip at all (store everything, conservative) for 0. The eagle-aware need is also used for the "segment too small, store everything" degenerate check.

3. Replay-boundary tail (_replay_tail_end_block, mirroring reachable_block_mask's replay-boundary handling). When sparse retention is active, each request additionally retains one tail run per SWA group ending at the latest full-attention-aligned boundary of its prompt — pulled back far enough (_replay_reserve_tokens) that every eagle group's peek block is still a complete prompt block. Replays of a 49k prompt then restore up to the latest 256-token boundary instead of only the latest 4096-token boundary.

Test Plan

Tested with mtp:2, lucifer-cutlass, vllm main 5f8e73c + vllm-project#48303 + vllm-project#48304 + vllm-project#48317 + flashinfer-python 0.6.14 (assumed to be minimal viable patchset for vllm main). Should also work with https://github.com/local-inference-lab/rtx6kpro/blob/master/models/ds4dspark-v10.md, but a slightly different patchset was tested there

• TODO re-test with v10 image
• TODO DSpark should work, but untested
• TODO GLM-5.2 should be unaffected, but untested

Testing this with a running vLLM is a bit annoying. What I did is run with --kv-cache-memory-bytes 7G --max-model-len 50000 (and vllm-project#48317 applied) to get about 1 full length request worth of GPU cache. we can then probe both cache levels with a sweep of unique or prefix-shared requests. E.g.

make_request.py:

"""Usage: python make_request.py <N> <seed>
Sends a chat completion request with exactly N input tokens to localhost:8000.
The seed determines the prompt content (same seed = same prompt) and is also
passed to the model for reproducible sampling.
Outputs the raw JSON response.
"""

import sys
import json
import random
import urllib.request

# 26 letters, each " x" is exactly 1 token (verified via /tokenize)
_VOCAB = [" " + c for c in "abcdefghijklmnopqrstuvwxyz"]

def make_request(n_tokens: int, seed: int) -> dict:
    rng = random.Random(seed)
    # First token has no leading space; remaining tokens are drawn from _VOCAB
    first = rng.choice(_VOCAB).lstrip()
    rest = [rng.choice(_VOCAB) for _ in range(n_tokens - 1)]
    prompt = first + "".join(rest)

    payload = json.dumps({
        "model": "DeepSeek-V4-Flash",
        "messages": [{"role": "user", "content": prompt}],
        "max_completion_tokens": 5,
        "seed": seed,
    }).encode()

    req = urllib.request.Request(
        "http://localhost:8000/v1/chat/completions",
        data=payload,
        headers={"Content-Type": "application/json"},
    )
    with urllib.request.urlopen(req) as resp:
        return json.loads(resp.read())

def make_request_split(n_prefix: int, prefix_seed: int,
                       n_suffix: int, suffix_seed: int) -> dict:
    """Build a prompt with a shared prefix (keyed by prefix_seed) and a
    unique suffix (keyed by suffix_seed).  Same prefix_seed => identical
    prefix tokens, enabling prefix-cache sharing across requests."""
    rng_p = random.Random(prefix_seed)
    rng_s = random.Random(suffix_seed)

    prefix = rng_p.choice(_VOCAB).lstrip() + \
             "".join(rng_p.choice(_VOCAB) for _ in range(n_prefix - 1))
    suffix = "".join(rng_s.choice(_VOCAB) for _ in range(n_suffix))
    prompt = prefix + suffix

    # use suffix_seed as the API seed so AB/AC/AD are distinguishable
    payload = json.dumps({
        "model": "DeepSeek-V4-Flash",
        "messages": [{"role": "user", "content": prompt}],
        "max_completion_tokens": 5,
        "seed": suffix_seed,
    }).encode()

    req = urllib.request.Request(
        "http://localhost:8000/v1/chat/completions",
        data=payload,
        headers={"Content-Type": "application/json"},
    )
    with urllib.request.urlopen(req) as resp:
        return json.loads(resp.read())


if __name__ == "__main__":
    n = int(sys.argv[1])
    seed = int(sys.argv[2])
    print(json.dumps(make_request(n, seed), indent=2))

probe_gpu_cache.py:

#!/usr/bin/env python3
"""Probe how many N-token requests fit in GPU vs DRAM (offload) KV cache.

Makes K requests forward then replays in reverse, timing each request and
distinguishing GPU cache hits from external (DRAM offload) hits via metrics.

Usage: python probe_gpu_cache.py [N] [K] [run_seed]

run_seed is mixed into every per-request seed so repeated runs don't
cross-contaminate each other's cache entries.
"""

import sys
import time
import urllib.request
import json
from make_request import make_request

N = int(sys.argv[1]) if len(sys.argv) > 1 else 9000
K = int(sys.argv[2]) if len(sys.argv) > 2 else 15
RUN_SEED = int(sys.argv[3]) if len(sys.argv) > 3 else int(time.time())

METRICS_URL = "http://localhost:8000/metrics"

def get_metrics():
    with urllib.request.urlopen(METRICS_URL) as resp:
        text = resp.read().decode()
    metrics = {}
    for line in text.splitlines():
        if line.startswith("#"):
            continue
        if "{" in line:
            name_part, val = line.rsplit("}", 1)
            name = name_part.split("{")[0]
        else:
            parts = line.split()
            if len(parts) != 2:
                continue
            name, val = parts
        try:
            metrics[name] = float(val)
        except ValueError:
            pass
    return metrics

def probe_request(n, seed):
    m0 = get_metrics()
    t0 = time.perf_counter()
    resp = make_request(n, seed)
    elapsed = time.perf_counter() - t0
    m1 = get_metrics()

    cached_tokens = resp["usage"]["prompt_tokens_details"]["cached_tokens"]
    ext_delta = m1.get("vllm:external_prefix_cache_hits_total", 0) - \
                m0.get("vllm:external_prefix_cache_hits_total", 0)
    gpu_hits = cached_tokens - int(ext_delta)

    return {
        "seed": seed,
        "prompt_tokens": resp["usage"]["prompt_tokens"],
        "cached_total": cached_tokens,
        "gpu_hits": gpu_hits,
        "dram_hits": int(ext_delta),
        "elapsed_s": elapsed,
    }

def fmt(r):
    source = "miss"
    if r["dram_hits"] > 0 and r["gpu_hits"] > 0:
        source = f"GPU+DRAM"
    elif r["dram_hits"] > 0:
        source = "DRAM"
    elif r["gpu_hits"] > 0:
        source = "GPU"
    local = r['seed'] % 10000  # display just the local index portion
    return (f"  seed={local:4d}  prompt_tokens={r['prompt_tokens']}  "
            f"cached={r['cached_total']:5d} (gpu={r['gpu_hits']:5d} dram={r['dram_hits']:5d})"
            f"  {r['elapsed_s']:.2f}s  [{source}]")

print(f"run_seed={RUN_SEED}")
seeds = [RUN_SEED * 10000 + 11 * (i + 1) for i in range(K)]
requests = [(N, s) for s in seeds]

print(f"=== Forward pass (N={N}, K={K}) ===")
for n, seed in requests:
    r = probe_request(n, seed)
    print(fmt(r))

print()
print(f"=== Reverse pass (N={N}, K={K}) ===")
for n, seed in reversed(requests):
    r = probe_request(n, seed)
    print(fmt(r))

Test Result

With --kv-offloading-size 64 we can fit 70 50k requests into the external cache in our test case with 7GB of GPU KV cache:

$ python3 probe_gpu_cache.py 49000 100
  seed=  11  prompt_tokens=49004  cached=    0 (gpu=    0 dram=    0)  4.40s  [miss]
  seed=  22  prompt_tokens=49004  cached=    0 (gpu=    0 dram=    0)  4.41s  [miss]
  seed=  33  prompt_tokens=49004  cached=    0 (gpu=    0 dram=    0)  4.41s  [miss]
...
  seed=1078  prompt_tokens=49004  cached=    0 (gpu=    0 dram=    0)  4.52s  [miss]
  seed=1089  prompt_tokens=49004  cached=    0 (gpu=    0 dram=    0)  4.52s  [miss]
  seed=1100  prompt_tokens=49004  cached=    0 (gpu=    0 dram=    0)  4.48s  [miss]

=== Reverse pass (N=49000, K=100) ===
  seed=1100  prompt_tokens=49004  cached=48896 (gpu=48896 dram=    0)  0.54s  [GPU]
  seed=1089  prompt_tokens=49004  cached=48896 (gpu=    0 dram=48896)  0.44s  [DRAM]
  seed=1078  prompt_tokens=49004  cached=48896 (gpu=    0 dram=48896)  0.44s  [DRAM]
...
  seed= 352  prompt_tokens=49004  cached=48896 (gpu=    0 dram=48896)  0.44s  [DRAM]
  seed= 341  prompt_tokens=49004  cached=48896 (gpu=    0 dram=48896)  0.46s  [DRAM]
  seed= 330  prompt_tokens=49004  cached=    0 (gpu=    0 dram=    0)  4.38s  [miss]
  seed= 319  prompt_tokens=49004  cached=    0 (gpu=    0 dram=    0)  4.46s  [miss]
  seed= 308  prompt_tokens=49004  cached=    0 (gpu=    0 dram=    0)  4.47s  [miss]
...

Summary by CodeRabbit

• Bug Fixes
  • Improved offloaded sliding-window attention cache handling at replay boundaries.
  • Preserved the correct cache blocks for EAGLE and MTP workflows, including peek blocks needed during replay.
  • Respect the configured prefix-cache retention interval when aligning and retaining offloaded blocks.
  • Improved cache-hit behavior at full-attention segment boundaries.

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