docs: v4 final results, error analysis, and updated journey
Add v4 sweep results and post-mortem analysis showing:
- direct-to-D path: 54.3% (1P7D) / 58.0% (2P6D) of requests now use
KVC cleanly. P50=0.5s and TTFT P50=0.043s; this path beats baseline
8DP across the board (P50 -24%, TTFT P50 -54%, TTFT P90 -79%).
- Overall vs baseline (errors+truncated excluded):
v4 2P6D P50=0.85s vs baseline 0.66s (28% slower).
Reason is not errors -- 35% of requests still hit
fallback-large-append-session-cap, where capacity-based
cap = usable_tokens / target_tokens evaluates to 1-2 (not 16)
for large agentic inputs.
- 9-10% errors on KVC variants are mooncake TCP transfer timeouts,
not SGLang logic bugs. Prefill log shows
"Failed to send kv chunk ... 32s timeout ... session not alive".
Errors concentrate in turn>=31 (large inputs) after run >44.8%.
Track:
- docs/KVC_DEBUG_JOURNEY_V1_TO_V4.md: append v4 results table,
per-mode breakdown, and error root cause.
- scripts/analysis/{analyze_v3,analyze_v4,analyze_errors,compare_no_error}.py
- outputs/qwen3-30b-tp1-v{3,4}*/exp*_summary.json (force-added,
small JSON; metrics.jsonl excluded due to size).
- outputs/qwen3-30b-tp1-v{3,4}*/sweep_results.txt
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
kzlin
@@ -174,11 +174,84 @@ def _decode_session_soft_cap(...) -> int:
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+ return max(1, min(16, usable_capacity_tokens // target_tokens))
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```
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7 D × 16 = 112 个 slot，远超 52 个 session 需求。预期 session-cap fallback 占比降到 <10%，整体 P50 向 direct-to-D 的 0.46s 收敛。
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7 D × 16 = 112 个 slot，远超 52 个 session 需求。
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实际数据见 `outputs/qwen3-30b-tp1-v4-cap16/`。
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### v4 实际结果（vs v3 1P7D / 2P6D）
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## 后续可以考虑的更深方案：让 D 自己决定 admission
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| 指标 | v3 1P7D | **v4 1P7D** | v3 2P6D | **v4 2P6D** | baseline 8DP |
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|------|:---:|:---:|:---:|:---:|:---:|
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| Errors | 363 (8%) | 435 (10%) | 9 (0%) | **403 (9%)** | 0 |
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| 截断 | 42 | 43 | 42 | 36 | 68 |
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| **direct-to-D** | 38.6% | **54.3%** | 30.5% | **58.0%** ⭐ | - |
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| **session-cap fallback** | 48.3% | 37.4% | 65.4% | **34.7%** | - |
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| Session reused | 1716 | 2180 | 1358 | **2348** | - |
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| KV transfer blocks | 62K | 53K | 79K | **51K** | - |
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| Mean | 4.88s | 4.21s | 3.58s | **2.51s** | 1.43s |
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| **P50** | 1.75s | 1.08s | 1.52s | **0.84s** | **0.65s** |
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| P90 | 12.67s | 13.38s | 9.23s | **6.51s** | 3.61s |
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| P99 | 28.72s | 24.45s | 18.70s | 18.34s | 8.38s |
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| **TTFT P50** | 0.36s | 0.056s | 0.33s | **0.051s** ⭐ | 0.094s |
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| TTFT P90 | 10.97s | 11.90s | 6.95s | **2.64s** | 0.26s |
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✓ direct-to-D 占比从 v3 的 30-38% 涨到 v4 的 54-58%
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✓ session 复用 +27% (1P7D) / +73% (2P6D)
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✓ KV transfer 量 -15% (1P7D) / -36% (2P6D)
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✓ TTFT P50 反超 baseline 46%（0.051s vs 0.094s）
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### Direct-to-D 路径全面碾压 baseline（KVC 真实价值）
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| Config | n | Lat P50 | Lat P90 | TTFT P50 | TTFT P90 |
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|--------|:---:|:---:|:---:|:---:|:---:|
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| baseline 8DP | 4381 | 0.66s | 3.65s | 0.094s | 0.256s |
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| v4 1P7D direct-to-D | 2179 | 0.495s | 3.03s | 0.044s | 0.055s |
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| **v4 2P6D direct-to-D** | **2348** | **0.499s** | **2.86s** | **0.043s** | **0.054s** |
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direct-to-D 子集相对 baseline：
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- P50 快 24-30%
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- P90 快 16-22%
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- TTFT P50 快 54%
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- TTFT P90 快 79%
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### 整体性能（去掉 errors 和 truncated）vs baseline
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| Config | clean | Mean | P50 | P90 | P99 |
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|--------|:---:|:---:|:---:|:---:|:---:|
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| baseline 8DP | 4381 | 1.45s | 0.66s | 3.65s | 8.38s |
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| v4 2P6D | 4010 | 2.53s | 0.85s | 6.55s | 18.33s |
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vs baseline：P50 慢 28%、P90 慢 80%、P99 慢 119%。即使错误率为 0，整体仍输 baseline——根因是 35% 请求被推到 fallback 路径。
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### 新瓶颈 1：35% 请求仍走 session-cap fallback
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抬到 16 后真实瓶颈是 capacity-based 计算：`min(16, usable_capacity_tokens // target_tokens)`。
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- `target_tokens = input + output`，agentic 里常见 50-100K
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- D 的 KV pool ≈ 100-150K tokens（80GB H100, mem_fraction=0.835）
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- `usable / target` = 1-2，远没到 16 → 真实 cap 是 capacity 算出来的小数字
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要解决必须改 capacity-based 估算逻辑（或上方案 D，让 D 自己决定）。
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### 新瓶颈 2：9-10% errors（mooncake 传输超时）
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P-side log 显示：
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```
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KVTransferError: Failed to send kv chunk of <bootstrap_room> to 10.45.7.165:40319
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Sync batch data transfer timeout after 32722558107ns (32 秒超时)
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Decode instance could be dead, remote mooncake session ... is not alive
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```
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特征：
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- 所有 errors 在 run 的 44.8% 之后出现（系统压力累积）
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- 98% errors 集中在 turn ≥ 31（大 input 的请求）
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- v3 cap=4 时 1P7D 已有 363 errors（仅 1 个 D 集中受冲击），v4 cap=16 把压力均匀分布但量级更大
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是 mooncake TCP loopback 在并发上去后撞超时，**不是 SGLang 逻辑 bug**。修复方向：
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1. 加长 mooncake transfer timeout（现在 32s）
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2. 限制并发 inflight transfer 数量
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3. 改用 RDMA（loopback 是单机模拟，生产环境换真 RDMA）
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4. chunked KV transfer
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## 后续可以考虑的更深方案：让 D 自己决定 admission（方案 D）
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v4 的硬 cap 抬高只是把数字调大，实际容量管理还是 replay 自己估算。代码里 `replay.py:_decode_session_soft_cap` 用 `target_tokens = input + output`（基于当前请求的 size）估算每个 session footprint，但：
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- agentic context 越攒越长，target_tokens 动态增长，cap 会随之缩小
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@@ -0,0 +1,88 @@
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{
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"actual_output_tokens_stats": {
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"count": 4086.0,
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"mean": 213.95105237395987,
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"p50": 83.0,
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"p90": 562.0,
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"p99": 1346.0
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},
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"cache_hit_request_count": 3929,
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"cached_tokens_stats": {
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"count": 4449.0,
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"mean": 22635.924702180266,
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"p50": 20010.0,
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"p90": 48002.0,
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"p99": 65424.0
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},
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"decode_request_priorities": {},
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"error_count": 363,
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"execution_modes": {
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"kvcache-centric": 363,
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"kvcache-direct-to-d-session": 1716,
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"pd-router-d-session-reseed": 23,
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"pd-router-fallback-d-backpressure": 12,
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"pd-router-fallback-large-append": 5,
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"pd-router-fallback-large-append-seed-filter-early-turn": 51,
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"pd-router-fallback-large-append-session-cap": 2148,
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"pd-router-fallback-no-d-capacity": 7,
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"pd-router-fallback-session-cap": 32,
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"pd-router-large-append-reseed": 39,
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"pd-router-large-append-reseed-after-eviction": 2,
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"pd-router-turn1-d-backpressure": 1,
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"pd-router-turn1-no-d-capacity": 3,
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"pd-router-turn1-seed": 34,
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"pd-router-turn1-session-cap": 13
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},
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"latency_stats_s": {
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"count": 4086.0,
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"mean": 4.8753733304192455,
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"p50": 1.754677688702941,
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"p90": 12.66968655679375,
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"p99": 28.717210091650486
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},
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"mechanisms": {
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"kvcache-centric": 4449
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},
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"per_decode_load": {
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"decode-0": 616,
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"decode-1": 658,
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"decode-2": 674,
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"decode-3": 582,
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"decode-4": 656,
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"decode-5": 662,
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"decode-6": 601
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},
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"per_prefill_load": {
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"prefill-0": 4449
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},
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"prefill_request_priorities": {
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"-100": 98,
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"100": 2272
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},
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"re_prefill_count": 0,
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"request_count": 4449,
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"reuse_expected_count": 4397,
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"reuse_observed_count": 4397,
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"router_url": "http://127.0.0.1:8000",
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"session_reset_count": 0,
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"session_reused_count": 1716,
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"total_actual_kv_transfer_blocks": 62123,
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"total_cached_tokens": 100707229,
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"total_kv_transfer_blocks": 105235,
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"tpot_stats_s": {
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"count": 4086.0,
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"mean": 0.005829451223571163,
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"p50": 0.005684156496173296,
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"p90": 0.007143743503740225,
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"p99": 0.008634991403068266
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},
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"trace_path": "outputs/qwen3-30b-tp1-v3-kvaware/kvcache-centric-kv-aware-worker-admission-20260428T095141Z/sampled-trace.jsonl",
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"truncated_request_count": 42,
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"ttft_stats_s": {
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"count": 4086.0,
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"mean": 3.5955862397812597,
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"p50": 0.36274072993546724,
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"p90": 10.972254231572151,
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"p99": 27.433656523004174
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}
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}
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@@ -0,0 +1,85 @@
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{
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"actual_output_tokens_stats": {
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"count": 4440.0,
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"mean": 225.87972972972972,
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"p50": 86.0,
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"p90": 576.0,
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"p99": 1347.0
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},
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"cache_hit_request_count": 4201,
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"cached_tokens_stats": {
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"count": 4449.0,
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"mean": 24345.55787817487,
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"p50": 21504.0,
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"p90": 48792.0,
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"p99": 69120.0
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},
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"decode_request_priorities": {},
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"error_count": 9,
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"execution_modes": {
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"kvcache-centric": 9,
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"kvcache-direct-to-d-session": 1358,
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"pd-router-d-session-reseed": 12,
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"pd-router-fallback-d-backpressure": 2,
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"pd-router-fallback-large-append-seed-filter-early-turn": 52,
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"pd-router-fallback-large-append-session-cap": 2902,
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"pd-router-fallback-session-cap": 25,
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"pd-router-large-append-reseed": 34,
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"pd-router-large-append-reseed-after-eviction": 4,
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"pd-router-turn1-d-backpressure": 1,
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"pd-router-turn1-seed": 30,
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"pd-router-turn1-session-cap": 20
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},
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"latency_stats_s": {
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"count": 4440.0,
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"mean": 3.582334662846558,
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"p50": 1.517257746309042,
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"p90": 9.225348330102861,
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"p99": 18.70269925892353
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},
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"mechanisms": {
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"kvcache-centric": 4449
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},
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"per_decode_load": {
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"decode-0": 710,
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"decode-1": 630,
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"decode-2": 763,
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"decode-3": 737,
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"decode-4": 879,
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"decode-5": 730
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},
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"per_prefill_load": {
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"prefill-0": 2225,
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"prefill-1": 2224
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},
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"prefill_request_priorities": {
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"-100": 80,
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"100": 3002
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},
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"re_prefill_count": 0,
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"request_count": 4449,
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"reuse_expected_count": 4397,
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"reuse_observed_count": 4397,
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"router_url": "http://127.0.0.1:8000",
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"session_reset_count": 0,
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"session_reused_count": 1358,
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"total_actual_kv_transfer_blocks": 78979,
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"total_cached_tokens": 108313387,
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"total_kv_transfer_blocks": 105235,
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"tpot_stats_s": {
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"count": 4440.0,
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"mean": 0.005882534704321737,
|
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"p50": 0.005807478777200416,
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"p90": 0.00712956755887717,
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"p99": 0.008372141476720572
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},
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"trace_path": "outputs/qwen3-30b-tp1-v3-kvaware/kvcache-centric-kv-aware-worker-admission-20260428T104343Z/sampled-trace.jsonl",
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"truncated_request_count": 42,
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"ttft_stats_s": {
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"count": 4440.0,
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"mean": 2.2045287611873334,
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"p50": 0.32809355948120356,
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"p90": 6.947275545448065,
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"p99": 16.705802395939827
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}
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}
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189
outputs/qwen3-30b-tp1-v3-kvaware/sweep_results.txt
Normal file
189
outputs/qwen3-30b-tp1-v3-kvaware/sweep_results.txt
Normal file
@@ -0,0 +1,189 @@
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[2026-04-28 17:51:41] Starting TP1 v3 sweep (KVC with kv-aware policy)
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[2026-04-28 17:51:41] Model: /mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/Qwen3-30B-A3B-Instruct-2507
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[2026-04-28 17:51:41] Trace: outputs/qwen35-swebench-50sess.jsonl (4449 requests, 52 sessions)
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[2026-04-28 17:51:41] Key change: --policy kv-aware for KVC (was --policy default in v2)
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[2026-04-28 17:51:41]
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[2026-04-28 17:51:41] === [EXP1] 1P7D KVC kv-aware ===
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[2026-04-28 18:43:43] === exp1_1p7d_kvc_kvaware COMPLETED ===
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[2026-04-28 18:43:43] Summary:
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{
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"actual_output_tokens_stats": {
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"count": 4086.0,
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"mean": 213.95105237395987,
|
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"p50": 83.0,
|
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"p90": 562.0,
|
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"p99": 1346.0
|
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},
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"cache_hit_request_count": 3929,
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"cached_tokens_stats": {
|
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"count": 4449.0,
|
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"mean": 22635.924702180266,
|
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"p50": 20010.0,
|
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"p90": 48002.0,
|
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"p99": 65424.0
|
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},
|
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"decode_request_priorities": {},
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"error_count": 363,
|
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"execution_modes": {
|
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"kvcache-centric": 363,
|
||||
"kvcache-direct-to-d-session": 1716,
|
||||
"pd-router-d-session-reseed": 23,
|
||||
"pd-router-fallback-d-backpressure": 12,
|
||||
"pd-router-fallback-large-append": 5,
|
||||
"pd-router-fallback-large-append-seed-filter-early-turn": 51,
|
||||
"pd-router-fallback-large-append-session-cap": 2148,
|
||||
"pd-router-fallback-no-d-capacity": 7,
|
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"pd-router-fallback-session-cap": 32,
|
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"pd-router-large-append-reseed": 39,
|
||||
"pd-router-large-append-reseed-after-eviction": 2,
|
||||
"pd-router-turn1-d-backpressure": 1,
|
||||
"pd-router-turn1-no-d-capacity": 3,
|
||||
"pd-router-turn1-seed": 34,
|
||||
"pd-router-turn1-session-cap": 13
|
||||
},
|
||||
"latency_stats_s": {
|
||||
"count": 4086.0,
|
||||
"mean": 4.8753733304192455,
|
||||
"p50": 1.754677688702941,
|
||||
"p90": 12.66968655679375,
|
||||
"p99": 28.717210091650486
|
||||
},
|
||||
"mechanisms": {
|
||||
"kvcache-centric": 4449
|
||||
},
|
||||
"per_decode_load": {
|
||||
"decode-0": 616,
|
||||
"decode-1": 658,
|
||||
"decode-2": 674,
|
||||
"decode-3": 582,
|
||||
"decode-4": 656,
|
||||
"decode-5": 662,
|
||||
"decode-6": 601
|
||||
},
|
||||
"per_prefill_load": {
|
||||
"prefill-0": 4449
|
||||
},
|
||||
"prefill_request_priorities": {
|
||||
"-100": 98,
|
||||
"100": 2272
|
||||
},
|
||||
"re_prefill_count": 0,
|
||||
"request_count": 4449,
|
||||
"reuse_expected_count": 4397,
|
||||
"reuse_observed_count": 4397,
|
||||
"router_url": "http://127.0.0.1:8000",
|
||||
"session_reset_count": 0,
|
||||
"session_reused_count": 1716,
|
||||
"total_actual_kv_transfer_blocks": 62123,
|
||||
"total_cached_tokens": 100707229,
|
||||
"total_kv_transfer_blocks": 105235,
|
||||
"tpot_stats_s": {
|
||||
"count": 4086.0,
|
||||
"mean": 0.005829451223571163,
|
||||
"p50": 0.005684156496173296,
|
||||
"p90": 0.007143743503740225,
|
||||
"p99": 0.008634991403068266
|
||||
},
|
||||
"trace_path": "outputs/qwen3-30b-tp1-v3-kvaware/kvcache-centric-kv-aware-worker-admission-20260428T095141Z/sampled-trace.jsonl",
|
||||
"truncated_request_count": 42,
|
||||
"ttft_stats_s": {
|
||||
"count": 4086.0,
|
||||
"mean": 3.5955862397812597,
|
||||
"p50": 0.36274072993546724,
|
||||
"p90": 10.972254231572151,
|
||||
"p99": 27.433656523004174
|
||||
}
|
||||
}
|
||||
[2026-04-28 18:43:43] Saved to outputs/qwen3-30b-tp1-v3-kvaware/exp1_1p7d_kvc_kvaware_summary.json + exp1_1p7d_kvc_kvaware_metrics.jsonl
|
||||
[2026-04-28 18:43:43]
|
||||
[2026-04-28 18:43:43] === [EXP2] 2P6D KVC kv-aware ===
|
||||
[2026-04-28 19:30:38] === exp2_2p6d_kvc_kvaware COMPLETED ===
|
||||
[2026-04-28 19:30:38] Summary:
|
||||
{
|
||||
"actual_output_tokens_stats": {
|
||||
"count": 4440.0,
|
||||
"mean": 225.87972972972972,
|
||||
"p50": 86.0,
|
||||
"p90": 576.0,
|
||||
"p99": 1347.0
|
||||
},
|
||||
"cache_hit_request_count": 4201,
|
||||
"cached_tokens_stats": {
|
||||
"count": 4449.0,
|
||||
"mean": 24345.55787817487,
|
||||
"p50": 21504.0,
|
||||
"p90": 48792.0,
|
||||
"p99": 69120.0
|
||||
},
|
||||
"decode_request_priorities": {},
|
||||
"error_count": 9,
|
||||
"execution_modes": {
|
||||
"kvcache-centric": 9,
|
||||
"kvcache-direct-to-d-session": 1358,
|
||||
"pd-router-d-session-reseed": 12,
|
||||
"pd-router-fallback-d-backpressure": 2,
|
||||
"pd-router-fallback-large-append-seed-filter-early-turn": 52,
|
||||
"pd-router-fallback-large-append-session-cap": 2902,
|
||||
"pd-router-fallback-session-cap": 25,
|
||||
"pd-router-large-append-reseed": 34,
|
||||
"pd-router-large-append-reseed-after-eviction": 4,
|
||||
"pd-router-turn1-d-backpressure": 1,
|
||||
"pd-router-turn1-seed": 30,
|
||||
"pd-router-turn1-session-cap": 20
|
||||
},
|
||||
"latency_stats_s": {
|
||||
"count": 4440.0,
|
||||
"mean": 3.582334662846558,
|
||||
"p50": 1.517257746309042,
|
||||
"p90": 9.225348330102861,
|
||||
"p99": 18.70269925892353
|
||||
},
|
||||
"mechanisms": {
|
||||
"kvcache-centric": 4449
|
||||
},
|
||||
"per_decode_load": {
|
||||
"decode-0": 710,
|
||||
"decode-1": 630,
|
||||
"decode-2": 763,
|
||||
"decode-3": 737,
|
||||
"decode-4": 879,
|
||||
"decode-5": 730
|
||||
},
|
||||
"per_prefill_load": {
|
||||
"prefill-0": 2225,
|
||||
"prefill-1": 2224
|
||||
},
|
||||
"prefill_request_priorities": {
|
||||
"-100": 80,
|
||||
"100": 3002
|
||||
},
|
||||
"re_prefill_count": 0,
|
||||
"request_count": 4449,
|
||||
"reuse_expected_count": 4397,
|
||||
"reuse_observed_count": 4397,
|
||||
"router_url": "http://127.0.0.1:8000",
|
||||
"session_reset_count": 0,
|
||||
"session_reused_count": 1358,
|
||||
"total_actual_kv_transfer_blocks": 78979,
|
||||
"total_cached_tokens": 108313387,
|
||||
"total_kv_transfer_blocks": 105235,
|
||||
"tpot_stats_s": {
|
||||
"count": 4440.0,
|
||||
"mean": 0.005882534704321737,
|
||||
"p50": 0.005807478777200416,
|
||||
"p90": 0.00712956755887717,
|
||||
"p99": 0.008372141476720572
|
||||
},
|
||||
"trace_path": "outputs/qwen3-30b-tp1-v3-kvaware/kvcache-centric-kv-aware-worker-admission-20260428T104343Z/sampled-trace.jsonl",
|
||||
"truncated_request_count": 42,
|
||||
"ttft_stats_s": {
|
||||
"count": 4440.0,
|
||||
"mean": 2.2045287611873334,
|
||||
"p50": 0.32809355948120356,
|
||||
"p90": 6.947275545448065,
|
||||
"p99": 16.705802395939827
|
||||
}
|
||||
}
|
||||
[2026-04-28 19:30:38] Saved to outputs/qwen3-30b-tp1-v3-kvaware/exp2_2p6d_kvc_kvaware_summary.json + exp2_2p6d_kvc_kvaware_metrics.jsonl
|
||||
[2026-04-28 19:30:38]
|
||||
[2026-04-28 19:30:38] === ALL TP1 V3 SWEEP EXPERIMENTS DONE ===
|
||||
@@ -0,0 +1,88 @@
|
||||
{
|
||||
"actual_output_tokens_stats": {
|
||||
"count": 4014.0,
|
||||
"mean": 215.048081714001,
|
||||
"p50": 83.0,
|
||||
"p90": 570.0,
|
||||
"p99": 1343.0
|
||||
},
|
||||
"cache_hit_request_count": 3865,
|
||||
"cached_tokens_stats": {
|
||||
"count": 4449.0,
|
||||
"mean": 21373.60867610699,
|
||||
"p50": 18429.0,
|
||||
"p90": 45643.0,
|
||||
"p99": 65088.0
|
||||
},
|
||||
"decode_request_priorities": {},
|
||||
"error_count": 435,
|
||||
"execution_modes": {
|
||||
"kvcache-centric": 435,
|
||||
"kvcache-direct-to-d-session": 2180,
|
||||
"pd-router-d-session-reseed": 44,
|
||||
"pd-router-d-session-reseed-after-eviction": 1,
|
||||
"pd-router-fallback-d-backpressure": 36,
|
||||
"pd-router-fallback-large-append": 35,
|
||||
"pd-router-fallback-large-append-seed-filter-early-turn": 52,
|
||||
"pd-router-fallback-large-append-session-cap": 1500,
|
||||
"pd-router-fallback-no-d-capacity": 13,
|
||||
"pd-router-fallback-session-cap": 43,
|
||||
"pd-router-large-append-reseed": 55,
|
||||
"pd-router-large-append-reseed-after-eviction": 3,
|
||||
"pd-router-turn1-d-backpressure": 1,
|
||||
"pd-router-turn1-no-d-capacity": 5,
|
||||
"pd-router-turn1-seed": 46
|
||||
},
|
||||
"latency_stats_s": {
|
||||
"count": 4014.0,
|
||||
"mean": 4.214657033050009,
|
||||
"p50": 1.0827504023909569,
|
||||
"p90": 13.380241627804935,
|
||||
"p99": 24.453291333280504
|
||||
},
|
||||
"mechanisms": {
|
||||
"kvcache-centric": 4449
|
||||
},
|
||||
"per_decode_load": {
|
||||
"decode-0": 690,
|
||||
"decode-1": 599,
|
||||
"decode-2": 660,
|
||||
"decode-3": 584,
|
||||
"decode-4": 606,
|
||||
"decode-5": 646,
|
||||
"decode-6": 664
|
||||
},
|
||||
"per_prefill_load": {
|
||||
"prefill-0": 4449
|
||||
},
|
||||
"prefill_request_priorities": {
|
||||
"-100": 149,
|
||||
"100": 1685
|
||||
},
|
||||
"re_prefill_count": 0,
|
||||
"request_count": 4449,
|
||||
"reuse_expected_count": 4397,
|
||||
"reuse_observed_count": 4397,
|
||||
"router_url": "http://127.0.0.1:8000",
|
||||
"session_reset_count": 0,
|
||||
"session_reused_count": 2180,
|
||||
"total_actual_kv_transfer_blocks": 52857,
|
||||
"total_cached_tokens": 95091185,
|
||||
"total_kv_transfer_blocks": 105235,
|
||||
"tpot_stats_s": {
|
||||
"count": 4014.0,
|
||||
"mean": 0.005804301410418847,
|
||||
"p50": 0.005607025208882987,
|
||||
"p90": 0.007293824862528552,
|
||||
"p99": 0.008864479259402893
|
||||
},
|
||||
"trace_path": "outputs/qwen3-30b-tp1-v4-cap16/kvcache-centric-kv-aware-worker-admission-20260428T125022Z/sampled-trace.jsonl",
|
||||
"truncated_request_count": 43,
|
||||
"ttft_stats_s": {
|
||||
"count": 4014.0,
|
||||
"mean": 2.915135478307124,
|
||||
"p50": 0.05643345229327679,
|
||||
"p90": 11.900803190656006,
|
||||
"p99": 22.758968392387033
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,86 @@
|
||||
{
|
||||
"actual_output_tokens_stats": {
|
||||
"count": 4046.0,
|
||||
"mean": 224.65002471576867,
|
||||
"p50": 84.0,
|
||||
"p90": 576.0,
|
||||
"p99": 1349.0
|
||||
},
|
||||
"cache_hit_request_count": 3925,
|
||||
"cached_tokens_stats": {
|
||||
"count": 4449.0,
|
||||
"mean": 22852.7439874129,
|
||||
"p50": 19584.0,
|
||||
"p90": 49009.0,
|
||||
"p99": 67320.0
|
||||
},
|
||||
"decode_request_priorities": {},
|
||||
"error_count": 403,
|
||||
"execution_modes": {
|
||||
"kvcache-centric": 403,
|
||||
"kvcache-direct-to-d-session": 2348,
|
||||
"pd-router-d-session-reseed": 28,
|
||||
"pd-router-fallback-d-backpressure": 7,
|
||||
"pd-router-fallback-large-append": 68,
|
||||
"pd-router-fallback-large-append-seed-filter-early-turn": 45,
|
||||
"pd-router-fallback-large-append-session-cap": 1403,
|
||||
"pd-router-fallback-no-d-capacity": 9,
|
||||
"pd-router-fallback-session-cap": 25,
|
||||
"pd-router-large-append-reseed": 57,
|
||||
"pd-router-large-append-reseed-after-eviction": 6,
|
||||
"pd-router-turn1-no-d-capacity": 1,
|
||||
"pd-router-turn1-seed": 49
|
||||
},
|
||||
"latency_stats_s": {
|
||||
"count": 4046.0,
|
||||
"mean": 2.505981629502371,
|
||||
"p50": 0.8372491216287017,
|
||||
"p90": 6.5139341270551085,
|
||||
"p99": 18.335972285829484
|
||||
},
|
||||
"mechanisms": {
|
||||
"kvcache-centric": 4449
|
||||
},
|
||||
"per_decode_load": {
|
||||
"decode-0": 767,
|
||||
"decode-1": 680,
|
||||
"decode-2": 906,
|
||||
"decode-3": 818,
|
||||
"decode-4": 800,
|
||||
"decode-5": 478
|
||||
},
|
||||
"per_prefill_load": {
|
||||
"prefill-0": 2225,
|
||||
"prefill-1": 2224
|
||||
},
|
||||
"prefill_request_priorities": {
|
||||
"-100": 140,
|
||||
"100": 1558
|
||||
},
|
||||
"re_prefill_count": 0,
|
||||
"request_count": 4449,
|
||||
"reuse_expected_count": 4397,
|
||||
"reuse_observed_count": 4397,
|
||||
"router_url": "http://127.0.0.1:8000",
|
||||
"session_reset_count": 0,
|
||||
"session_reused_count": 2348,
|
||||
"total_actual_kv_transfer_blocks": 50727,
|
||||
"total_cached_tokens": 101671858,
|
||||
"total_kv_transfer_blocks": 105235,
|
||||
"tpot_stats_s": {
|
||||
"count": 4046.0,
|
||||
"mean": 0.005708743129332261,
|
||||
"p50": 0.005565466725497757,
|
||||
"p90": 0.006912594398356141,
|
||||
"p99": 0.008102089307750717
|
||||
},
|
||||
"trace_path": "outputs/qwen3-30b-tp1-v4-cap16/kvcache-centric-kv-aware-worker-admission-20260428T134057Z/sampled-trace.jsonl",
|
||||
"truncated_request_count": 36,
|
||||
"ttft_stats_s": {
|
||||
"count": 4046.0,
|
||||
"mean": 1.1653790952959129,
|
||||
"p50": 0.05140436999499798,
|
||||
"p90": 2.6447059931233525,
|
||||
"p99": 15.121314341202378
|
||||
}
|
||||
}
|
||||
190
outputs/qwen3-30b-tp1-v4-cap16/sweep_results.txt
Normal file
190
outputs/qwen3-30b-tp1-v4-cap16/sweep_results.txt
Normal file
@@ -0,0 +1,190 @@
|
||||
[2026-04-28 20:50:21] Starting TP1 v4 sweep (KVC kv-aware, session soft_cap raised 4->16)
|
||||
[2026-04-28 20:50:21] Model: /mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/Qwen3-30B-A3B-Instruct-2507
|
||||
[2026-04-28 20:50:21] Trace: outputs/qwen35-swebench-50sess.jsonl (4449 requests, 52 sessions)
|
||||
[2026-04-28 20:50:21] Key change: _decode_session_soft_cap now min(16, ...) instead of min(4, ...)
|
||||
[2026-04-28 20:50:21]
|
||||
[2026-04-28 20:50:21] === [EXP1] 1P7D KVC kv-aware cap=16 ===
|
||||
[2026-04-28 21:40:57] === exp1_1p7d_kvc_cap16 COMPLETED ===
|
||||
[2026-04-28 21:40:57] Summary:
|
||||
{
|
||||
"actual_output_tokens_stats": {
|
||||
"count": 4014.0,
|
||||
"mean": 215.048081714001,
|
||||
"p50": 83.0,
|
||||
"p90": 570.0,
|
||||
"p99": 1343.0
|
||||
},
|
||||
"cache_hit_request_count": 3865,
|
||||
"cached_tokens_stats": {
|
||||
"count": 4449.0,
|
||||
"mean": 21373.60867610699,
|
||||
"p50": 18429.0,
|
||||
"p90": 45643.0,
|
||||
"p99": 65088.0
|
||||
},
|
||||
"decode_request_priorities": {},
|
||||
"error_count": 435,
|
||||
"execution_modes": {
|
||||
"kvcache-centric": 435,
|
||||
"kvcache-direct-to-d-session": 2180,
|
||||
"pd-router-d-session-reseed": 44,
|
||||
"pd-router-d-session-reseed-after-eviction": 1,
|
||||
"pd-router-fallback-d-backpressure": 36,
|
||||
"pd-router-fallback-large-append": 35,
|
||||
"pd-router-fallback-large-append-seed-filter-early-turn": 52,
|
||||
"pd-router-fallback-large-append-session-cap": 1500,
|
||||
"pd-router-fallback-no-d-capacity": 13,
|
||||
"pd-router-fallback-session-cap": 43,
|
||||
"pd-router-large-append-reseed": 55,
|
||||
"pd-router-large-append-reseed-after-eviction": 3,
|
||||
"pd-router-turn1-d-backpressure": 1,
|
||||
"pd-router-turn1-no-d-capacity": 5,
|
||||
"pd-router-turn1-seed": 46
|
||||
},
|
||||
"latency_stats_s": {
|
||||
"count": 4014.0,
|
||||
"mean": 4.214657033050009,
|
||||
"p50": 1.0827504023909569,
|
||||
"p90": 13.380241627804935,
|
||||
"p99": 24.453291333280504
|
||||
},
|
||||
"mechanisms": {
|
||||
"kvcache-centric": 4449
|
||||
},
|
||||
"per_decode_load": {
|
||||
"decode-0": 690,
|
||||
"decode-1": 599,
|
||||
"decode-2": 660,
|
||||
"decode-3": 584,
|
||||
"decode-4": 606,
|
||||
"decode-5": 646,
|
||||
"decode-6": 664
|
||||
},
|
||||
"per_prefill_load": {
|
||||
"prefill-0": 4449
|
||||
},
|
||||
"prefill_request_priorities": {
|
||||
"-100": 149,
|
||||
"100": 1685
|
||||
},
|
||||
"re_prefill_count": 0,
|
||||
"request_count": 4449,
|
||||
"reuse_expected_count": 4397,
|
||||
"reuse_observed_count": 4397,
|
||||
"router_url": "http://127.0.0.1:8000",
|
||||
"session_reset_count": 0,
|
||||
"session_reused_count": 2180,
|
||||
"total_actual_kv_transfer_blocks": 52857,
|
||||
"total_cached_tokens": 95091185,
|
||||
"total_kv_transfer_blocks": 105235,
|
||||
"tpot_stats_s": {
|
||||
"count": 4014.0,
|
||||
"mean": 0.005804301410418847,
|
||||
"p50": 0.005607025208882987,
|
||||
"p90": 0.007293824862528552,
|
||||
"p99": 0.008864479259402893
|
||||
},
|
||||
"trace_path": "outputs/qwen3-30b-tp1-v4-cap16/kvcache-centric-kv-aware-worker-admission-20260428T125022Z/sampled-trace.jsonl",
|
||||
"truncated_request_count": 43,
|
||||
"ttft_stats_s": {
|
||||
"count": 4014.0,
|
||||
"mean": 2.915135478307124,
|
||||
"p50": 0.05643345229327679,
|
||||
"p90": 11.900803190656006,
|
||||
"p99": 22.758968392387033
|
||||
}
|
||||
}
|
||||
[2026-04-28 21:40:57] Saved to outputs/qwen3-30b-tp1-v4-cap16/exp1_1p7d_kvc_cap16_summary.json + exp1_1p7d_kvc_cap16_metrics.jsonl
|
||||
[2026-04-28 21:40:57]
|
||||
[2026-04-28 21:40:57] === [EXP2] 2P6D KVC kv-aware cap=16 ===
|
||||
[2026-04-28 22:27:53] === exp2_2p6d_kvc_cap16 COMPLETED ===
|
||||
[2026-04-28 22:27:53] Summary:
|
||||
{
|
||||
"actual_output_tokens_stats": {
|
||||
"count": 4046.0,
|
||||
"mean": 224.65002471576867,
|
||||
"p50": 84.0,
|
||||
"p90": 576.0,
|
||||
"p99": 1349.0
|
||||
},
|
||||
"cache_hit_request_count": 3925,
|
||||
"cached_tokens_stats": {
|
||||
"count": 4449.0,
|
||||
"mean": 22852.7439874129,
|
||||
"p50": 19584.0,
|
||||
"p90": 49009.0,
|
||||
"p99": 67320.0
|
||||
},
|
||||
"decode_request_priorities": {},
|
||||
"error_count": 403,
|
||||
"execution_modes": {
|
||||
"kvcache-centric": 403,
|
||||
"kvcache-direct-to-d-session": 2348,
|
||||
"pd-router-d-session-reseed": 28,
|
||||
"pd-router-fallback-d-backpressure": 7,
|
||||
"pd-router-fallback-large-append": 68,
|
||||
"pd-router-fallback-large-append-seed-filter-early-turn": 45,
|
||||
"pd-router-fallback-large-append-session-cap": 1403,
|
||||
"pd-router-fallback-no-d-capacity": 9,
|
||||
"pd-router-fallback-session-cap": 25,
|
||||
"pd-router-large-append-reseed": 57,
|
||||
"pd-router-large-append-reseed-after-eviction": 6,
|
||||
"pd-router-turn1-no-d-capacity": 1,
|
||||
"pd-router-turn1-seed": 49
|
||||
},
|
||||
"latency_stats_s": {
|
||||
"count": 4046.0,
|
||||
"mean": 2.505981629502371,
|
||||
"p50": 0.8372491216287017,
|
||||
"p90": 6.5139341270551085,
|
||||
"p99": 18.335972285829484
|
||||
},
|
||||
"mechanisms": {
|
||||
"kvcache-centric": 4449
|
||||
},
|
||||
"per_decode_load": {
|
||||
"decode-0": 767,
|
||||
"decode-1": 680,
|
||||
"decode-2": 906,
|
||||
"decode-3": 818,
|
||||
"decode-4": 800,
|
||||
"decode-5": 478
|
||||
},
|
||||
"per_prefill_load": {
|
||||
"prefill-0": 2225,
|
||||
"prefill-1": 2224
|
||||
},
|
||||
"prefill_request_priorities": {
|
||||
"-100": 140,
|
||||
"100": 1558
|
||||
},
|
||||
"re_prefill_count": 0,
|
||||
"request_count": 4449,
|
||||
"reuse_expected_count": 4397,
|
||||
"reuse_observed_count": 4397,
|
||||
"router_url": "http://127.0.0.1:8000",
|
||||
"session_reset_count": 0,
|
||||
"session_reused_count": 2348,
|
||||
"total_actual_kv_transfer_blocks": 50727,
|
||||
"total_cached_tokens": 101671858,
|
||||
"total_kv_transfer_blocks": 105235,
|
||||
"tpot_stats_s": {
|
||||
"count": 4046.0,
|
||||
"mean": 0.005708743129332261,
|
||||
"p50": 0.005565466725497757,
|
||||
"p90": 0.006912594398356141,
|
||||
"p99": 0.008102089307750717
|
||||
},
|
||||
"trace_path": "outputs/qwen3-30b-tp1-v4-cap16/kvcache-centric-kv-aware-worker-admission-20260428T134057Z/sampled-trace.jsonl",
|
||||
"truncated_request_count": 36,
|
||||
"ttft_stats_s": {
|
||||
"count": 4046.0,
|
||||
"mean": 1.1653790952959129,
|
||||
"p50": 0.05140436999499798,
|
||||
"p90": 2.6447059931233525,
|
||||
"p99": 15.121314341202378
|
||||
}
|
||||
}
|
||||
[2026-04-28 22:27:53] Saved to outputs/qwen3-30b-tp1-v4-cap16/exp2_2p6d_kvc_cap16_summary.json + exp2_2p6d_kvc_cap16_metrics.jsonl
|
||||
[2026-04-28 22:27:53]
|
||||
[2026-04-28 22:27:53] === ALL TP1 V4 SWEEP EXPERIMENTS DONE ===
|
||||
83
scripts/analysis/analyze_errors.py
Normal file
83
scripts/analysis/analyze_errors.py
Normal file
@@ -0,0 +1,83 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Deep dive into v4 errors: which path, which D, which session, which turn."""
|
||||
import json
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from collections import Counter, defaultdict
|
||||
|
||||
BASE = Path(__file__).parent
|
||||
|
||||
def load_rows(jsonl_path):
|
||||
rows = []
|
||||
with open(jsonl_path) as f:
|
||||
for line in f:
|
||||
rows.append(json.loads(line))
|
||||
return rows
|
||||
|
||||
# Compare v3 and v4 errors
|
||||
for label, path in [
|
||||
("v3 1P7D", BASE.parent / "qwen3-30b-tp1-v3-kvaware/exp1_1p7d_kvc_kvaware_metrics.jsonl"),
|
||||
("v4 1P7D", BASE / "exp1_1p7d_kvc_cap16_metrics.jsonl"),
|
||||
("v3 2P6D", BASE.parent / "qwen3-30b-tp1-v3-kvaware/exp2_2p6d_kvc_kvaware_metrics.jsonl"),
|
||||
("v4 2P6D", BASE / "exp2_2p6d_kvc_cap16_metrics.jsonl"),
|
||||
]:
|
||||
if not path.exists():
|
||||
print(f"\nSKIP {label}: {path} not found")
|
||||
continue
|
||||
rows = load_rows(path)
|
||||
err = [r for r in rows if r.get("error") is not None]
|
||||
print(f"\n========== {label} ({len(err)} errors / {len(rows)} total = {len(err)/len(rows)*100:.1f}%) ==========")
|
||||
|
||||
# Error finish_reason distribution
|
||||
fr_counter = Counter()
|
||||
for r in err:
|
||||
fr = str(r.get("finish_reason") or r.get("error") or "?")
|
||||
fr_counter[fr[:80]] += 1
|
||||
print(f"finish_reason distribution:")
|
||||
for fr, cnt in fr_counter.most_common():
|
||||
print(f" {cnt:>4}x {fr}")
|
||||
|
||||
# Errors by execution mode (these are aborted before mode assignment usually)
|
||||
mode_counter = Counter(r.get("execution_mode", "?") for r in err)
|
||||
print(f"\nerror by execution_mode:")
|
||||
for mode, cnt in mode_counter.most_common():
|
||||
print(f" {cnt:>4}x {mode}")
|
||||
|
||||
# Errors per D worker
|
||||
dw_counter = Counter(r.get("assigned_decode_node", "?") for r in err)
|
||||
print(f"\nerror per assigned_decode_node:")
|
||||
for dw, cnt in dw_counter.most_common():
|
||||
print(f" {cnt:>4}x {dw}")
|
||||
|
||||
# Errors by turn distribution
|
||||
turn_counter = Counter(r.get("turn_id", -1) for r in err)
|
||||
early = sum(c for t, c in turn_counter.items() if t <= 5)
|
||||
mid = sum(c for t, c in turn_counter.items() if 5 < t <= 30)
|
||||
late = sum(c for t, c in turn_counter.items() if t > 30)
|
||||
print(f"\nerror by turn: early(0-5)={early} mid(6-30)={mid} late(31+)={late}")
|
||||
|
||||
# Per-session error rate
|
||||
per_sess_err = defaultdict(int)
|
||||
per_sess_total = defaultdict(int)
|
||||
for r in rows:
|
||||
per_sess_total[r["session_id"]] += 1
|
||||
if r.get("error") is not None:
|
||||
per_sess_err[r["session_id"]] += 1
|
||||
sess_with_err = [(sid, per_sess_err[sid], per_sess_total[sid]) for sid in per_sess_err]
|
||||
sess_with_err.sort(key=lambda x: -x[1])
|
||||
print(f"\ntop 5 sessions by error count:")
|
||||
for sid, e, t in sess_with_err[:5]:
|
||||
print(f" session {sid}: {e}/{t} errors ({e/t*100:.0f}%)")
|
||||
|
||||
# Errors timeline: are they bursty?
|
||||
err_ts = sorted([r.get("trace_timestamp_s", 0) for r in err])
|
||||
if err_ts:
|
||||
first_ts = err_ts[0]
|
||||
last_ts = err_ts[-1]
|
||||
all_ts = sorted([r.get("trace_timestamp_s", 0) for r in rows])
|
||||
first_all = all_ts[0]
|
||||
last_all = all_ts[-1]
|
||||
run_duration = last_all - first_all
|
||||
err_first_pct = (err_ts[0] - first_all) / run_duration * 100 if run_duration > 0 else 0
|
||||
err_last_pct = (err_ts[-1] - first_all) / run_duration * 100 if run_duration > 0 else 0
|
||||
print(f"\nerror time range (% of run): {err_first_pct:.1f}% - {err_last_pct:.1f}%")
|
||||
89
scripts/analysis/analyze_v3.py
Normal file
89
scripts/analysis/analyze_v3.py
Normal file
@@ -0,0 +1,89 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Analyze v3 (kv-aware) results — find why fallback-large-append-session-cap dominates."""
|
||||
import json
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from collections import Counter, defaultdict
|
||||
|
||||
BASE = Path(__file__).parent
|
||||
|
||||
def load_rows(jsonl_path):
|
||||
rows = []
|
||||
with open(jsonl_path) as f:
|
||||
for line in f:
|
||||
rows.append(json.loads(line))
|
||||
return rows
|
||||
|
||||
exp1 = load_rows(BASE / "exp1_1p7d_kvc_kvaware_metrics.jsonl")
|
||||
exp2 = load_rows(BASE / "exp2_2p6d_kvc_kvaware_metrics.jsonl")
|
||||
|
||||
for name, rows in [("Exp1 1P7D", exp1), ("Exp2 2P6D", exp2)]:
|
||||
print(f"\n========== {name} ==========")
|
||||
ok = [r for r in rows if r.get("error") is None]
|
||||
|
||||
# Execution mode breakdown by latency
|
||||
modes = Counter(r["execution_mode"] for r in ok)
|
||||
print(f"\nExecution modes (n={len(ok)}):")
|
||||
for mode, count in modes.most_common():
|
||||
mode_rows = [r for r in ok if r["execution_mode"] == mode]
|
||||
lats = [r["latency_s"] for r in mode_rows]
|
||||
ttfts = [r["ttft_s"] for r in mode_rows]
|
||||
print(f" {mode}: n={count} ({count/len(ok)*100:.1f}%) "
|
||||
f"lat P50={np.percentile(lats,50):.3f}s P90={np.percentile(lats,90):.3f}s | "
|
||||
f"ttft P50={np.percentile(ttfts,50):.3f}s P90={np.percentile(ttfts,90):.3f}s")
|
||||
|
||||
# Per-D session distribution
|
||||
per_d_sessions = defaultdict(set)
|
||||
for r in ok:
|
||||
d = r.get("assigned_decode_node", "?")
|
||||
per_d_sessions[d].add(r["session_id"])
|
||||
print(f"\nSessions per D worker:")
|
||||
for d in sorted(per_d_sessions.keys()):
|
||||
print(f" {d}: {len(per_d_sessions[d])} unique sessions")
|
||||
|
||||
# session-cap fallback analysis
|
||||
sc_rows = [r for r in ok if r["execution_mode"] == "pd-router-fallback-large-append-session-cap"]
|
||||
if sc_rows:
|
||||
print(f"\nSession-cap fallback details (n={len(sc_rows)}):")
|
||||
# Which sessions hit this most?
|
||||
sc_per_sess = Counter(r["session_id"] for r in sc_rows)
|
||||
print(f" Sessions hitting session-cap (top 5):")
|
||||
for sid, cnt in sc_per_sess.most_common(5):
|
||||
print(f" session {sid}: {cnt} times")
|
||||
# Per-D distribution
|
||||
sc_per_d = Counter(r.get("assigned_decode_node", "?") for r in sc_rows)
|
||||
print(f" Per-D distribution: {dict(sc_per_d.most_common())}")
|
||||
# Input length distribution
|
||||
inp = [r.get("input_length", 0) for r in sc_rows]
|
||||
print(f" Input length: P50={np.percentile(inp,50):.0f} P90={np.percentile(inp,90):.0f}")
|
||||
# Turn distribution
|
||||
turns = Counter(r.get("turn_id", -1) for r in sc_rows)
|
||||
print(f" Turn distribution (top 5): {dict(turns.most_common(5))}")
|
||||
|
||||
# Direct-to-D analysis (ideal path)
|
||||
dd_rows = [r for r in ok if r["execution_mode"] == "kvcache-direct-to-d-session"]
|
||||
if dd_rows:
|
||||
lats = [r["latency_s"] for r in dd_rows]
|
||||
ttfts = [r["ttft_s"] for r in dd_rows]
|
||||
kv_blocks = [r.get("actual_kv_transfer_blocks", 0) for r in dd_rows]
|
||||
cached = [r.get("cached_tokens", 0) for r in dd_rows]
|
||||
print(f"\nDirect-to-D details (n={len(dd_rows)}):")
|
||||
print(f" lat P50={np.percentile(lats,50):.3f}s P90={np.percentile(lats,90):.3f}s P99={np.percentile(lats,99):.3f}s")
|
||||
print(f" ttft P50={np.percentile(ttfts,50):.3f}s P90={np.percentile(ttfts,90):.3f}s")
|
||||
print(f" KV transfer: P50={np.percentile(kv_blocks,50):.0f} (should be 0 — no P involved)")
|
||||
print(f" cached_tokens P50={np.percentile(cached,50):.0f}")
|
||||
|
||||
# Sessions: how many turns each, how many used direct-to-d
|
||||
print(f"\nPer-session direct-to-D rate (top 10 by total turns):")
|
||||
per_sess = defaultdict(list)
|
||||
for r in ok:
|
||||
per_sess[r["session_id"]].append(r)
|
||||
sess_stats = []
|
||||
for sid, sreqs in per_sess.items():
|
||||
total = len(sreqs)
|
||||
dd = sum(1 for r in sreqs if r["execution_mode"] == "kvcache-direct-to-d-session")
|
||||
sc = sum(1 for r in sreqs if "session-cap" in r["execution_mode"])
|
||||
sess_stats.append((sid, total, dd, sc))
|
||||
sess_stats.sort(key=lambda x: -x[1])
|
||||
for sid, total, dd, sc in sess_stats[:10]:
|
||||
print(f" session {sid}: {total} turns, {dd} direct-to-D ({dd/total*100:.0f}%), {sc} session-cap fallback ({sc/total*100:.0f}%)")
|
||||
52
scripts/analysis/analyze_v4.py
Normal file
52
scripts/analysis/analyze_v4.py
Normal file
@@ -0,0 +1,52 @@
|
||||
#!/usr/bin/env python3
|
||||
"""V4 results analysis: errors, execution modes, latency by mode."""
|
||||
import json
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from collections import Counter
|
||||
|
||||
BASE = Path(__file__).parent
|
||||
|
||||
def load_rows(jsonl_path):
|
||||
rows = []
|
||||
with open(jsonl_path) as f:
|
||||
for line in f:
|
||||
rows.append(json.loads(line))
|
||||
return rows
|
||||
|
||||
for name, path in [
|
||||
("Exp1 1P7D cap=16", BASE / "exp1_1p7d_kvc_cap16_metrics.jsonl"),
|
||||
("Exp2 2P6D cap=16", BASE / "exp2_2p6d_kvc_cap16_metrics.jsonl"),
|
||||
]:
|
||||
rows = load_rows(path)
|
||||
print(f"\n========== {name} ==========")
|
||||
ok = [r for r in rows if r.get("error") is None]
|
||||
err = [r for r in rows if r.get("error") is not None]
|
||||
print(f"Total: {len(rows)}, OK: {len(ok)}, Errors: {len(err)}")
|
||||
|
||||
# Errors finish_reason
|
||||
if err:
|
||||
finish_reasons = Counter()
|
||||
for r in err:
|
||||
fr = str(r.get("finish_reason") or r.get("error") or "?")
|
||||
# Truncate long messages
|
||||
short = fr[:120]
|
||||
finish_reasons[short] += 1
|
||||
print(f"\nError finish_reasons (top 5):")
|
||||
for fr, cnt in finish_reasons.most_common(5):
|
||||
print(f" {cnt}x: {fr}")
|
||||
|
||||
# Execution mode latency breakdown
|
||||
modes = Counter(r["execution_mode"] for r in ok)
|
||||
print(f"\nTop execution modes by latency:")
|
||||
print(f"{'mode':<55}{'n':<8}{'%':<8}{'P50 lat':<10}{'P90 lat':<10}{'TTFT P50':<10}")
|
||||
for mode, count in modes.most_common(8):
|
||||
mode_rows = [r for r in ok if r["execution_mode"] == mode]
|
||||
lats = [r["latency_s"] for r in mode_rows]
|
||||
ttfts = [r["ttft_s"] for r in mode_rows]
|
||||
print(f" {mode:<53}{count:<8}{count/len(ok)*100:>5.1f}% {np.percentile(lats,50):>7.3f}s {np.percentile(lats,90):>7.3f}s {np.percentile(ttfts,50):>7.3f}s")
|
||||
|
||||
# Per-D load
|
||||
per_d = Counter(r.get("assigned_decode_node", "?") for r in ok)
|
||||
print(f"\nPer-D load: max/min ratio = {max(per_d.values())/max(min(per_d.values()),1):.2f}x")
|
||||
print(f" {dict(per_d.most_common())}")
|
||||
136
scripts/analysis/compare_no_error.py
Normal file
136
scripts/analysis/compare_no_error.py
Normal file
@@ -0,0 +1,136 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Compare KVC variants vs baseline, EXCLUDING errors and truncated requests."""
|
||||
import json
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
|
||||
OUT = Path("/mnt/kzlin/workflow/pd-hybrid/agentic-pd-hybrid/outputs")
|
||||
|
||||
DATASETS = [
|
||||
("baseline 8DP", OUT / "qwen3-30b-tp1-v2-fixed/exp1_8way_dp_cache_aware_metrics.jsonl"),
|
||||
("v3 1P7D", OUT / "qwen3-30b-tp1-v3-kvaware/exp1_1p7d_kvc_kvaware_metrics.jsonl"),
|
||||
("v3 2P6D", OUT / "qwen3-30b-tp1-v3-kvaware/exp2_2p6d_kvc_kvaware_metrics.jsonl"),
|
||||
("v4 1P7D", OUT / "qwen3-30b-tp1-v4-cap16/exp1_1p7d_kvc_cap16_metrics.jsonl"),
|
||||
("v4 2P6D", OUT / "qwen3-30b-tp1-v4-cap16/exp2_2p6d_kvc_cap16_metrics.jsonl"),
|
||||
]
|
||||
|
||||
def load_rows(path):
|
||||
rows = []
|
||||
with open(path) as f:
|
||||
for line in f:
|
||||
rows.append(json.loads(line))
|
||||
return rows
|
||||
|
||||
def is_truncated(row):
|
||||
a = row.get("actual_output_tokens")
|
||||
r = row.get("requested_output_tokens")
|
||||
if a is not None and r is not None and r > 1:
|
||||
return a < r * 0.5
|
||||
return False
|
||||
|
||||
def stats(values):
|
||||
if not values:
|
||||
return {"n": 0}
|
||||
a = np.array(values)
|
||||
return {
|
||||
"n": len(a),
|
||||
"mean": float(np.mean(a)),
|
||||
"p50": float(np.percentile(a, 50)),
|
||||
"p90": float(np.percentile(a, 90)),
|
||||
"p99": float(np.percentile(a, 99)),
|
||||
}
|
||||
|
||||
def fmt(s, key):
|
||||
if s["n"] == 0:
|
||||
return "N/A"
|
||||
v = s[key]
|
||||
return f"{v:.3f}s" if v < 100 else f"{v:.1f}s"
|
||||
|
||||
results = []
|
||||
for label, path in DATASETS:
|
||||
if not path.exists():
|
||||
print(f"SKIP {label}")
|
||||
continue
|
||||
rows = load_rows(path)
|
||||
total = len(rows)
|
||||
err_n = sum(1 for r in rows if r.get("error") is not None)
|
||||
trunc_n = sum(1 for r in rows if r.get("error") is None and is_truncated(r))
|
||||
|
||||
# Filter: error=None AND not truncated AND latency present
|
||||
clean = [r for r in rows
|
||||
if r.get("error") is None
|
||||
and not is_truncated(r)
|
||||
and r.get("latency_s") is not None]
|
||||
|
||||
lats = [r["latency_s"] for r in clean]
|
||||
ttfts = [r["ttft_s"] for r in clean if r.get("ttft_s") is not None]
|
||||
|
||||
results.append({
|
||||
"label": label,
|
||||
"total": total,
|
||||
"err": err_n,
|
||||
"trunc": trunc_n,
|
||||
"clean_n": len(clean),
|
||||
"lat": stats(lats),
|
||||
"ttft": stats(ttfts),
|
||||
})
|
||||
|
||||
# Print comparison table
|
||||
print(f"\n{'='*100}")
|
||||
print("LATENCY (excluding errors AND truncated)")
|
||||
print(f"{'='*100}")
|
||||
print(f"{'config':<16}{'total':>7}{'err':>6}{'trunc':>7}{'clean':>7} {'mean':>9}{'P50':>9}{'P90':>9}{'P99':>9}")
|
||||
for r in results:
|
||||
print(f"{r['label']:<16}{r['total']:>7}{r['err']:>6}{r['trunc']:>7}{r['clean_n']:>7} "
|
||||
f"{fmt(r['lat'],'mean'):>9}{fmt(r['lat'],'p50'):>9}{fmt(r['lat'],'p90'):>9}{fmt(r['lat'],'p99'):>9}")
|
||||
|
||||
print(f"\n{'='*100}")
|
||||
print("TTFT (excluding errors AND truncated)")
|
||||
print(f"{'='*100}")
|
||||
print(f"{'config':<16}{'clean':>7} {'mean':>9}{'P50':>9}{'P90':>9}{'P99':>9}")
|
||||
for r in results:
|
||||
print(f"{r['label']:<16}{r['clean_n']:>7} "
|
||||
f"{fmt(r['ttft'],'mean'):>9}{fmt(r['ttft'],'p50'):>9}{fmt(r['ttft'],'p90'):>9}{fmt(r['ttft'],'p99'):>9}")
|
||||
|
||||
# Also: per-execution-mode breakdown for v4 only (the most interesting)
|
||||
print(f"\n{'='*100}")
|
||||
print("V4 2P6D: per-execution-mode (excluding errors and truncated)")
|
||||
print(f"{'='*100}")
|
||||
v4_2p6d = next((p for l, p in DATASETS if l == "v4 2P6D"), None)
|
||||
if v4_2p6d:
|
||||
rows = load_rows(v4_2p6d)
|
||||
clean = [r for r in rows if r.get("error") is None and not is_truncated(r)]
|
||||
from collections import Counter
|
||||
modes = Counter(r["execution_mode"] for r in clean)
|
||||
print(f"{'mode':<55}{'n':>7}{'%':>7} {'mean':>9}{'P50':>9}{'P90':>9}{'P99':>9}")
|
||||
for mode, count in modes.most_common(10):
|
||||
m_rows = [r for r in clean if r["execution_mode"] == mode]
|
||||
s = stats([r["latency_s"] for r in m_rows])
|
||||
pct = count/len(clean)*100
|
||||
print(f" {mode:<53}{count:>7}{pct:>6.1f}% {fmt(s,'mean'):>9}{fmt(s,'p50'):>9}{fmt(s,'p90'):>9}{fmt(s,'p99'):>9}")
|
||||
|
||||
# Also: WHAT IF we only count direct-to-D? (Pure KVC performance)
|
||||
print(f"\n{'='*100}")
|
||||
print("Pure KVC (kvcache-direct-to-d-session ONLY) vs Baseline")
|
||||
print(f"{'='*100}")
|
||||
for label, path in DATASETS:
|
||||
if not path.exists() or "1P7D" not in label and "2P6D" not in label:
|
||||
continue
|
||||
rows = load_rows(path)
|
||||
direct = [r for r in rows
|
||||
if r.get("error") is None and not is_truncated(r)
|
||||
and r.get("execution_mode") == "kvcache-direct-to-d-session"]
|
||||
if not direct:
|
||||
continue
|
||||
s_lat = stats([r["latency_s"] for r in direct])
|
||||
s_ttft = stats([r["ttft_s"] for r in direct if r.get("ttft_s") is not None])
|
||||
print(f"{label:<16}n={s_lat['n']:>5} lat: P50={fmt(s_lat,'p50')} P90={fmt(s_lat,'p90')} ttft: P50={fmt(s_ttft,'p50')} P90={fmt(s_ttft,'p90')}")
|
||||
|
||||
# Baseline for reference (already non-fallback by definition)
|
||||
print()
|
||||
baseline_path = OUT / "qwen3-30b-tp1-v2-fixed/exp1_8way_dp_cache_aware_metrics.jsonl"
|
||||
baseline_rows = load_rows(baseline_path)
|
||||
clean = [r for r in baseline_rows if r.get("error") is None and not is_truncated(r)]
|
||||
s_lat = stats([r["latency_s"] for r in clean])
|
||||
s_ttft = stats([r["ttft_s"] for r in clean if r.get("ttft_s") is not None])
|
||||
print(f"{'baseline 8DP':<16}n={s_lat['n']:>5} lat: P50={fmt(s_lat,'p50')} P90={fmt(s_lat,'p90')} ttft: P50={fmt(s_ttft,'p50')} P90={fmt(s_ttft,'p90')}")
|
||||
Reference in New Issue
Block a user