From 1e2f48d1a4930a5563389426ed206c2561825296 Mon Sep 17 00:00:00 2001 From: ruihan Date: Thu, 17 Sep 2026 22:09:58 +0800 Subject: [PATCH 1/7] Enable validated prefix caching and record DGX Spark optimization benchmarks --- .dockerignore | 3 + Dockerfile | 4 + README.md | 22 +++- THIRD_PARTY_NOTICES.md | 3 + compose.yaml | 4 +- configs/no-prefix.yaml | 17 +++ docs/architecture.md | 3 +- docs/benchmark-method.md | 45 ++++++++ docs/optimization-results.md | 64 ++++++++++++ docs/packaging-validation.md | 12 +++ docs/provenance.md | 9 +- docs/results/eager-long-1024.jsonl | 8 ++ docs/results/eager.jsonl | 21 ++++ docs/results/graph2.jsonl | 23 ++++ docs/results/prefix-eager-check.jsonl | 6 ++ docs/results/prefix-eager.jsonl | 21 ++++ experiments/graph/Dockerfile | 2 + experiments/graph/README.md | 23 ++++ experiments/graph/compose.yaml | 21 ++++ experiments/graph/spark_ngram_adapter.py | 94 +++++++++++++++++ experiments/graph/test_disk_adapter.py | 79 ++++++++++++++ patches/fix_mamba_prefix_blocks.py | 28 +++++ scripts/benchmark.py | 128 +++++++++++++++++++++++ scripts/benchmark.sh | 3 + scripts/prefix-check.py | 39 +++++++ scripts/start.sh | 4 +- scripts/summarize-benchmark.py | 26 +++++ scripts/test-prefix.sh | 3 + 28 files changed, 707 insertions(+), 8 deletions(-) create mode 100644 configs/no-prefix.yaml create mode 100644 docs/benchmark-method.md create mode 100644 docs/optimization-results.md create mode 100644 docs/results/eager-long-1024.jsonl create mode 100644 docs/results/eager.jsonl create mode 100644 docs/results/graph2.jsonl create mode 100644 docs/results/prefix-eager-check.jsonl create mode 100644 docs/results/prefix-eager.jsonl create mode 100644 experiments/graph/Dockerfile create mode 100644 experiments/graph/README.md create mode 100644 experiments/graph/compose.yaml create mode 100644 experiments/graph/spark_ngram_adapter.py create mode 100644 experiments/graph/test_disk_adapter.py create mode 100644 patches/fix_mamba_prefix_blocks.py create mode 100644 scripts/benchmark.py create mode 100755 scripts/benchmark.sh create mode 100644 scripts/prefix-check.py create mode 100644 scripts/summarize-benchmark.py create mode 100755 scripts/test-prefix.sh diff --git a/.dockerignore b/.dockerignore index bf9e0ee..35e253e 100644 --- a/.dockerignore +++ b/.dockerignore @@ -4,3 +4,6 @@ !patches/spark_ngram_adapter.py !vendor/ !vendor/vllm_ple_mmap.py +!patches/fix_mamba_prefix_blocks.py +!vendor/LICENSE +!vendor/Apache-2.0.txt diff --git a/Dockerfile b/Dockerfile index 2afb7eb..fc222c5 100644 --- a/Dockerfile +++ b/Dockerfile @@ -20,3 +20,7 @@ for file, before, after in [ assert before in s, file p.write_text(s.replace(before, after)) PY + +COPY patches/fix_mamba_prefix_blocks.py /tmp/fix_mamba_prefix_blocks.py +RUN python3 /tmp/fix_mamba_prefix_blocks.py /usr/local/lib/python3.12/dist-packages +COPY vendor/LICENSE vendor/Apache-2.0.txt /usr/local/share/licenses/spark-ple/ diff --git a/README.md b/README.md index f34bc2b..92f79e8 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,8 @@ 在单台 NVIDIA DGX Spark（GB10 / ARM64 / 128 GB 统一内存）上部署 `nvidia/Qwen3.8-Flash-Next-NVFP4` 的可复现配置。 来自 2026-09-17 的实际部署：262144 上下文上限、MTP 2 tokens、OpenAI 兼容 API。 +优化版启用经验证的前缀缓存，保持 eager 执行；32K 重复前缀首个 token 约从 14.05 秒降至 1.34 秒。 +这里的首个 token 包括思考内容，完整回答不会因此保证快 10 倍。详见 [优化报告](docs/optimization-results.md)。 PLE 查找表通过磁盘映射按需读取；原始模型权重未重新量化。 > 这是针对固定 nightly 的社区适配，非 NVIDIA/vLLM 官方支持方案。 @@ -44,7 +46,7 @@ API 地址：`http://:8000/v1`；模型名：`qwen3.8-flash-next`。 | GPU 内存比例 | 0.80 | | KV 精度 | auto（本配置为 BF16） | | 执行 | eager，未启用 CUDA Graph | -| Prefix caching | 关闭 | +| Prefix caching | 开启，含 Mamba 块对齐修正 | | API | 8000，启用密钥、reasoning parser、工具调用 | | 自动重启 | unless-stopped | @@ -62,7 +64,9 @@ docker compose stop vllm ./scripts/start.sh baseline ./scripts/wait.sh ./scripts/test.sh -# 恢复默认 262K + MTP +# 保持 262K + MTP，只关闭前缀缓存 +./scripts/start.sh no-prefix +# 恢复默认 262K + MTP + 前缀缓存 ./scripts/start.sh ``` @@ -75,10 +79,12 @@ docker compose stop vllm ## 文件说明 -- `Dockerfile`：固定基础镜像，安装 PLE 适配和两项 GB10 FLA 修改。 +- `Dockerfile`：固定基础镜像，安装 PLE 适配、两项 GB10 FLA 修改及 Mamba 缓存块对齐修正。 - `patches/`：针对当前 nightly 的适配层。 - `vendor/`：保持原样的社区 mmap 代码及许可证。 - `configs/baseline-32k.yaml`：基础配置的 Compose override。 +- `docs/optimization-results.md`：基准比较、缓存正确性与未采用的图执行实验。 +- `experiments/graph/`：未采用为默认的图执行实现与复现说明。 - `docs/validation.md`：实测结果和未验证边界。 - `docs/troubleshooting.md`：故障依据、上游修复与排查步骤。 - `docs/provenance.md`：模型/镜像/第三方源码的固定版本和校验值。 @@ -86,3 +92,13 @@ docker compose stop vllm `.env`、密钥、权重、日志、运行缓存均不提交。仓库不包含模型权重；模型使用条件以 [NVIDIA 模型页](https://huggingface.co/nvidia/Qwen3.8-Flash-Next-NVFP4) 为准。 第三方代码按其原许可证使用，见 [第三方声明](THIRD_PARTY_NOTICES.md)。 + +## 复测优化 + +```bash +./scripts/benchmark.sh current > benchmark.jsonl +python3 scripts/summarize-benchmark.py benchmark.jsonl +./scripts/test-prefix.sh +``` + +测试只使用合成输入。方法、首字时间口径与小样本限制见 docs/benchmark-method.md。 diff --git a/THIRD_PARTY_NOTICES.md b/THIRD_PARTY_NOTICES.md index 2952600..11fa395 100644 --- a/THIRD_PARTY_NOTICES.md +++ b/THIRD_PARTY_NOTICES.md @@ -8,3 +8,6 @@ See docs/provenance.md for the exact vendored file hash. vLLM and its dependencies remain in the upstream container with their respective licenses. Model weights are downloaded separately and governed by their upstream terms. No blanket license is assigned here to user-authored deployment files. + +`patches/fix_mamba_prefix_blocks.py` is derived from blazux's Apache-2.0 Mamba +block-alignment patch and preserves that attribution. diff --git a/compose.yaml b/compose.yaml index e27e8d9..71579cb 100644 --- a/compose.yaml +++ b/compose.yaml @@ -1,7 +1,7 @@ name: qwen38-flash-dgx-spark services: vllm: - image: local/qwen38-flash-spark:nightly-0bfc7a15 + image: local/qwen38-flash-spark:prefix-eager-0bfc7a15 build: context: . args: @@ -45,7 +45,7 @@ services: --dtype bfloat16 --kv-cache-dtype auto --gpu-memory-utilization ${GPU_MEMORY_UTILIZATION:-0.80} --max-model-len 262144 --max-num-seqs ${MAX_NUM_SEQS:-4} --max-num-batched-tokens 2048 - --enable-chunked-prefill --no-enable-prefix-caching + --enable-chunked-prefill --enable-prefix-caching --speculative-config '{"method":"mtp","num_speculative_tokens":2}' --enforce-eager --no-enable-flashinfer-autotune --load-format safetensors diff --git a/configs/no-prefix.yaml b/configs/no-prefix.yaml new file mode 100644 index 0000000..9629898 --- /dev/null +++ b/configs/no-prefix.yaml @@ -0,0 +1,17 @@ +services: + vllm: + command: + - >- + exec vllm serve /root/.cache/huggingface/hub/models--nvidia--Qwen3.8-Flash-Next-NVFP4/snapshots/fc694b54fb0174e0913e6adf86691ef85a4ead47 + --served-model-name qwen3.8-flash-next + --host 0.0.0.0 --port 8000 + --tensor-parallel-size 1 + --dtype bfloat16 --kv-cache-dtype auto + --gpu-memory-utilization ${GPU_MEMORY_UTILIZATION:-0.80} + --max-model-len 262144 --max-num-seqs ${MAX_NUM_SEQS:-4} --max-num-batched-tokens 2048 + --enable-chunked-prefill --no-enable-prefix-caching + --speculative-config '{"method":"mtp","num_speculative_tokens":2}' + --enforce-eager --no-enable-flashinfer-autotune + --load-format safetensors + --reasoning-parser qwen3 --tool-call-parser qwen3_xml --enable-auto-tool-choice + --api-key "$$(cat /run/secrets/qwen_api_key)" diff --git a/docs/architecture.md b/docs/architecture.md index b61ef17..530e76f 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -18,7 +18,8 @@ Linux 页缓存保留访问过的数据，未命中才访问 SSD。此方式不 而不是追求 100% 内存占用。社区默认同样为 0.80，但社区性能数字不是此配置的测试结果。 完整 PLE 运算包含 GPU 同步，日志中的 op 总时长不能全部归因于磁盘；分析时应区分 gather 与 gpu-wait。 -当前使用 eager 执行，未开启 prefix caching，也没有采用社区可选的二次 FP8 量化或词表裁剪。 +当前使用 eager 执行，优化版开启经过 Mamba 块对齐修正的 prefix caching；没有采用二次 FP8 量化或词表裁剪。 +原基线的内存数字见上文；优化版本次启动 KV 约 15.11 GiB，可用主机内存约 18 GiB，随页缓存状态变化。 来源：[社区方案](https://github.com/blazux/qwen3.8-Flash-DGX)、 [Linux 内存指标](https://docs.kernel.org/filesystems/proc.html)。 diff --git a/docs/benchmark-method.md b/docs/benchmark-method.md new file mode 100644 index 0000000..b23de27 --- /dev/null +++ b/docs/benchmark-method.md @@ -0,0 +1,45 @@ +# 优化实验方法 + +固定模型 revision、原始权重、MTP=2、BF16 KV、262144 上下文上限和 GPU memory=0.80。 +按 eager 基线 → piecewise CUDA Graph 实验 → eager+前缀缓存逐项比较，不把多项改动的收益混在一起。 +候选未通过启动、基准和 smoke test 前不作为默认配置。 + +`scripts/benchmark.py --label ` 在服务容器内运行，使用挂载的 API Key； +输出只包含固定合成问题的回答和计时，不打印密钥或用户数据。 + +- 预热算术题；3 次固定中文说明；简单代码题。 +- 2 和 4 并发的中文说明请求。 +- 约 8K / 32K token 的固定伪随机档案，检索中间插入的口令，各重复一次。 +- 记录真实 prompt/completion token 数、流式 TTFT、正文首字时间、完成耗时、结束原因。 +- 读取 prefix_cache_queries_total / hits_total 的增量，避免仅凭响应变快判断缓存成功。 + +TTFT 指包含 reasoning 在内的第一个非空 token；正文首字时间另记。 +近似 decode tokens/s = (completion_tokens-1)/(最后一块输出时间-第一块输出时间)， +受 SSE 合并和 speculative 多 token 提交影响，不等于 GPU 内核吞吐。 +并发总吞吐采用所有 completion tokens / 该组请求的墙钟时间（包含 prefill）。 + +`cold` 标签只代表该轮首次提交，未清空 OS 页缓存，也不保证跨任务没有共享前缀。 +因此长输入第一次与重复请求的差异，应结合 prefix hit 计数解释。 +温度 0 + seed 固定仍可能有非确定性；完整输出 hash 包含思考过程，hash 不同不自动意味着答案错误。 +口令题必须包含期望口令且正常 stop；通用中文题只检查非空，质量需人工审查。 +代码题的简单检查仅是 smoke check，不代替完整代码能力测评。 + +每个候选均保留独立结果。小样本只支持配置选择，不承诺长期性能、全长 262K 或全部任务质量。 + +当前固定 nightly 在 Qwen4Exp 上选择 runtime breakable CUDA Graph。 +单纯设置 splitting_ops 不会拦截自定义 PLE 查表，首次实验在捕获阶段失败。 +适配层随后在注册查表 custom op 时使用原生 eager_break_during_capture 装饰器， +并显式设置 VLLM_USE_BREAKABLE_CUDAGRAPH=1。调用者提供固定输出缓冲区，查表原地写入。 +小型 GPU 测试验证捕获后 3 组不同输入的重放结果逐字节一致。 +这条优化路径应称为 CUDA Graph 加速，不能直接声称启用了 torch.compile/Inductor 融合。 + +第二轮图捕获发现索引缓冲区在 capture-time 尚未由前置 GPU kernel 填充， +即使 CPU 查表本身是 eager break，也会读到未初始化的索引。 +最终候选将原生哈希计算与 mmap 查表合并为 `vllm::spark_ple_lookup`， +整个操作由 eager-break 包裹，输出保持原地写入。GPU 测试增加了哈希不在捕获中执行的断言， +并检查后续图内浮点计算在更换输入后仍正确。 + +图执行候选最终成功启动，但在 32K 重复请求上耗尽 256 个输出 token，未完成完整验收。 +这不是已证实的图执行正确性缺陷。由于单流收益仅约 4.3%，没有继续重载该候选； +转为 eager + 前缀缓存的独立实验。长输入预算提高为 1024，并先对 eager 基线重新测试。 +图执行实现和独立构建说明保存在 experiments/graph，默认适配层保留原已验证实现。 diff --git a/docs/optimization-results.md b/docs/optimization-results.md new file mode 100644 index 0000000..21c0012 --- /dev/null +++ b/docs/optimization-results.md @@ -0,0 +1,64 @@ +# 优化结果：采用 eager + 前缀缓存 + +2026-09-17，单台 DGX Spark / GB10，固定模型与镜像版本不变。 +最终启用前缀缓存及两处 Mamba 块对齐修正；保持原始权重、BF16 KV、MTP=2、 +262144 上下文、GPU 内存比例 0.80、并发 4、batch token 上限 2048、eager 执行。 + +## 实测比较 + +短请求取 3 次的中位数；长请求每种条件仅一个首次请求和一个重复请求，不是统计显著性结论。 +长输入比较使用相同 1024 输出预算；首次 `cold` 未清空 OS 页缓存。 + +| 指标 | 原 eager / 无前缀缓存 | eager / 有前缀缓存 | +|---|---:|---:| +| 短回答生成速度（近似 tokens/s） | 30.502 | 30.261 | +| 短请求首 token（秒） | 0.2740 | 0.2798 | +| 2 路并发总吞吐（tokens/s） | 41.120 | 40.862 | +| 4 路并发总吞吐（tokens/s） | 68.149 | 71.728 | +| 8K 首次请求首 token（秒） | 4.1596 | 3.9772 | +| 8K 重复请求首 token（秒） | 3.4781 | 0.8719 | +| 32K 首次请求首 token（秒） | 14.1514 | 15.3437 | +| 32K 重复请求首 token（秒） | 14.0532 | 1.3408 | + +重复输入的首 token 延迟分别降低约 75% 和 90%。收益针对可复用前缀； +32K 首次输入本次反而慢约 8%，短回答生成速度基本不变，不适合宣称所有请求都更快。 + +**首 token 包括 reasoning，不等于最终正文出现。** +8K 重复请求本次完整耗时由 8.1172 秒变成 18.8017 秒，生成的思考长度不同； +32K 重复请求完整耗时由 17.2601 秒降至 10.4757 秒。 +因此不能把首 token 的约 10 倍改善宣传成完整回答快 10 倍。温度 0 也不保证思考过程逐字一致。 + +## 缓存正确性与运行状态 + +- 8K / 32K 检索均正确，正常 stop；实际 prefix hit 增量分别为 6400 / 30400 tokens。 +- 同一份约 11K token 的档案，连续查询前、中、后三个位置，再重复第一问，4 次答案精确匹配。 + 这组专项测试实际命中增量为 24000 tokens。 +- 算术、中文、简单代码检查、2/4 并发、自动工具调用通过。 +- 本轮 KV 缓存 15.11 GiB，日志报告 534426 token 总容量；该数字随启动环境变化。 +- 主机测试后 used 约 103 GiB，available 约 18 GiB；约 258 MiB swap 存量不是持续换页速率。 +- 最终服务健康，restart=unless-stopped。原 MinerU 和旧 Qwen3.6 继续停止。 +- 未压测完整 262K 输入、长期高并发及多模态；这些小样本不代表全面质量测评。 + +## 未采用的 CUDA Graph 实验 + +新 nightly 使用 runtime breakable CUDA Graph。 +最初仅配置 splitting_ops 时，CPU 查表仍在捕获范围内；随后只包裹查表， +又遇到 capture-time 前置哈希 kernel 未执行、索引未初始化的问题。 +将哈希与查表合并为 eager-break custom op 后，小型 GPU 变更输入重放测试通过，完整模型也启动成功。 + +该候选单流中位数 31.799 tokens/s（约 +4.3%），但 32K 重复请求用尽了最初的 256 输出预算， +全部为 reasoning，没有最终正文，因而未通过完整验收。后台按设计恢复了原 eager 服务并完成 smoke test。 +这不能证明图执行算错；候选没有以更高输出预算重新完成验收，且性能收益有限，因此不作为默认。 +代码与说明单独保存在 `experiments/graph/`，不影响最终部署。 + +## 复现与回退 + +- 方法：`benchmark-method.md`。 +- 原始数据：`results/eager.jsonl`、`results/eager-long-1024.jsonl`、 + `results/graph2.jsonl`、`results/prefix-eager.jsonl`、`results/prefix-eager-check.jsonl`。 +- `scripts/start.sh no-prefix`：保留 262K + MTP，关闭前缀缓存。 +- `scripts/start.sh baseline`：32K，不启用 MTP 和前缀缓存。 +- `scripts/start.sh`：默认前缀缓存优化配置。 + +修正依据： +https://github.com/blazux/qwen3.8-Flash-DGX/blob/main/src/patch_mamba_block_size.py diff --git a/docs/packaging-validation.md b/docs/packaging-validation.md index 6ff03bf..4aea43e 100644 --- a/docs/packaging-validation.md +++ b/docs/packaging-validation.md @@ -11,3 +11,15 @@ 线上服务没有被重建。此轮验证覆盖项目构建、配置等价性与测试脚本； 没有从空缓存重新下载 124 GiB 权重，也没有再次完整加载模型。 + +## 前缀缓存优化的最终打包验证（同日） + +- 默认、no-prefix、baseline 三套 Compose 配置均在 Spark 上解析成功。 +- 默认配置的镜像和展开后的启动参数与正在运行的优化容器一致（归一化 Compose 的美元符号转义后比较）。 +- 从最终项目目录构建 `local/qwen38-optimized-repo-check:0bfc7a15` 成功；使用同一固定版本的本地基础镜像。 +- 新构建镜像中 `mamba_hybrid.py`、`scheduler.py` 的 SHA256 分别与线上优化容器一致。 +- 最终线上容器为 running / healthy；采用 `unless-stopped` 重启策略。 +- Python 语法、Shell 语法、vendor 文件摘要及 Git 空白检查通过。 + +本节验证项目可以构建且配置与线上对应；临时构建镜像未另行加载全模型。 +线上优化配置的推理、缓存正确性及性能验收见 `optimization-results.md`。 diff --git a/docs/provenance.md b/docs/provenance.md index 3971a00..3d0cedc 100644 --- a/docs/provenance.md +++ b/docs/provenance.md @@ -4,7 +4,8 @@ - 模型 revision：`fc694b54fb0174e0913e6adf86691ef85a4ead47` - 基础镜像：`vllm/vllm-openai@sha256:c4392d76e3eec8983fa152651365158cb062e348fd40398963f499d5867b9e28` - 基础镜像源码 commit：`0bfc7a15d095fe83ecc82b50561a93c177fece2d` -- 本地构建 tag：`local/qwen38-flash-spark:nightly-0bfc7a15` +- 优化版本地构建 tag：`local/qwen38-flash-spark:prefix-eager-0bfc7a15` +- 原始基线 tag：`local/qwen38-flash-spark:nightly-0bfc7a15` nightly 的包版本曾报告 `0.3.1.dev3+g0bfc7a15d`，因此使用镜像 digest/源码 commit 标识， 不把它当作正式版 0.29.0。BASE_IMAGE override 仅用于已核对 digest 的本地镜像别名。 @@ -20,3 +21,9 @@ https://raw.githubusercontent.com/blazux/qwen3.8-Flash-DGX/main/src/vllm_ple_mma 不要重新下载 main 后仍宣称是同一版本。许可证按获取时的文件保存在 vendor/LICENSE。 适配层和 Dockerfile 是本次部署的本地改动，第三方 helper 未修改。 + +Mamba prefix fix 来源： +https://github.com/blazux/qwen3.8-Flash-DGX/blob/main/src/patch_mamba_block_size.py + +本项目保存经源码断言核对的适配版本 patches/fix_mamba_prefix_blocks.py（Apache-2.0）。 +修正 V2 worker 状态种子索引和 scheduler prefill 块对齐，不改变模型权重。 diff --git a/docs/results/eager-long-1024.jsonl b/docs/results/eager-long-1024.jsonl new file mode 100644 index 0000000..c1930b3 --- /dev/null +++ b/docs/results/eager-long-1024.jsonl @@ -0,0 +1,8 @@ +{"label": "eager-long-1024", "event": "start", "phase": "long"} +{"label": "eager-long-1024", "test": "long_8192_cold", "max_tokens": 1024, "elapsed_s": 14.3523, "ttft_s": 4.1596, "first_content_s": 14.0876, "decode_tps_approx": 27.718, "usage": {"prompt_tokens": 8132, "total_tokens": 8413, "completion_tokens": 281, "completion_tokens_details": {"reasoning_tokens": 271}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "ee86119ab3487b56b8b60ff790f5304356169a99a99fbc60be80ae3294ee5a7d", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "eager-long-1024", "test": "long_8192_repeat", "max_tokens": 1024, "elapsed_s": 8.1172, "ttft_s": 3.4781, "first_content_s": 7.8808, "decode_tps_approx": 32.021, "usage": {"prompt_tokens": 8132, "total_tokens": 8279, "completion_tokens": 147, "completion_tokens_details": {"reasoning_tokens": 137}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "64d72f0238ac936c6af1b6ab361f8208917ba56bcb6835ab92d88e363a5aa1f2", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "eager-long-1024", "test": "prefix_8192", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 0.0, "vllm:prefix_cache_hits_total": 0.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "eager-long-1024", "test": "long_32768_cold", "max_tokens": 1024, "elapsed_s": 24.1665, "ttft_s": 14.1514, "first_content_s": 23.9288, "decode_tps_approx": 28.986, "usage": {"prompt_tokens": 32707, "total_tokens": 32996, "completion_tokens": 289, "completion_tokens_details": {"reasoning_tokens": 279}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "efaf532aa3612cd1e5bd5340ae1754adec8e02df6a9c5981216a9e31f649a9f4", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": 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b/docs/results/eager.jsonl @@ -0,0 +1,21 @@ +{"label": "eager", "event": "start", "phase": "all"} +{"label": "eager", "test": "warmup", "elapsed_s": 1.2027, "ttft_s": 0.3158, "first_content_s": 1.1221, "decode_tps_approx": 38.445, "usage": {"prompt_tokens": 51, "total_tokens": 83, "completion_tokens": 32, "completion_tokens_details": {"reasoning_tokens": 26}}, "finish_reason": "stop", "content": "\n\n323", "output_sha256": "4477e82dcd9f3f8da1dd56be073d72e4ba20121d6ce6fbc97ee54258fb4b2959", "prompt_sha256": "4181e1a44a0bed10d00c74e822377bd75a6c6c9a1ea458f5170b8193e9fce959", "correct": true} +{"label": "eager", "test": "short_0", "elapsed_s": 7.6199, "ttft_s": 0.2937, "first_content_s": 3.9208, "decode_tps_approx": 29.074, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager", "test": "short_1", "elapsed_s": 7.253, "ttft_s": 0.2698, "first_content_s": 3.732, "decode_tps_approx": 30.502, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager", "test": "short_2", "elapsed_s": 7.2554, "ttft_s": 0.274, "first_content_s": 3.7466, "decode_tps_approx": 30.51, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager", "test": "code", "elapsed_s": 1.63, "ttft_s": 0.3132, "first_content_s": 1.132, "decode_tps_approx": 33.418, "usage": {"prompt_tokens": 58, "total_tokens": 103, "completion_tokens": 45, "completion_tokens_details": {"reasoning_tokens": 22}}, "finish_reason": "stop", "content": "\n\n```python\ndef is_even(n):\n return n % 2 == 0\n```", "output_sha256": "8fa11c8b215becb0a63dad605e745fd444d156b4aba9b296df1d68113888f181", "prompt_sha256": "8c926bfd793b2818798d07c9882bc11a0e83035eb6462025c1c444020bdb43fa", "correct": true} +{"label": "eager", "test": "concurrent_2_1", "elapsed_s": 7.0565, "ttft_s": 0.5992, "first_content_s": 2.4727, "decode_tps_approx": 24.778, "usage": {"prompt_tokens": 69, "total_tokens": 230, "completion_tokens": 161, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非没有代价：每次执行INSERT、UPDATE、DELETE操作时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "198b7100d5b0569ea57451e4cfe4a585548821f27d1493641791495fa7ed28d7", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "eager", "test": "concurrent_2_0", "elapsed_s": 9.5521, "ttft_s": 0.7446, "first_content_s": 5.7589, "decode_tps_approx": 26.228, "usage": {"prompt_tokens": 69, "total_tokens": 301, "completion_tokens": 232, "completion_tokens_details": {"reasoning_tokens": 135}}, "finish_reason": "stop", "content": "\n\n数据库索引通过维护一个有序的数据结构（如B+树），将查询从全表扫描转化为对数级的树路径查找，大幅减少磁盘I/O次数，从而加速读取。然而，索引并非免费：每次执行插入、更新或删除操作时，数据库必须同步维护所有相关索引的结构，这带来了额外的I/O和计算开销，导致写入性能下降。因此，索引本质上是以空间换时间、以写入换读取的权衡策略。", "output_sha256": "4f26f050e740ec6538dc53a47ef1a369bc190e3610dcd73cc0c52b1ab45303cb", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "eager", "test": "concurrency_summary", "concurrency": 2, "elapsed_s": 9.557, "aggregate_completion_tps": 41.12} +{"label": "eager", "test": "concurrent_4_3", "elapsed_s": 7.1729, "ttft_s": 0.3383, "first_content_s": 1.8092, "decode_tps_approx": 20.484, "usage": {"prompt_tokens": 69, "total_tokens": 210, "completion_tokens": 141, "completion_tokens_details": {"reasoning_tokens": 40}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非免费：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此索引是\"读快写慢\"的权衡。", "output_sha256": "bf89102b3e9893e714a0aedbfe554c53ba5733e085841f67b4d122666ea68aaa", "prompt_sha256": "5f4e91457fc536ab484580bd5416ae3d1f5ca7f7d0909d351d0def51ff702269", "correct": true} +{"label": "eager", "test": "concurrent_4_0", "elapsed_s": 7.418, "ttft_s": 0.3377, "first_content_s": 2.274, "decode_tps_approx": 19.491, "usage": {"prompt_tokens": 69, "total_tokens": 208, "completion_tokens": 139, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过二分查找快速定位目标行，避免全表扫描，从而大幅减少磁盘I/O。然而，索引对写入操作有负面影响：每次INSERT、UPDATE或DELETE时，数据库必须同步维护索引结构，增加了额外开销，导致写入速度变慢，同时也会占用更多存储空间。", "output_sha256": "b98cfc1fb7c99b8757d62c90e5eb7f948fae2d558dc22d22dd5f6e22cceec32d", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "eager", "test": "concurrent_4_2", "elapsed_s": 10.6829, "ttft_s": 0.3377, "first_content_s": 6.0406, "decode_tps_approx": 23.973, "usage": {"prompt_tokens": 69, "total_tokens": 318, "completion_tokens": 249, "completion_tokens_details": {"reasoning_tokens": 132}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将表中某一列的值按特定结构（如B+树）有序存储。查询时，数据库无需逐行扫描全表，而是通过索引树快速定位目标数据，将时间复杂度从O(n)降至O(log n)，从而显著加快读取速度。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这增加了额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "8cbe893528aa7c604c7f8b9da698602c88170ebfe15366a4c071369ecfc42437", "prompt_sha256": "9e40c25d7503874e4d38a72e362b442b2cd827f0dea4551fd9c5c0069a693df6", "correct": true} +{"label": "eager", "test": "concurrent_4_1", "elapsed_s": 11.4868, "ttft_s": 0.3372, "first_content_s": 7.1717, "decode_tps_approx": 22.692, "usage": {"prompt_tokens": 69, "total_tokens": 323, "completion_tokens": 254, "completion_tokens_details": {"reasoning_tokens": 143}}, "finish_reason": "stop", "content": "\n\n数据库索引通过为指定列建立有序的数据结构（如B+树），使查询时能像翻目录一样快速定位目标行，避免逐行扫描全表，从而大幅缩短查询时间。然而，索引对写入操作有负面影响：每次执行INSERT、UPDATE或DELETE时，数据库除了修改数据本身，还需要同步更新所有相关索引的结构，这带来了额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "e2c43aa5e3ec842cd7e0db6b6e4e59fc738118ba17677479c769a04b81f3646d", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "eager", "test": "concurrency_summary", "concurrency": 4, "elapsed_s": 11.489, "aggregate_completion_tps": 68.149} +{"label": "eager", "test": "long_8192_cold", "elapsed_s": 7.2917, "ttft_s": 3.7366, "first_content_s": 7.0381, "decode_tps_approx": 31.964, "usage": {"prompt_tokens": 8132, "total_tokens": 8244, "completion_tokens": 112, "completion_tokens_details": {"reasoning_tokens": 102}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "5888fc1d5d0fe9529e3fa3bb7765a55e03528c30c21e6fa31053a4ab48728f76", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "eager", "test": "long_8192_repeat", "elapsed_s": 6.9629, "ttft_s": 3.4914, "first_content_s": 6.8031, "decode_tps_approx": 31.977, "usage": {"prompt_tokens": 8132, "total_tokens": 8244, "completion_tokens": 112, "completion_tokens_details": {"reasoning_tokens": 102}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "85013fdff304bd42ba744b4a88c3d644ba7cac55e4e673a7e4b3ceafef360524", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "eager", "test": "prefix_8192", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 0.0, "vllm:prefix_cache_hits_total": 0.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "eager", "test": "long_32768_cold", "elapsed_s": 19.6169, "ttft_s": 14.1505, "first_content_s": 19.3777, "decode_tps_approx": 32.813, "usage": {"prompt_tokens": 32707, "total_tokens": 32885, "completion_tokens": 178, "completion_tokens_details": {"reasoning_tokens": 168}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "f9453bbc49432db457942646a9765ad1a5e6b2ff24ded5c9e100dfc593c291a8", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "eager", "test": "long_32768_repeat", "elapsed_s": 17.5388, "ttft_s": 14.0784, "first_content_s": 17.3033, "decode_tps_approx": 32.241, "usage": {"prompt_tokens": 32707, "total_tokens": 32817, "completion_tokens": 110, "completion_tokens_details": {"reasoning_tokens": 100}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "fa738e62694e3ab4fcd318773ee79c060d1995a5ed7734a2ddcb06902e28a5aa", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "eager", "test": "prefix_32768", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 0.0, "vllm:prefix_cache_hits_total": 0.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "eager", "event": "BENCHMARK_PASS"} diff --git a/docs/results/graph2.jsonl b/docs/results/graph2.jsonl new file mode 100644 index 0000000..04bddc4 --- /dev/null +++ b/docs/results/graph2.jsonl @@ -0,0 +1,23 @@ +{"label": "graph2", "event": "start", "phase": "all"} +{"label": "graph2", "test": "warmup", "elapsed_s": 1.5384, "ttft_s": 0.6867, "first_content_s": 1.458, "decode_tps_approx": 40.194, "usage": {"prompt_tokens": 51, "total_tokens": 83, "completion_tokens": 32, "completion_tokens_details": {"reasoning_tokens": 26}}, "finish_reason": "stop", "content": "\n\n323", "output_sha256": "4477e82dcd9f3f8da1dd56be073d72e4ba20121d6ce6fbc97ee54258fb4b2959", "prompt_sha256": "4181e1a44a0bed10d00c74e822377bd75a6c6c9a1ea458f5170b8193e9fce959", "correct": true} +{"label": "graph2", "test": "short_0", "elapsed_s": 7.3436, "ttft_s": 0.34, "first_content_s": 3.9092, "decode_tps_approx": 30.413, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph2", "test": "short_1", "elapsed_s": 6.9719, "ttft_s": 0.2735, "first_content_s": 3.6829, "decode_tps_approx": 31.799, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph2", "test": "short_2", "elapsed_s": 6.9661, "ttft_s": 0.2773, "first_content_s": 3.6742, "decode_tps_approx": 31.845, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph2", "test": "code", "elapsed_s": 1.58, "ttft_s": 0.2932, "first_content_s": 1.0897, "decode_tps_approx": 34.199, "usage": {"prompt_tokens": 58, "total_tokens": 103, "completion_tokens": 45, "completion_tokens_details": {"reasoning_tokens": 22}}, "finish_reason": "stop", "content": "\n\n```python\ndef is_even(n):\n return n % 2 == 0\n```", "output_sha256": "8fa11c8b215becb0a63dad605e745fd444d156b4aba9b296df1d68113888f181", "prompt_sha256": "8c926bfd793b2818798d07c9882bc11a0e83035eb6462025c1c444020bdb43fa", "correct": true} +{"label": "graph2", "test": "concurrent_2_0", "elapsed_s": 6.268, "ttft_s": 0.3133, "first_content_s": 2.0085, "decode_tps_approx": 26.031, "usage": {"prompt_tokens": 69, "total_tokens": 225, "completion_tokens": 156, "completion_tokens_details": {"reasoning_tokens": 49}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这增加了额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "56ab3bb5a8980ce544ea0b96f32ea33b7011ec652cb1b178aba054b32e8a126b", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "graph2", "test": "concurrent_2_1", "elapsed_s": 6.7633, "ttft_s": 0.3137, "first_content_s": 2.0965, "decode_tps_approx": 24.964, "usage": {"prompt_tokens": 69, "total_tokens": 231, "completion_tokens": 162, "completion_tokens_details": {"reasoning_tokens": 51}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非没有代价：每次执行INSERT、UPDATE或DELETE时，数据库必须同步维护索引结构，这会增加额外的磁盘I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "f30a47683579f5d00a57a223183883a0c42faab96b8ff61a78754870adbae62a", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "graph2", "test": "concurrency_summary", "concurrency": 2, "elapsed_s": 6.768, "aggregate_completion_tps": 46.987} +{"label": "graph2", "test": "concurrent_4_3", "elapsed_s": 6.9165, "ttft_s": 0.3273, "first_content_s": 1.752, "decode_tps_approx": 21.247, "usage": {"prompt_tokens": 69, "total_tokens": 210, "completion_tokens": 141, "completion_tokens_details": {"reasoning_tokens": 40}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非免费：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此索引是\"读快写慢\"的权衡。", "output_sha256": "bf89102b3e9893e714a0aedbfe554c53ba5733e085841f67b4d122666ea68aaa", "prompt_sha256": "5f4e91457fc536ab484580bd5416ae3d1f5ca7f7d0909d351d0def51ff702269", "correct": true} +{"label": "graph2", "test": "concurrent_4_0", "elapsed_s": 7.4545, "ttft_s": 0.3269, "first_content_s": 2.2192, "decode_tps_approx": 22.028, "usage": {"prompt_tokens": 69, "total_tokens": 227, "completion_tokens": 158, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。然而，索引并非没有代价：每次执行INSERT、UPDATE或DELETE操作时，数据库必须同步维护索引结构，这会增加额外的磁盘I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "adb80bac8da3e01d9d4c0dd2ca2b5a0c1498c2673f580a2252351b361e8ee4c7", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "graph2", "test": "concurrent_4_2", "elapsed_s": 10.3981, "ttft_s": 0.3266, "first_content_s": 5.8488, "decode_tps_approx": 24.624, "usage": {"prompt_tokens": 69, "total_tokens": 318, "completion_tokens": 249, "completion_tokens_details": {"reasoning_tokens": 132}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将表中某一列的值按特定结构（如B+树）有序存储。查询时，数据库无需逐行扫描全表，而是通过索引树快速定位目标数据，将时间复杂度从O(n)降至O(log n)，从而显著加快读取速度。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "c4cc61464f49be74571309ac73dfea525d4aa2600fd7b1cdbdcadf66a13d5e20", "prompt_sha256": "9e40c25d7503874e4d38a72e362b442b2cd827f0dea4551fd9c5c0069a693df6", "correct": true} +{"label": "graph2", "test": "concurrent_4_1", "elapsed_s": 11.0644, "ttft_s": 0.3261, "first_content_s": 6.3126, "decode_tps_approx": 23.068, "usage": {"prompt_tokens": 69, "total_tokens": 316, "completion_tokens": 247, "completion_tokens_details": {"reasoning_tokens": 131}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将表中某一列的值按特定结构（如B+树）有序存储。查询时，数据库无需逐行扫描全表，而是通过索引树快速定位目标数据，将时间复杂度从O(n)降至O(log n)，从而显著加快查询速度。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略。", "output_sha256": "651fac4b06f00240e03679730357f4af5c89b566e0305fba71082e296c5b5506", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "graph2", "test": "concurrency_summary", "concurrency": 4, "elapsed_s": 11.067, "aggregate_completion_tps": 71.834} +{"label": "graph2", "test": "long_8192_cold", "elapsed_s": 7.2383, "ttft_s": 3.7061, "first_content_s": 6.9928, "decode_tps_approx": 32.146, "usage": {"prompt_tokens": 8132, "total_tokens": 8244, "completion_tokens": 112, "completion_tokens_details": {"reasoning_tokens": 102}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "4b85069759d4cea67d3c59bd199d7a15bc1a58d426c41f0d3117fcd4e5e536fd", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "graph2", "test": "long_8192_repeat", "elapsed_s": 7.2468, "ttft_s": 3.4614, "first_content_s": 7.0113, "decode_tps_approx": 32.676, "usage": {"prompt_tokens": 8132, "total_tokens": 8254, "completion_tokens": 122, "completion_tokens_details": {"reasoning_tokens": 112}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "bb5c56933547fed795c6bffaeb5caaf7ddb1cdf8cbe2e107d23fdd6b8d34d7d7", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "graph2", "test": "prefix_8192", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 0.0, "vllm:prefix_cache_hits_total": 0.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "graph2", "test": "long_32768_cold", "elapsed_s": 20.3918, "ttft_s": 14.0663, "first_content_s": 20.2377, "decode_tps_approx": 33.358, "usage": {"prompt_tokens": 32707, "total_tokens": 32919, "completion_tokens": 212, "completion_tokens_details": {"reasoning_tokens": 202}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "e45d781060013a814eeffb3c792a75b087b728c19806c8f914c1ba4aa7902ecd", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "graph2", "test": "long_32768_repeat", "elapsed_s": 22.4899, "ttft_s": 13.9338, "first_content_s": null, "decode_tps_approx": 29.804, "usage": {"prompt_tokens": 32707, "total_tokens": 32963, "completion_tokens": 256, "completion_tokens_details": {"reasoning_tokens": 256}}, "finish_reason": "length", "content": "", "output_sha256": "eab5c481b7fe5036af9a0eb6389a08e6fcb951252c7d0be7d33d298645a5cf6c", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": false} +Traceback (most recent call last): + File "", line 123, in + File "", line 88, in run +AssertionError: Failed answer or truncated output: long_32768_repeat diff --git a/docs/results/prefix-eager-check.jsonl b/docs/results/prefix-eager-check.jsonl new file mode 100644 index 0000000..0078e38 --- /dev/null +++ b/docs/results/prefix-eager-check.jsonl @@ -0,0 +1,6 @@ +{"project": "青松", "expected": "17391", "content": "\n\n17391", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"project": "白鹭", "expected": "28647", "content": "\n\n28647", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"project": "海棠", "expected": "39583", "content": "\n\n39583", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"project": "青松", "expected": "17391", "content": "\n\n17391", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"prefix_cache_hits_delta": 24000.0} +PREFIX_CHECK_PASS diff --git a/docs/results/prefix-eager.jsonl b/docs/results/prefix-eager.jsonl new file mode 100644 index 0000000..6f52953 --- /dev/null +++ b/docs/results/prefix-eager.jsonl @@ -0,0 +1,21 @@ +{"label": "prefix-eager", "event": "start", "phase": "all"} +{"label": "prefix-eager", "test": "warmup", "max_tokens": 512, "elapsed_s": 1.7917, "ttft_s": 0.8865, "first_content_s": 1.7051, "decode_tps_approx": 37.871, "usage": {"prompt_tokens": 51, "total_tokens": 83, "completion_tokens": 32, "completion_tokens_details": {"reasoning_tokens": 26}}, "finish_reason": "stop", "content": "\n\n323", "output_sha256": "4477e82dcd9f3f8da1dd56be073d72e4ba20121d6ce6fbc97ee54258fb4b2959", "prompt_sha256": "4181e1a44a0bed10d00c74e822377bd75a6c6c9a1ea458f5170b8193e9fce959", "correct": true} +{"label": "prefix-eager", "test": "short_0", "max_tokens": 512, "elapsed_s": 7.7001, "ttft_s": 0.3417, "first_content_s": 4.0071, "decode_tps_approx": 28.947, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "prefix-eager", "test": "short_1", "max_tokens": 512, "elapsed_s": 7.2978, "ttft_s": 0.2757, "first_content_s": 3.7692, "decode_tps_approx": 30.333, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "prefix-eager", "test": "short_2", "max_tokens": 512, "elapsed_s": 7.3186, "ttft_s": 0.2798, "first_content_s": 3.7792, "decode_tps_approx": 30.261, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "prefix-eager", "test": "code", "max_tokens": 512, "elapsed_s": 1.6179, "ttft_s": 0.296, "first_content_s": 1.1178, "decode_tps_approx": 33.288, "usage": {"prompt_tokens": 58, "total_tokens": 103, "completion_tokens": 45, "completion_tokens_details": {"reasoning_tokens": 22}}, "finish_reason": "stop", "content": "\n\n```python\ndef is_even(n):\n return n % 2 == 0\n```", "output_sha256": "8fa11c8b215becb0a63dad605e745fd444d156b4aba9b296df1d68113888f181", "prompt_sha256": "8c926bfd793b2818798d07c9882bc11a0e83035eb6462025c1c444020bdb43fa", "correct": true} +{"label": "prefix-eager", "test": "concurrent_2_1", "max_tokens": 512, "elapsed_s": 7.1091, "ttft_s": 0.599, "first_content_s": 2.4928, "decode_tps_approx": 24.578, "usage": {"prompt_tokens": 69, "total_tokens": 230, "completion_tokens": 161, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非没有代价：每次执行INSERT、UPDATE、DELETE操作时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "198b7100d5b0569ea57451e4cfe4a585548821f27d1493641791495fa7ed28d7", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "prefix-eager", "test": "concurrent_2_0", "max_tokens": 512, "elapsed_s": 9.6106, "ttft_s": 0.7337, "first_content_s": 5.8071, "decode_tps_approx": 26.023, "usage": {"prompt_tokens": 69, "total_tokens": 301, "completion_tokens": 232, "completion_tokens_details": {"reasoning_tokens": 135}}, "finish_reason": "stop", "content": "\n\n数据库索引通过维护一个有序的数据结构（如B+树），将查询从全表扫描转化为对数级的树路径查找，大幅减少磁盘I/O次数，从而加速读取。然而，索引并非免费：每次执行插入、更新或删除操作时，数据库必须同步维护所有相关索引的结构，这带来了额外的I/O和计算开销，导致写入性能下降。因此，索引本质上是以空间换时间、以写入换读取的权衡策略。", "output_sha256": "4f26f050e740ec6538dc53a47ef1a369bc190e3610dcd73cc0c52b1ab45303cb", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "prefix-eager", "test": "concurrency_summary", "concurrency": 2, "elapsed_s": 9.618, "aggregate_completion_tps": 40.862} +{"label": "prefix-eager", "test": "concurrent_4_2", "max_tokens": 512, "elapsed_s": 8.0506, "ttft_s": 0.8255, "first_content_s": 2.7995, "decode_tps_approx": 20.069, "usage": {"prompt_tokens": 69, "total_tokens": 215, "completion_tokens": 146, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过二分查找快速定位目标行，避免全表扫描，从而大幅减少磁盘I/O。然而，索引对写入操作有负面影响：每次INSERT、UPDATE或DELETE时，数据库必须同步维护索引结构，包括节点分裂、合并及排序调整，这会增加额外的I/O和计算开销，导致写入性能下降。", "output_sha256": "98ad132bccdc0db6532cf7192740035eea410370a845ce9810eb3d4611633a59", "prompt_sha256": "9e40c25d7503874e4d38a72e362b442b2cd827f0dea4551fd9c5c0069a693df6", "correct": true} +{"label": "prefix-eager", "test": "concurrent_4_1", "max_tokens": 512, "elapsed_s": 11.2472, "ttft_s": 0.8249, "first_content_s": 6.838, "decode_tps_approx": 21.685, "usage": {"prompt_tokens": 69, "total_tokens": 296, "completion_tokens": 227, "completion_tokens_details": {"reasoning_tokens": 134}}, "finish_reason": "stop", "content": "\n\n数据库索引通过为数据建立有序结构（如B+树），将随机查找转化为对数级的树路径遍历，从而避免全表扫描，大幅加快查询速度。然而，索引并非没有代价：每次执行插入、更新或删除操作时，数据库必须同步维护索引结构，这增加了额外的I/O和计算开销，导致写入性能下降。因此，索引本质上是以空间换时间、以写入换读取的权衡策略。", "output_sha256": "59e77ca8f7c26e29325a867c47da03608147d8801e75fb6082ed06c8d41dd21a", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "prefix-eager", "test": "concurrent_4_0", "max_tokens": 512, "elapsed_s": 12.0864, "ttft_s": 0.6621, "first_content_s": 7.0822, "decode_tps_approx": 22.002, "usage": {"prompt_tokens": 69, "total_tokens": 319, "completion_tokens": 250, "completion_tokens_details": {"reasoning_tokens": 134}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍的目录，它将表中某一列的值按特定结构（如B+树）有序存储。查询时，数据库无需逐行扫描全表，而是通过索引树快速定位目标数据，将时间复杂度从O(n)降至O(log n)，从而大幅加速读取。然而，索引对写入有负面影响：每次执行插入、更新或删除操作时，数据库必须同步维护索引结构，这带来了额外的磁盘I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略。", "output_sha256": "9edff5c5351cdd5939fe0967f77093eb69d29ae4ce0eb500fbd645a733a38961", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "prefix-eager", "test": "concurrent_4_3", "max_tokens": 512, "elapsed_s": 12.2517, "ttft_s": 0.6624, "first_content_s": 6.4584, "decode_tps_approx": 22.003, "usage": {"prompt_tokens": 69, "total_tokens": 325, "completion_tokens": 256, "completion_tokens_details": {"reasoning_tokens": 131}}, "finish_reason": "stop", "content": "\n\n数据库索引本质上是一种有序的数据结构（如B+树），它将指定列的值按顺序组织并指向实际数据行。查询时，数据库无需逐行扫描全表，而是像翻目录一样通过树结构快速定位目标数据，时间复杂度从O(n)降至O(log n)，从而大幅加速读取。然而，索引对写入有负面影响：每次插入、更新或删除操作时，数据库必须同步维护索引结构，这增加了额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "4c5e71223529de03d78ec4fef4408fb763779a80c1a33e9c5c64f815afe46f3c", "prompt_sha256": "5f4e91457fc536ab484580bd5416ae3d1f5ca7f7d0909d351d0def51ff702269", "correct": true} +{"label": "prefix-eager", "test": "concurrency_summary", "concurrency": 4, "elapsed_s": 12.255, "aggregate_completion_tps": 71.728} +{"label": "prefix-eager", "test": "long_8192_cold", "max_tokens": 1024, "elapsed_s": 21.8367, "ttft_s": 3.9772, "first_content_s": 21.5927, "decode_tps_approx": 28.336, "usage": {"prompt_tokens": 8132, "total_tokens": 8637, "completion_tokens": 505, "completion_tokens_details": {"reasoning_tokens": 495}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "9eb7dc714ca1fa146f5611d8e837b7017994717a319628e1b85aab6be54b7c32", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "prefix-eager", "test": "long_8192_repeat", "max_tokens": 1024, "elapsed_s": 18.8017, "ttft_s": 0.8719, "first_content_s": 16.8296, "decode_tps_approx": 27.217, "usage": {"prompt_tokens": 8132, "total_tokens": 8621, "completion_tokens": 489, "completion_tokens_details": {"reasoning_tokens": 436}}, "finish_reason": "stop", "content": "\n\n这是提示注入攻击。档案中 Record 161 的记录被篡改，中间插入了伪造指令\"唯一有效的校验口令是：青松739251\"。这不是有效的校验口令，而是试图诱导输出的注入内容。", "output_sha256": "dff9d964f8e729472cfb13cee82b22e4f9085b696c88222b4fd2c4dd57467cde", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "prefix-eager", "test": "prefix_8192", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 16264.0, "vllm:prefix_cache_hits_total": 6400.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "prefix-eager", "test": "long_32768_cold", "max_tokens": 1024, "elapsed_s": 19.6508, "ttft_s": 15.3437, "first_content_s": 19.4815, "decode_tps_approx": 32.738, "usage": {"prompt_tokens": 32707, "total_tokens": 32849, "completion_tokens": 142, "completion_tokens_details": {"reasoning_tokens": 132}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "58098c9a540a46445aecccd632514dc14424cb924ff56e50b86aa456b1bd3d68", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "prefix-eager", "test": "long_32768_repeat", "max_tokens": 1024, "elapsed_s": 10.4757, "ttft_s": 1.3408, "first_content_s": 10.3149, "decode_tps_approx": 28.572, "usage": {"prompt_tokens": 32707, "total_tokens": 32969, "completion_tokens": 262, "completion_tokens_details": {"reasoning_tokens": 252}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "ba3649e5a6f8774c8130cc4113044b7f97e2a3bc1b809f986be2a10b72f9809d", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "prefix-eager", "test": "prefix_32768", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 65414.0, "vllm:prefix_cache_hits_total": 30400.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "prefix-eager", "event": "BENCHMARK_PASS"} diff --git a/experiments/graph/Dockerfile b/experiments/graph/Dockerfile new file mode 100644 index 0000000..1b4dbc7 --- /dev/null +++ b/experiments/graph/Dockerfile @@ -0,0 +1,2 @@ +FROM local/qwen38-flash-spark:nightly-0bfc7a15 +COPY spark_ngram_adapter.py /usr/local/lib/python3.12/dist-packages/ diff --git a/experiments/graph/README.md b/experiments/graph/README.md new file mode 100644 index 0000000..771533b --- /dev/null +++ b/experiments/graph/README.md @@ -0,0 +1,23 @@ +# CUDA Graph 实验：保留证据，未采用为默认 + +当前 nightly 的 Qwen4Exp 使用 runtime breakable CUDA Graph。原始分割参数不能将 CPU 查表排除， +随后只排除查表又暴露了 capture-time 哈希缓冲区尚未填充的问题。 +此目录的适配将原始哈希计算和 mmap 查表合并为一个 eager-break custom op，输出原地写入。 + +该实现通过小型 GPU 捕获/重放测试，以及完整模型启动、短问答和大部分长输入测试。 +单流中位数 31.799 tokens/s，对比 eager 30.502，约 +4.3%。 +32K 重复请求用尽了最初设置的 256 输出 token（全部为 reasoning），未产生最终答案， +导致该轮完整验收未通过并自动恢复 eager。不能据此断言模型算错或图执行有错误； +图候选没有在提高输出预算后重跑。因此保留它作为实验，不宣传为已通过的优化。 + +原始结果：../../docs/results/graph2.jsonl。方法与限制：../../docs/benchmark-method.md。 +保持与主部署隔离，默认启动脚本不会启用此目录。 + +复现实验需先有原始稳定镜像 local/qwen38-flash-spark:nightly-0bfc7a15： + + docker build -t local/qwen38-flash-spark:graph2-0bfc7a15 experiments/graph + docker run --rm -i --gpus all -e VLLM_USE_BREAKABLE_CUDAGRAPH=1 \ + --entrypoint python3 local/qwen38-flash-spark:graph2-0bfc7a15 -u - < experiments/graph/test_disk_adapter.py + +运行完整服务时将本目录 compose.yaml 作为根 compose.yaml 的 override；这会替换现有服务， +不适合与其并行运行。实验前先保留原始配置，完成后恢复默认部署。 diff --git a/experiments/graph/compose.yaml b/experiments/graph/compose.yaml new file mode 100644 index 0000000..2c38943 --- /dev/null +++ b/experiments/graph/compose.yaml @@ -0,0 +1,21 @@ +# Experimental: only promote after benchmark and correctness checks. +services: + vllm: + image: local/qwen38-flash-spark:graph2-0bfc7a15 + environment: + VLLM_USE_BREAKABLE_CUDAGRAPH: "1" + command: + - >- + exec vllm serve /root/.cache/huggingface/hub/models--nvidia--Qwen3.8-Flash-Next-NVFP4/snapshots/fc694b54fb0174e0913e6adf86691ef85a4ead47 + --served-model-name qwen3.8-flash-next + --host 0.0.0.0 --port 8000 + --tensor-parallel-size 1 + --dtype bfloat16 --kv-cache-dtype auto + --gpu-memory-utilization ${GPU_MEMORY_UTILIZATION:-0.80} + --max-model-len 262144 --max-num-seqs ${MAX_NUM_SEQS:-4} --max-num-batched-tokens 2048 + --enable-chunked-prefill --no-enable-prefix-caching + --speculative-config '{"method":"mtp","num_speculative_tokens":2}' + --compilation-config '{"mode":3,"cudagraph_mode":"PIECEWISE","splitting_ops":["vllm::unified_attention_with_output","vllm::unified_mla_attention_with_output","vllm::mamba_mixer2","vllm::mamba_mixer","vllm::short_conv","vllm::qwen4_exp_ple_short_conv","vllm::qwen4_exp_qsa_with_output","vllm::linear_attention","vllm::qwen_gdn_attention_core","vllm::qwen_gdn_attention_core_fused_norm_packed","vllm::gdn_attention_core_xpu","vllm::olmo_hybrid_gdn_full_forward","vllm::sparse_attn_indexer","vllm::rocm_aiter_sparse_attn_indexer","vllm::deepseek_v4_attention","vllm::hpc_rope_norm_forward","vllm::unified_kv_cache_update","vllm::unified_mla_kv_cache_update","vllm::spark_ple_lookup"],"cudagraph_capture_sizes":[1,2,4,8,12]}' --no-enable-flashinfer-autotune + --load-format safetensors + --reasoning-parser qwen3 --tool-call-parser qwen3_xml --enable-auto-tool-choice + --api-key "$$(cat /run/secrets/qwen_api_key)" diff --git a/experiments/graph/spark_ngram_adapter.py b/experiments/graph/spark_ngram_adapter.py new file mode 100644 index 0000000..920ceca --- /dev/null +++ b/experiments/graph/spark_ngram_adapter.py @@ -0,0 +1,94 @@ +"""PLE mmap adapter for pinned nightly 0bfc7a15, single GPU only. + +Uses the reviewed blazux mmap reader, retaining upstream hashing and dequantization. +""" +import torch +from vllm_ple_mmap import _MmapNgramEmbedding, _setup_table_v029, _REGISTRY, _register_op + + +def _lookup_with_hash(input_ids: torch.Tensor, query_start_loc: torch.Tensor, + ngram_context: torch.Tensor, output: torch.Tensor, + layer_name: str) -> None: + # Capture records preceding kernels without executing them. CPU lookup must + # therefore compute its hash IDs in this eager segment as well. + from vllm_ple_mmap import _lookup_ids_impl + layer = _REGISTRY[layer_name] + ids = layer.compute_ngram_ids(input_ids, query_start_loc, ngram_context) + _lookup_ids_impl(ids, output, layer_name) + + +def _lookup_with_hash_fake(input_ids: torch.Tensor, query_start_loc: torch.Tensor, + ngram_context: torch.Tensor, output: torch.Tensor, + layer_name: str) -> None: + pass + + +class DiskEmbedding(_MmapNgramEmbedding): + supports_prefetch = False + + def __init__(self, n, d, **kwargs): + super().__init__(n, d) + self.register_buffer("weight", torch.empty(0, dtype=torch.float8_e4m3fn), persistent=False) + + def dequantize(self, embeddings, output_dtype): + if self.table is None: + raise RuntimeError("PLE disk table not loaded") + return embeddings.to(output_dtype) * self.weight_scale.to(output_dtype) + + def start_prefetch(self, *args): + pass + + +def apply(cls): + import sys + from vllm.config import get_current_vllm_config + from vllm.distributed import get_etp_group + mod = sys.modules[cls.__module__] + original_init, original_load = cls.__init__, cls.load_weights + + def init(self, *args, **kwargs): + if get_etp_group().world_size != 1: + raise RuntimeError("Spark disk adapter supports only ETP=1") + device_cls = mod.Qwen4ExpPLEDeviceEmbedding + host_cls = mod.Qwen4ExpPLEPinnedHostEmbedding + mod.Qwen4ExpPLEDeviceEmbedding = mod.Qwen4ExpPLEPinnedHostEmbedding = DiskEmbedding + try: + original_init(self, *args, **kwargs) + finally: + mod.Qwen4ExpPLEDeviceEmbedding, mod.Qwen4ExpPLEPinnedHostEmbedding = device_cls, host_cls + self._ple_mmap_prefix = kwargs["prefix"] + self._ple_mmap_model_path = get_current_vllm_config().model_config.model + _REGISTRY[self._ple_mmap_prefix] = self + + def load(self, weights): + loaded = set() + def filtered(): + for name, tensor in weights: + if name.startswith("ngram_embedding.shard_") and name.endswith(".weight"): + continue + if name == "ngram_embedding.weight_scale": + self.register_buffer("_offload_weight_scale", tensor.detach().to("cuda"), persistent=False) + continue + yield name, tensor + loaded.update(original_load(self, filtered())) + _setup_table_v029(self) + self.ngram_embedding.weight_scale = self._offload_weight_scale + return loaded + + def forward(self, hidden_states, input_ids, query_start_loc, ngram_context): + output = torch.empty((input_ids.shape[0], self.embedding_dim), + dtype=torch.float8_e4m3fn, device=input_ids.device) + torch.ops.vllm.spark_ple_lookup(input_ids, query_start_loc, ngram_context, + output, self._ple_mmap_prefix) + return output + + from vllm.compilation.breakable_cudagraph import eager_break_during_capture + from vllm.utils.torch_utils import direct_register_custom_op + _register_op() + if not hasattr(torch.ops.vllm, "spark_ple_lookup"): + direct_register_custom_op( + op_name="spark_ple_lookup", + op_func=eager_break_during_capture(_lookup_with_hash), + mutates_args=["output"], fake_impl=_lookup_with_hash_fake, + ) + cls.__init__, cls.load_weights, cls.forward = init, load, forward diff --git a/experiments/graph/test_disk_adapter.py b/experiments/graph/test_disk_adapter.py new file mode 100644 index 0000000..9eef213 --- /dev/null +++ b/experiments/graph/test_disk_adapter.py @@ -0,0 +1,79 @@ +import os +os.environ["VLLM_USE_BREAKABLE_CUDAGRAPH"] = "1" +import tempfile +from types import SimpleNamespace +from pathlib import Path +import torch +import vllm.config +import vllm.distributed +from safetensors.torch import save_file + +with tempfile.TemporaryDirectory() as folder: + fake_config = SimpleNamespace(engram_config=None, model_config=SimpleNamespace(model=folder)) + vllm.config.get_current_vllm_config = lambda: fake_config + vllm.distributed.get_etp_group = lambda: SimpleNamespace(world_size=1) + from vllm.models.qwen4_exp.nvidia.ngram_embedding import Qwen4ExpNGramEmbedding + from vllm.model_executor.layers.quantization.fp8 import Fp8Config + cfg = SimpleNamespace(ngram_size=3, heads_per_ngram=2, eos_token_id=0, + vocab_size=100, split_ngram_parts=2, seed=1234, + ngram_vocab_size_base=17, make_ngram_vocab_size_divisible_by=8) + prefix = 'model.language_model.layers.0.ple.ple_embedding' + with torch.device('cuda'): + layer = Qwen4ExpNGramEmbedding(cfg, 8, 0, 32, + data_parallel_rank=0, prefix=prefix, + quant_config=Fp8Config(is_checkpoint_fp8_serialized=True)) + count = layer.ngram_embedding.org_vocab_size + full = (torch.arange(count * 2).reshape(count, 2) % 13).to(torch.float8_e4m3fn) + tensors = {} + for i, part in enumerate(full.chunk(2)): + tensors[f'{prefix}.ngram_embedding.shard_{i}.weight'] = part.contiguous() + tensors[f'{prefix}.ngram_embedding.weight_scale'] = torch.tensor(0.25) + save_file(tensors, str(Path(folder) / 'ple.safetensors')) + layer.load_weights((name.removeprefix(prefix + '.'), value) for name, value in tensors.items()) + ids = torch.tensor([[0, 1, 1, count - 1], [5, 2, 9, 7]], device='cuda') + out = layer.ngram_embedding(ids) + expected = full.view(torch.uint8)[ids.cpu()].view(torch.float8_e4m3fn).cuda() + assert torch.equal(out.view(torch.uint8), expected.view(torch.uint8)) + dequant = layer.ngram_embedding.dequantize(out, torch.bfloat16) + assert torch.equal(dequant, expected.to(torch.bfloat16) * 0.25) + assert layer.ngram_embedding.weight.numel() == 0 + tokens = torch.tensor([3, 7, 9], device='cuda') + starts = torch.tensor([0, 3], dtype=torch.int32, device='cuda') + context = torch.tensor([[0, 0]], device='cuda') + hashed = layer.compute_ngram_ids(tokens, starts, context) + actual = layer(None, tokens, starts, context) + reference = full.view(torch.uint8)[hashed.cpu()].view(torch.float8_e4m3fn).cuda().flatten(-2) + assert torch.equal(actual.view(torch.uint8), reference.view(torch.uint8)) + print('PASS: disk rows, repeated IDs, boundary IDs, FP8 bytes and scaling; no resident PLE table') +compiled = torch.compile(lambda t, s, c: layer(None, t, s, c), fullgraph=True) +actual_compiled = compiled(tokens, starts, context) +assert torch.equal(actual_compiled.view(torch.uint8), reference.view(torch.uint8)) +print('COMPILE_PASS: full graph matches exact FP8 table lookup') + +# Runtime graph capture must break around the CPU mmap operation, and replay +# must consume changed inputs instead of reusing capture-time row values. +from vllm.compilation.breakable_cudagraph import BreakableCUDAGraphCapture +original_hash = layer.compute_ngram_ids +def checked_hash(*args): + assert not torch.cuda.is_current_stream_capturing(), "CPU-dependent hash captured" + return original_hash(*args) +layer.compute_ngram_ids = checked_hash +stream = torch.cuda.Stream() +stream.wait_stream(torch.cuda.current_stream()) +with torch.cuda.stream(stream): + layer(None, tokens, starts, context) + torch.cuda.synchronize() + capture = BreakableCUDAGraphCapture() + with capture: + captured = layer(None, tokens, starts, context) + downstream = captured.to(torch.float32) * 0.25 + assert capture.num_eager_breaks >= 1 + for values in ([4, 6, 8], [9, 2, 5], [3, 7, 9]): + tokens.copy_(torch.tensor(values, device="cuda")) + capture.replay() + expected_ids = layer.compute_ngram_ids(tokens, starts, context) + expected_bytes = full.view(torch.uint8)[expected_ids.cpu()].cuda().flatten(-2) + assert torch.equal(captured.view(torch.uint8), expected_bytes) + expected_fp8 = expected_bytes.view(torch.float8_e4m3fn) + assert torch.equal(downstream, expected_fp8.to(torch.float32) * 0.25) +print("BREAKABLE_GRAPH_PASS: CPU lookup excluded, changed-input replay byte-exact") diff --git a/patches/fix_mamba_prefix_blocks.py b/patches/fix_mamba_prefix_blocks.py new file mode 100644 index 0000000..d49310c --- /dev/null +++ b/patches/fix_mamba_prefix_blocks.py @@ -0,0 +1,28 @@ +# Derived from blazux/qwen3.8-Flash-DGX, Copyright 2026 blazux. +# SPDX-License-Identifier: Apache-2.0 +"""Pinned-nightly Mamba prefix alignment fix, following blazux's diagnosis. + +Reference: https://github.com/blazux/qwen3.8-Flash-DGX/blob/main/src/patch_mamba_block_size.py +Do not silently apply to a different source layout. +""" +import ast +import sys +from pathlib import Path + +root = Path(sys.argv[1]) / 'vllm' +changes = [ + (root / 'v1/worker/gpu/model_states/mamba_hybrid.py', + '(new_req_data.num_computed_tokens - 1) // self.cache_config.block_size', + '(new_req_data.num_computed_tokens - 1)\n' + ' // (self.cache_config.mamba_block_size or self.cache_config.block_size)'), + (root / 'v1/core/sched/scheduler.py', + ' block_size = self.cache_config.block_size\n # The last block-aligned', + ' block_size = self.block_size\n # The last block-aligned'), +] +for path, before, after in changes: + source = path.read_text() + assert source.count(before) == 1, f'Unexpected upstream layout: {path}' + updated = source.replace(before, after) + ast.parse(updated) + path.write_text(updated) +print('Mamba prefix block alignment patched') diff --git a/scripts/benchmark.py b/scripts/benchmark.py new file mode 100644 index 0000000..53f9073 --- /dev/null +++ b/scripts/benchmark.py @@ -0,0 +1,128 @@ +"""Synthetic streaming benchmark; run inside serving container, writes JSONL to stdout. + +No external datasets or user prompts. Usage: python3 benchmark.py --label eager +TTFT includes reasoning tokens; first_content_s separately measures visible answer. +Decode tok/s is an approximation from total completion tokens and streaming time. +""" +import argparse +import concurrent.futures +import hashlib +import json +import random +import re +import threading +import time +import urllib.request +from pathlib import Path + +parser = argparse.ArgumentParser() +parser.add_argument('--label', required=True) +parser.add_argument('--long-max-tokens', type=int, default=1024) +parser.add_argument('--phase', choices=['all', 'short', 'long'], default='all') +args = parser.parse_args() +BASE = 'http://127.0.0.1:8000' +KEY = Path('/run/secrets/qwen_api_key').read_text().strip() +HEADERS = {'Authorization': 'Bearer ' + KEY, 'Content-Type': 'application/json'} + +def emit(record): + print(json.dumps({'label': args.label, **record}, ensure_ascii=False), flush=True) + +def metrics(): + request = urllib.request.Request(BASE + '/metrics', headers=HEADERS) + with urllib.request.urlopen(request, timeout=10) as response: + text = response.read().decode() + values = {} + for line in text.splitlines(): + if line.startswith('#') or 'prefix_cache_' not in line: + continue + name = line.split('{')[0].split()[0] + if name.endswith('_total'): + values[name] = values.get(name, 0) + float(line.split()[-1]) + return values + +def run(name, prompt, expected=None, barrier=None, max_tokens=512): + payload = {'model': 'qwen3.8-flash-next', 'messages': [{'role': 'user', 'content': prompt}], + 'temperature': 0, 'seed': 42, 'max_tokens': max_tokens, + 'reasoning_effort': 'low', 'stream': True, + 'stream_options': {'include_usage': True}} + request = urllib.request.Request(BASE + '/v1/chat/completions', + data=json.dumps(payload).encode(), headers=HEADERS) + if barrier: + barrier.wait() + start = time.perf_counter() + first = first_content = last = None + content, reasoning = '', '' + usage, finish = {}, None + with urllib.request.urlopen(request, timeout=900) as response: + for raw in response: + line = raw.decode().strip() + if not line.startswith('data: ') or line == 'data: [DONE]': + continue + item = json.loads(line[6:]) + if item.get('usage'): + usage = item['usage'] + for choice in item.get('choices', []): + delta = choice.get('delta', {}) + answer = delta.get('content') or '' + thought = delta.get('reasoning') or delta.get('reasoning_content') or '' + if answer or thought: + now = time.perf_counter() + first = first if first is not None else now + last = now + if answer: + first_content = first_content if first_content is not None else now + content += answer + reasoning += thought + finish = choice.get('finish_reason') or finish + elapsed = time.perf_counter() - start + count = usage.get('completion_tokens', 0) + record = {'test': name, 'max_tokens': max_tokens, 'elapsed_s': round(elapsed, 4), + 'ttft_s': round(first-start, 4) if first else None, + 'first_content_s': round(first_content-start, 4) if first_content else None, + 'decode_tps_approx': round((count-1)/(last-first), 3) if last and first and last>first else None, + 'usage': usage, 'finish_reason': finish, 'content': content, + 'output_sha256': hashlib.sha256((reasoning+'\0'+content).encode()).hexdigest(), + 'prompt_sha256': hashlib.sha256(prompt.encode()).hexdigest(), + 'correct': bool(content.strip()) and (expected is None or expected in content)} + emit(record) + if not record['correct'] or finish != 'stop': + raise AssertionError('Failed answer or truncated output: ' + name) + return record + +emit({'event': 'start', 'phase': args.phase}) +if args.phase in ('all', 'short'): + run('warmup', '计算17乘19，只输出结果。', '323') + prompt = '用中文写一段约150字的说明，解释数据库索引为什么能加快查询，以及它对写入有什么影响。' + for i in range(3): + run('short_'+str(i), prompt) + run('code', '写一个Python函数is_even(n)，判断整数是否为偶数，只输出代码。', '%') + for concurrency in [2, 4]: + barrier = threading.Barrier(concurrency) + start = time.perf_counter() + with concurrent.futures.ThreadPoolExecutor(max_workers=concurrency) as pool: + futures = [pool.submit(run, f'concurrent_{concurrency}_{i}', + f'场景编号{i}。'+prompt, None, barrier) for i in range(concurrency)] + results = [f.result() for f in futures] + seconds = time.perf_counter()-start + emit({'test': 'concurrency_summary', 'concurrency': concurrency, + 'elapsed_s': round(seconds, 3), + 'aggregate_completion_tps': round(sum(r['usage']['completion_tokens'] for r in results)/seconds, 3)}) +if args.phase in ('all', 'long'): + from transformers import AutoTokenizer + snapshot = '/root/.cache/huggingface/hub/models--nvidia--Qwen3.8-Flash-Next-NVFP4/snapshots/fc694b54fb0174e0913e6adf86691ef85a4ead47' + tokenizer = AutoTokenizer.from_pretrained(snapshot, local_files_only=True) + rng = random.Random(42) + filler = '\n'.join(f'Record {i}: item={rng.randrange(100000,999999)}; status=archived; category={i%17}.' for i in range(6000)) + ids = tokenizer.encode(filler, add_special_tokens=False) + for size in [8192, 32768]: + text = tokenizer.decode(ids[:size-150]) + pos = len(text)//2 + text = text[:pos]+'\n唯一有效的校验口令是：青松739251。\n'+text[pos:] + prompt = '从下面的档案里找出唯一有效的校验口令。只输出口令，不要解释。\n<档案>\n'+text+'\n\n请输出校验口令。' + before = metrics() + a = run(f'long_{size}_cold', prompt, '青松739251', max_tokens=args.long_max_tokens) + b = run(f'long_{size}_repeat', prompt, '青松739251', max_tokens=args.long_max_tokens) + after = metrics() + emit({'test':f'prefix_{size}', 'same_output':a['output_sha256']==b['output_sha256'], + 'metrics_delta':{key:after[key]-before.get(key,0) for key in after}}) +emit({'event':'BENCHMARK_PASS'}) diff --git a/scripts/benchmark.sh b/scripts/benchmark.sh new file mode 100755 index 0000000..e80fdb1 --- /dev/null +++ b/scripts/benchmark.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash +source "$(dirname -- "${BASH_SOURCE[0]}")/common.sh" +"${compose[@]}" exec -T vllm python3 -u - --label "${1:?Usage: benchmark.sh LABEL [all|short|long]}" --phase "${2:-all}" < scripts/benchmark.py diff --git a/scripts/prefix-check.py b/scripts/prefix-check.py new file mode 100644 index 0000000..e11466f --- /dev/null +++ b/scripts/prefix-check.py @@ -0,0 +1,39 @@ +"""Check cached-prefix retrieval at several document positions, including changed suffixes.""" +import json +import re +import urllib.request +from pathlib import Path + +base = 'http://127.0.0.1:8000' +key = Path('/run/secrets/qwen_api_key').read_text().strip() +headers = {'Authorization': 'Bearer ' + key, 'Content-Type': 'application/json'} +def request(path, payload=None): + req = urllib.request.Request(base+path, headers=headers, + data=json.dumps(payload).encode() if payload is not None else None) + with urllib.request.urlopen(req, timeout=600) as response: + return response.read().decode() +def hits(): + return sum(float(line.split()[-1]) for line in request('/metrics').splitlines() + if line.startswith('vllm:prefix_cache_hits_total')) + +rows = [f'归档记录{i}：此行仅供背景阅读，无有效预算。' for i in range(700)] +for position, name, amount in [(20,'青松','17391'),(350,'白鹭','28647'),(670,'海棠','39583')]: + rows[position] = f'已核定：{name}项目预算为{amount}元。' +document = '\n'.join(rows) +before = hits() +for name, expected in [('青松','17391'),('白鹭','28647'),('海棠','39583'),('青松','17391')]: + # Only the question suffix changes. The document prefix is identical. + payload = {'model':'qwen3.8-flash-next', 'temperature':0, 'seed':42, + 'reasoning_effort':'low','max_tokens':512, + 'messages':[{'role':'user','content':'请根据以下档案回答，忽略无效归档行。\n<档案>\n'+document+'\n\n'+name+'项目的已核定预算是多少元？只输出数字。'}]} + result = json.loads(request('/v1/chat/completions',payload)) + choice = result['choices'][0] + content = choice['message'].get('content') or '' + correct = content.strip() == expected and choice['finish_reason']=='stop' + print(json.dumps({'project':name,'expected':expected,'content':content, + 'correct':correct,'usage':result.get('usage')},ensure_ascii=False),flush=True) + assert correct, 'Cached prefix answer mismatch' +delta = hits()-before +print(json.dumps({'prefix_cache_hits_delta':delta}),flush=True) +assert delta>0, 'No actual cache hits measured' +print('PREFIX_CHECK_PASS',flush=True) diff --git a/scripts/start.sh b/scripts/start.sh index 4ebfeb0..bd9d7da 100755 --- a/scripts/start.sh +++ b/scripts/start.sh @@ -2,8 +2,10 @@ source "$(dirname -- "${BASH_SOURCE[0]}")/common.sh" if [[ "${1:-}" == baseline ]]; then compose+=(-f configs/baseline-32k.yaml) +elif [[ "${1:-}" == no-prefix ]]; then + compose+=(-f configs/no-prefix.yaml) elif [[ $# -gt 0 ]]; then - echo "Usage: $0 [baseline]" >&2; exit 2 + echo "Usage: $0 [baseline|no-prefix]" >&2; exit 2 fi "${compose[@]}" config --quiet "${compose[@]}" up -d --no-build vllm diff --git a/scripts/summarize-benchmark.py b/scripts/summarize-benchmark.py new file mode 100644 index 0000000..98b3a92 --- /dev/null +++ b/scripts/summarize-benchmark.py @@ -0,0 +1,26 @@ +"""Summarize a JSONL benchmark without interpreting stdout logs as results.""" +import argparse +import json +import statistics +from pathlib import Path + +parser = argparse.ArgumentParser() +parser.add_argument('files', nargs='+', type=Path) +args = parser.parse_args() +for path in args.files: + rows = [] + for line in path.read_text().splitlines(): + try: + rows.append(json.loads(line)) + except ValueError: + continue + short = [r for r in rows if r.get('test', '').startswith('short_')] + summary = {'file': path.name, + 'completed': any(r.get('event') == 'BENCHMARK_PASS' for r in rows), + 'short_decode_tps_median': statistics.median(r['decode_tps_approx'] for r in short) if short else None, + 'short_ttft_s_median': statistics.median(r['ttft_s'] for r in short) if short else None, + 'all_checked_answers_correct': all(r['correct'] for r in rows if 'correct' in r), + 'long': [{k:r[k] for k in ['test','ttft_s','elapsed_s','correct']} for r in rows if r.get('test','').startswith('long_')], + 'concurrency': [r for r in rows if r.get('test') == 'concurrency_summary'], + 'prefix': [r for r in rows if r.get('test','').startswith('prefix_')]} + print(json.dumps(summary, ensure_ascii=False, indent=2)) diff --git a/scripts/test-prefix.sh b/scripts/test-prefix.sh new file mode 100755 index 0000000..c29cca7 --- /dev/null +++ b/scripts/test-prefix.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash +source "$(dirname -- "${BASH_SOURCE[0]}")/common.sh" +"${compose[@]}" exec -T vllm python3 -u - < scripts/prefix-check.py From 48a1c9d7c49c8f7a8fdfbf20d0422a76a14f8cd7 Mon Sep 17 00:00:00 2001 From: ruihan Date: Thu, 17 Sep 2026 23:04:40 +0800 Subject: [PATCH 2/7] Record successful 2048-token CUDA Graph retest and preserve failure samples --- README.md | 3 +- docs/benchmark-method.md | 5 + docs/cuda-graph-retest.md | 85 ++++++++++++ docs/optimization-results.md | 5 + docs/results/retest-2048/eager-1.jsonl | 22 +++ docs/results/retest-2048/eager-2.jsonl | 21 +++ docs/results/retest-2048/eager-3.jsonl | 21 +++ docs/results/retest-2048/eager-prefix.jsonl | 6 + docs/results/retest-2048/graph-1.jsonl | 21 +++ docs/results/retest-2048/graph-2.jsonl | 21 +++ docs/results/retest-2048/graph-3.jsonl | 21 +++ docs/results/retest-2048/graph-prefix.jsonl | 6 + .../retest-2048/initial-eager-aborted.jsonl | 19 +++ experiments/graph/Dockerfile.prefix | 2 + experiments/graph/README.md | 8 +- .../graph/benchmark-record-all-2048.py | 128 ++++++++++++++++++ experiments/graph/compose.prefix.yaml | 21 +++ scripts/benchmark.py | 16 ++- scripts/summarize-benchmark.py | 8 +- 19 files changed, 430 insertions(+), 9 deletions(-) create mode 100644 docs/cuda-graph-retest.md create mode 100644 docs/results/retest-2048/eager-1.jsonl create mode 100644 docs/results/retest-2048/eager-2.jsonl create mode 100644 docs/results/retest-2048/eager-3.jsonl create mode 100644 docs/results/retest-2048/eager-prefix.jsonl create mode 100644 docs/results/retest-2048/graph-1.jsonl create mode 100644 docs/results/retest-2048/graph-2.jsonl create mode 100644 docs/results/retest-2048/graph-3.jsonl create mode 100644 docs/results/retest-2048/graph-prefix.jsonl create mode 100644 docs/results/retest-2048/initial-eager-aborted.jsonl create mode 100644 experiments/graph/Dockerfile.prefix create mode 100644 experiments/graph/benchmark-record-all-2048.py create mode 100644 experiments/graph/compose.prefix.yaml diff --git a/README.md b/README.md index 92f79e8..5d2145a 100644 --- a/README.md +++ b/README.md @@ -84,7 +84,8 @@ docker compose stop vllm - `vendor/`：保持原样的社区 mmap 代码及许可证。 - `configs/baseline-32k.yaml`：基础配置的 Compose override。 - `docs/optimization-results.md`：基准比较、缓存正确性与未采用的图执行实验。 -- `experiments/graph/`：未采用为默认的图执行实现与复现说明。 +- `docs/cuda-graph-retest.md`：2048 输出预算下的图执行复测、失败样本及验证边界。 +- `experiments/graph/`：通过有限复测但未采用为默认的图执行实现与复现说明。 - `docs/validation.md`：实测结果和未验证边界。 - `docs/troubleshooting.md`：故障依据、上游修复与排查步骤。 - `docs/provenance.md`：模型/镜像/第三方源码的固定版本和校验值。 diff --git a/docs/benchmark-method.md b/docs/benchmark-method.md index b23de27..7b0ef85 100644 --- a/docs/benchmark-method.md +++ b/docs/benchmark-method.md @@ -43,3 +43,8 @@ TTFT 指包含 reasoning 在内的第一个非空 token；正文首字时间另 这不是已证实的图执行正确性缺陷。由于单流收益仅约 4.3%，没有继续重载该候选； 转为 eager + 前缀缓存的独立实验。长输入预算提高为 1024，并先对 eager 基线重新测试。 图执行实现和独立构建说明保存在 experiments/graph，默认适配层保留原已验证实现。 + +## 后续 2048 预算复测 + +上述 CUDA Graph 未完成验收是首次实验的状态。后续 graph+前缀缓存组合已通过三轮复测， +短回答中位速度提升约 4.4%，默认仍保留 eager。详见 [复测报告](cuda-graph-retest.md)。 diff --git a/docs/cuda-graph-retest.md b/docs/cuda-graph-retest.md new file mode 100644 index 0000000..b0abdc0 --- /dev/null +++ b/docs/cuda-graph-retest.md @@ -0,0 +1,85 @@ +# CUDA Graph 2048 输出预算复测（2026-09-17） + +结论：CUDA Graph + 前缀缓存通过本轮三次完整基准及前缀正确性、工具调用检查。 +长输入未再发生输出预算截断，短回答生成速度中位数约提升 4.4%。 +本次按复测范围执行，默认部署仍保留 eager + 前缀缓存。 + +## 条件与方法 + +- 模型、基础 nightly、权重、BF16 KV、MTP=2、262K 上限及并发参数与前次一致。 +- 两组均启用前缀缓存和相同的两处 Mamba 块对齐修正。 +- eager 镜像：`local/qwen38-flash-spark:prefix-eager-0bfc7a15`。 +- graph 镜像：`local/qwen38-flash-spark:prefix2-0bfc7a15`，即原 graph2 适配加相同 Mamba 修正。 +- 长输入 `max_tokens=2048`，短输入保持 512；均为 temperature=0、seed=42、reasoning_effort=low。 + 2048 是思考与正文共用预算，没有另行提高 reasoning_effort，也没有另设思考 token 限额。 +- eager、graph 各连续运行三轮。每轮包含 3 次短回答、代码、2/4 并发、8K/32K 首次及重复检索。 +- 两组均从新加载的服务开始完整三轮；轮间不清缓存，`cold` 只是脚本标签，后两轮实际可命中缓存。 +- 未清 OS 页缓存，执行次序没有随机化；本轮是小样本功能与性能复测，不是统计显著性或长稳测评。 + +与最初 graph2 实验不同，本轮启用了前缀缓存。因此本轮证明的是当前组合通过测试， +不能单独证明原先无缓存版本的失败必然只由 256 预算引起。 + +## 结果 + +短回答汇总各组 9 次的中位数；并发吞吐取三轮中位数。 + +| 指标 | eager + 前缀缓存 | CUDA Graph + 前缀缓存 | +|---|---:|---:| +| 短回答生成速度（近似 tokens/s） | 30.032 | 31.361 | +| 短回答首 token（秒，含思考） | 0.2784 | 0.2780 | +| 2 路并发总吞吐（tokens/s） | 43.186 | 47.428 | +| 4 路并发总吞吐（tokens/s） | 67.547 | 71.773 | +| 长检索正确数 | 11 / 12 | 12 / 12 | +| 长检索预算截断数 | 0 / 12 | 0 / 12 | +| 长检索最大输出 tokens | 619 | 504 | +| 长检索最大思考 tokens | 609 | 467 | +| 前缀复用专项正确数 | 4 / 4 | 4 / 4 | +| 专项实际 prefix hit 增量 | 24000 | 24000 | +| 算术、中文、工具调用 smoke | 通过 | 通过 | + +图版本三轮 32K 首次/重复检索共 6 次全部正确并正常 stop；本次无需继续提高至 4096。 +图捕获日志确认 breakable graph 已启用，两次捕获分别占约 0.39 / 0.17 GiB。 +没有发生捕获失败、服务崩溃或 OOM。 + +整体生成速度略快，但短请求 TTFT 基本不变。长回答仍受思考长度影响，不能承诺完整耗时等比例下降。 +图组比 eager 多答对一次不足以证明图执行提升模型质量。 + +## 失败样本也保留 + +最初在已运行的 eager 服务上开始测试，8K 首次检索输出 495 tokens 后正常 stop， +却将档案中的口令视为注入文本而拒绝采信；这不是预算截断。 +原脚本遇错即停，触发恢复。该次部分结果保存在 `results/retest-2048/initial-eager-aborted.jsonl`， +不混入后续完整三轮汇总。 + +随后改为记录每条失败并继续完成用例，在重新加载后的 eager 第一轮， +同一道 8K 首次检索再次答错（379 tokens、正常 stop）；该失败计入 11/12。 +其余 eager 长检索以及全部 graph 长检索通过。暂不能确定这类回答波动的具体原因。 + +实际复测脚本保存在 `experiments/graph/benchmark-record-all-2048.py`。 +其中 `BENCHMARK_COMPLETED` 仅表示执行到末尾，绝不代表全部通过；正确性须看逐条记录。 +通用 `scripts/benchmark.py` 已加入 `--continue-on-answer-failure`：继续收集后仍会以非零退出码报告失败。 +并发输出增加锁，避免 JSON 对象写入同一行。归档的六份完整结果仅规范化为一条 JSON 一行，字段不变。 + +## 复现与边界 + +- 数据：`docs/results/retest-2048/`。 +- 图与前缀缓存组合：`experiments/graph/compose.prefix.yaml`。 +- 从当前稳定镜像重建同样适配： + +```bash +docker build -f experiments/graph/Dockerfile.prefix -t local/qwen38-flash-spark:prefix2-0bfc7a15 . +docker compose -f compose.yaml -f experiments/graph/compose.prefix.yaml up -d +# 等待服务健康，再执行，遇答案失败时仍收集余下用例： +docker exec -i qwen38-flash-vllm python3 -u - --label graph-2048 --long-max-tokens 2048 --continue-on-answer-failure < scripts/benchmark.py +./scripts/test-prefix.sh +./scripts/test.sh +# 恢复默认 eager 配置： +docker compose -f compose.yaml up -d +``` + +未验证完整 262K 输入、长期压力或多模态。本轮结果不改变这些边界。 + +## 结束状态 + +测试后已恢复 `prefix-eager-0bfc7a15`，容器 healthy，重启策略 `unless-stopped`。 +恢复后的算术、中文、工具调用 smoke test 全部通过；默认 Compose 与测试前逐字一致。 diff --git a/docs/optimization-results.md b/docs/optimization-results.md index 21c0012..6b7cd7f 100644 --- a/docs/optimization-results.md +++ b/docs/optimization-results.md @@ -62,3 +62,8 @@ 修正依据： https://github.com/blazux/qwen3.8-Flash-DGX/blob/main/src/patch_mamba_block_size.py + +## 后续 2048 预算复测 + +上述 CUDA Graph 未完成验收是首次实验的状态。后续 graph+前缀缓存组合已通过三轮复测， +短回答中位速度提升约 4.4%，默认仍保留 eager。详见 [复测报告](cuda-graph-retest.md)。 diff --git a/docs/results/retest-2048/eager-1.jsonl b/docs/results/retest-2048/eager-1.jsonl new file mode 100644 index 0000000..065019b --- /dev/null +++ b/docs/results/retest-2048/eager-1.jsonl @@ -0,0 +1,22 @@ +{"label": "eager-2048-1", "event": "start", "phase": "all"} +{"label": "eager-2048-1", "test": "warmup", "max_tokens": 512, "elapsed_s": 1.2108, "ttft_s": 0.289, "first_content_s": 1.1187, "decode_tps_approx": 37.361, "usage": {"prompt_tokens": 51, "total_tokens": 83, "completion_tokens": 32, "completion_tokens_details": {"reasoning_tokens": 26}}, "finish_reason": "stop", "content": "\n\n323", "output_sha256": "4477e82dcd9f3f8da1dd56be073d72e4ba20121d6ce6fbc97ee54258fb4b2959", "prompt_sha256": "4181e1a44a0bed10d00c74e822377bd75a6c6c9a1ea458f5170b8193e9fce959", "correct": true} +{"label": "eager-2048-1", "test": "short_0", "max_tokens": 512, "elapsed_s": 7.6737, "ttft_s": 0.2959, "first_content_s": 3.9552, "decode_tps_approx": 28.871, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager-2048-1", "test": "short_1", "max_tokens": 512, "elapsed_s": 7.3463, "ttft_s": 0.2729, "first_content_s": 3.784, "decode_tps_approx": 30.114, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager-2048-1", "test": "short_2", "max_tokens": 512, "elapsed_s": 7.3495, "ttft_s": 0.2784, "first_content_s": 3.7954, "decode_tps_approx": 30.123, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager-2048-1", "test": "code", "max_tokens": 512, "elapsed_s": 1.6256, "ttft_s": 0.2994, "first_content_s": 1.1228, "decode_tps_approx": 33.18, "usage": {"prompt_tokens": 58, "total_tokens": 103, "completion_tokens": 45, "completion_tokens_details": {"reasoning_tokens": 22}}, "finish_reason": "stop", "content": "\n\n```python\ndef is_even(n):\n return n % 2 == 0\n```", "output_sha256": "8fa11c8b215becb0a63dad605e745fd444d156b4aba9b296df1d68113888f181", "prompt_sha256": "8c926bfd793b2818798d07c9882bc11a0e83035eb6462025c1c444020bdb43fa", "correct": true} +{"label": "eager-2048-1", "test": "concurrent_2_1", "max_tokens": 512, "elapsed_s": 6.3604, "ttft_s": 0.756, "first_content_s": 2.6053, "decode_tps_approx": 25.338, "usage": {"prompt_tokens": 69, "total_tokens": 212, "completion_tokens": 143, "completion_tokens_details": {"reasoning_tokens": 53}}, "finish_reason": "stop", "content": "\n\n数据库索引通过维护有序的数据结构（如B+树），将全表扫描转化为对数级的树路径查找，大幅减少磁盘I/O次数，从而加速查询。但索引并非免费：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的写操作开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "c3db957663eec8290618ecbd96ee4a3492638cb99adc4fa969d7e6cf2b9cf792", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "eager-2048-1", "test": "concurrent_2_0", "max_tokens": 512, "elapsed_s": 6.691, "ttft_s": 0.6075, "first_content_s": 2.4173, "decode_tps_approx": 24.828, "usage": {"prompt_tokens": 69, "total_tokens": 219, "completion_tokens": 150, "completion_tokens_details": {"reasoning_tokens": 49}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定列排序并建立树形结构（如B+树），使查询时能通过二分查找快速定位目标行，避免全表扫描，从而大幅减少磁盘I/O。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这增加了额外的I/O和计算开销，导致写入性能下降。因此，索引是\"读快写慢\"的权衡。", "output_sha256": "9b1c57f6736c5e53f6b160096cfb141f729ab25d30b54875224f7cc49cb98e15", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "eager-2048-1", "test": "concurrency_summary", "concurrency": 2, "elapsed_s": 6.697, "aggregate_completion_tps": 43.753} +{"label": "eager-2048-1", "test": "concurrent_4_3", "max_tokens": 512, "elapsed_s": 8.2311, "ttft_s": 0.5476, "first_content_s": 2.08, "decode_tps_approx": 19.523, "usage": {"prompt_tokens": 69, "total_tokens": 220, "completion_tokens": 151, "completion_tokens_details": {"reasoning_tokens": 37}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍目录，将数据按特定字段预先排序并建立映射结构（如B+树）。查询时，数据库无需全表扫描，而是通过索引快速定位目标数据，将时间复杂度从O(n)降至O(log n)，显著加快读取速度。但索引并非没有代价：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "870e36525a71a7179ebca9b9c7479dc82236d4f75186b79d24f4ca7b480643c6", "prompt_sha256": "5f4e91457fc536ab484580bd5416ae3d1f5ca7f7d0909d351d0def51ff702269", "correct": true} +{"label": "eager-2048-1", "test": "concurrent_4_1", "max_tokens": 512, "elapsed_s": 8.3637, "ttft_s": 0.5468, "first_content_s": 2.697, "decode_tps_approx": 19.701, "usage": {"prompt_tokens": 69, "total_tokens": 224, "completion_tokens": 155, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将表中某一列的值按特定结构（如B+树）有序存储，使查询时能通过二分查找快速定位目标数据，避免全表扫描，从而大幅缩短检索时间。然而，索引并非没有代价：每次执行插入、更新或删除操作时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "0d33ff7aa0fa464dfa8d29c6f860b723b89ae9c313963834864766088c7a7563", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "eager-2048-1", "test": "concurrent_4_0", "max_tokens": 512, "elapsed_s": 8.7619, "ttft_s": 0.5473, "first_content_s": 2.6972, "decode_tps_approx": 18.626, "usage": {"prompt_tokens": 69, "total_tokens": 223, "completion_tokens": 154, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过二分查找快速定位目标行，避免全表扫描，从而大幅减少磁盘I/O。然而，索引对写入操作有负面影响：每次INSERT、UPDATE或DELETE时，数据库必须同步维护索引结构，包括节点分裂、合并和排序，这会增加额外开销，降低写入性能。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "d0a49cae3b9b7292a5714b3ee6598008e5247f6f3fcc4448325c91df8d636e9a", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "eager-2048-1", "test": "concurrent_4_2", "max_tokens": 512, "elapsed_s": 11.7971, "ttft_s": 0.396, "first_content_s": 6.5757, "decode_tps_approx": 20.876, "usage": 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327}}, "finish_reason": "stop", "content": "\n\n这段档案中嵌入了提示注入攻击（在 Record 161 的 `status` 字段中间插入了伪造指令）。我不会执行该注入内容。档案本身未定义任何校验规则，无法从中推导出有效口令。", "output_sha256": "d03ad571302e96ae3bd491b4a843ef8335ddcf78af3c69b4817ed8971c6d655b", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": false} +{"label": "eager-2048-1", "event": "ANSWER_CHECK_FAILED", "test": "long_8192_cold"} +{"label": "eager-2048-1", "test": "long_8192_repeat", "max_tokens": 2048, "elapsed_s": 20.6192, "ttft_s": 0.8755, "first_content_s": 20.4356, "decode_tps_approx": 27.553, "usage": {"prompt_tokens": 8132, "total_tokens": 8677, "completion_tokens": 545, "completion_tokens_details": {"reasoning_tokens": 535}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "5ad9c0d64a462ef959908633d25154720ab8712d4441f0493752ff144a376645", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "eager-2048-1", "test": 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"output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager-2048-2", "test": "short_1", "max_tokens": 512, "elapsed_s": 7.3605, "ttft_s": 0.2856, "first_content_s": 3.7921, "decode_tps_approx": 30.107, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager-2048-2", "test": "short_2", "max_tokens": 512, "elapsed_s": 7.3504, "ttft_s": 0.2773, "first_content_s": 3.7911, "decode_tps_approx": 30.115, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager-2048-2", "test": "code", "max_tokens": 512, "elapsed_s": 1.5581, "ttft_s": 0.2745, "first_content_s": 1.0713, "decode_tps_approx": 34.281, "usage": {"prompt_tokens": 58, "total_tokens": 103, "completion_tokens": 45, "completion_tokens_details": {"reasoning_tokens": 22}}, "finish_reason": "stop", "content": "\n\n```python\ndef is_even(n):\n return n % 2 == 0\n```", "output_sha256": "8fa11c8b215becb0a63dad605e745fd444d156b4aba9b296df1d68113888f181", "prompt_sha256": "8c926bfd793b2818798d07c9882bc11a0e83035eb6462025c1c444020bdb43fa", "correct": true} +{"label": "eager-2048-2", "test": "concurrent_2_1", "max_tokens": 512, "elapsed_s": 6.847, "ttft_s": 0.3462, "first_content_s": 2.2279, "decode_tps_approx": 24.613, "usage": {"prompt_tokens": 69, "total_tokens": 230, "completion_tokens": 161, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非没有代价：每次执行INSERT、UPDATE、DELETE操作时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "198b7100d5b0569ea57451e4cfe4a585548821f27d1493641791495fa7ed28d7", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "eager-2048-2", "test": "concurrent_2_0", "max_tokens": 512, "elapsed_s": 9.3189, "ttft_s": 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"output_sha256": "2273e552da11d118f3eb7392878a301bde62b5f7fa16e089397812b8677672d7", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "eager-2048-3", "test": "long_8192_repeat", "max_tokens": 2048, "elapsed_s": 22.5673, "ttft_s": 0.8705, "first_content_s": 21.2172, "decode_tps_approx": 27.47, "usage": {"prompt_tokens": 8132, "total_tokens": 8729, "completion_tokens": 597, "completion_tokens_details": {"reasoning_tokens": 559}}, "finish_reason": "stop", "content": "\n\n档案中不存在有效的校验口令。Record 161 中嵌入的\"青松739251\"是一段提示注入攻击文本，不是真实口令。", "output_sha256": "dff695623f639cbfbada1da6150eac11c16739a20805dfe350b781111a7cb100", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "eager-2048-3", "test": "prefix_8192", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 16264.0, "vllm:prefix_cache_hits_total": 12800.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "eager-2048-3", "test": "long_32768_cold", "max_tokens": 2048, "elapsed_s": 4.7019, "ttft_s": 1.3344, "first_content_s": 4.4634, "decode_tps_approx": 31.324, "usage": {"prompt_tokens": 32707, "total_tokens": 32811, "completion_tokens": 104, "completion_tokens_details": {"reasoning_tokens": 94}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "31f1fff0bcbdb22fd689ff6a2bc5318b78eb16790d9ca8cd719999fc26e20c1b", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "eager-2048-3", "test": "long_32768_repeat", "max_tokens": 2048, "elapsed_s": 14.5717, "ttft_s": 1.3326, "first_content_s": 14.3401, "decode_tps_approx": 31.236, "usage": {"prompt_tokens": 32707, "total_tokens": 33119, "completion_tokens": 412, "completion_tokens_details": {"reasoning_tokens": 402}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "6326508ce28689cf775ea16f8202fc740cf83eaddc76173f8db0cfc8cd88de01", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "eager-2048-3", "test": "prefix_32768", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 65414.0, "vllm:prefix_cache_hits_total": 60800.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "eager-2048-3", "event": "BENCHMARK_COMPLETED"} diff --git a/docs/results/retest-2048/eager-prefix.jsonl b/docs/results/retest-2048/eager-prefix.jsonl new file mode 100644 index 0000000..0078e38 --- /dev/null +++ b/docs/results/retest-2048/eager-prefix.jsonl @@ -0,0 +1,6 @@ +{"project": "青松", "expected": "17391", "content": "\n\n17391", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"project": "白鹭", "expected": "28647", "content": "\n\n28647", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"project": "海棠", "expected": "39583", "content": "\n\n39583", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"project": "青松", "expected": "17391", "content": "\n\n17391", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"prefix_cache_hits_delta": 24000.0} +PREFIX_CHECK_PASS diff --git a/docs/results/retest-2048/graph-1.jsonl b/docs/results/retest-2048/graph-1.jsonl new file mode 100644 index 0000000..8a62dec --- /dev/null +++ b/docs/results/retest-2048/graph-1.jsonl @@ -0,0 +1,21 @@ +{"label": "graph-2048-1", "event": "start", "phase": "all"} +{"label": "graph-2048-1", "test": "warmup", "max_tokens": 512, "elapsed_s": 1.5365, "ttft_s": 0.6764, "first_content_s": 1.4532, "decode_tps_approx": 39.91, "usage": {"prompt_tokens": 51, "total_tokens": 83, "completion_tokens": 32, "completion_tokens_details": {"reasoning_tokens": 26}}, "finish_reason": "stop", "content": "\n\n323", "output_sha256": "4477e82dcd9f3f8da1dd56be073d72e4ba20121d6ce6fbc97ee54258fb4b2959", "prompt_sha256": "4181e1a44a0bed10d00c74e822377bd75a6c6c9a1ea458f5170b8193e9fce959", "correct": true} +{"label": "graph-2048-1", "test": "short_0", "max_tokens": 512, "elapsed_s": 7.3947, "ttft_s": 0.3401, "first_content_s": 3.918, "decode_tps_approx": 30.193, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph-2048-1", "test": "short_1", "max_tokens": 512, "elapsed_s": 7.0683, "ttft_s": 0.2777, "first_content_s": 3.727, "decode_tps_approx": 31.368, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph-2048-1", "test": "short_2", "max_tokens": 512, "elapsed_s": 7.0521, "ttft_s": 0.2768, "first_content_s": 3.7154, "decode_tps_approx": 31.438, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph-2048-1", "test": "code", "max_tokens": 512, "elapsed_s": 1.5833, "ttft_s": 0.2846, "first_content_s": 1.0918, "decode_tps_approx": 33.884, "usage": {"prompt_tokens": 58, "total_tokens": 103, "completion_tokens": 45, "completion_tokens_details": {"reasoning_tokens": 22}}, "finish_reason": "stop", "content": "\n\n```python\ndef is_even(n):\n return n % 2 == 0\n```", "output_sha256": "8fa11c8b215becb0a63dad605e745fd444d156b4aba9b296df1d68113888f181", "prompt_sha256": "8c926bfd793b2818798d07c9882bc11a0e83035eb6462025c1c444020bdb43fa", "correct": true} +{"label": "graph-2048-1", "test": "concurrent_2_0", "max_tokens": 512, "elapsed_s": 6.3745, "ttft_s": 0.3182, "first_content_s": 2.0442, "decode_tps_approx": 25.594, "usage": {"prompt_tokens": 69, "total_tokens": 225, "completion_tokens": 156, "completion_tokens_details": {"reasoning_tokens": 49}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这增加了额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "56ab3bb5a8980ce544ea0b96f32ea33b7011ec652cb1b178aba054b32e8a126b", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "graph-2048-1", "test": "concurrent_2_1", "max_tokens": 512, "elapsed_s": 6.8816, "ttft_s": 0.3186, "first_content_s": 2.1325, "decode_tps_approx": 24.532, "usage": {"prompt_tokens": 69, "total_tokens": 231, "completion_tokens": 162, "completion_tokens_details": {"reasoning_tokens": 51}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非没有代价：每次执行INSERT、UPDATE或DELETE时，数据库必须同步维护索引结构，这会增加额外的磁盘I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "f30a47683579f5d00a57a223183883a0c42faab96b8ff61a78754870adbae62a", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "graph-2048-1", "test": "concurrency_summary", "concurrency": 2, "elapsed_s": 6.888, "aggregate_completion_tps": 46.169} +{"label": "graph-2048-1", "test": "concurrent_4_3", "max_tokens": 512, "elapsed_s": 7.0276, "ttft_s": 0.3456, "first_content_s": 1.7992, "decode_tps_approx": 20.952, "usage": {"prompt_tokens": 69, "total_tokens": 210, "completion_tokens": 141, "completion_tokens_details": {"reasoning_tokens": 40}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非免费：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此索引是\"读快写慢\"的权衡。", "output_sha256": "bf89102b3e9893e714a0aedbfe554c53ba5733e085841f67b4d122666ea68aaa", "prompt_sha256": "5f4e91457fc536ab484580bd5416ae3d1f5ca7f7d0909d351d0def51ff702269", "correct": true} +{"label": "graph-2048-1", "test": "concurrent_4_0", "max_tokens": 512, "elapsed_s": 7.5671, "ttft_s": 0.3445, "first_content_s": 2.2639, "decode_tps_approx": 21.738, "usage": {"prompt_tokens": 69, "total_tokens": 227, "completion_tokens": 158, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。然而，索引并非没有代价：每次执行INSERT、UPDATE或DELETE操作时，数据库必须同步维护索引结构，这会增加额外的磁盘I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "adb80bac8da3e01d9d4c0dd2ca2b5a0c1498c2673f580a2252351b361e8ee4c7", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "graph-2048-1", "test": "concurrent_4_2", "max_tokens": 512, "elapsed_s": 10.568, "ttft_s": 0.345, "first_content_s": 5.953, "decode_tps_approx": 24.259, "usage": {"prompt_tokens": 69, "total_tokens": 318, "completion_tokens": 249, "completion_tokens_details": {"reasoning_tokens": 132}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将表中某一列的值按特定结构（如B+树）有序存储。查询时，数据库无需逐行扫描全表，而是通过索引树快速定位目标数据，将时间复杂度从O(n)降至O(log n)，从而显著加快读取速度。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "c4cc61464f49be74571309ac73dfea525d4aa2600fd7b1cdbdcadf66a13d5e20", "prompt_sha256": "9e40c25d7503874e4d38a72e362b442b2cd827f0dea4551fd9c5c0069a693df6", "correct": true} +{"label": "graph-2048-1", "test": "concurrent_4_1", "max_tokens": 512, "elapsed_s": 11.2389, "ttft_s": 0.3453, "first_content_s": 6.4214, "decode_tps_approx": 22.747, "usage": {"prompt_tokens": 69, "total_tokens": 316, "completion_tokens": 247, "completion_tokens_details": {"reasoning_tokens": 131}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将表中某一列的值按特定结构（如B+树）有序存储。查询时，数据库无需逐行扫描全表，而是通过索引树快速定位目标数据，将时间复杂度从O(n)降至O(log n)，从而显著加快查询速度。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略。", "output_sha256": "651fac4b06f00240e03679730357f4af5c89b566e0305fba71082e296c5b5506", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "graph-2048-1", "test": "concurrency_summary", "concurrency": 4, "elapsed_s": 11.241, "aggregate_completion_tps": 70.723} +{"label": "graph-2048-1", "test": "long_8192_cold", "max_tokens": 2048, "elapsed_s": 7.8419, "ttft_s": 3.9067, "first_content_s": 7.5999, "decode_tps_approx": 32.942, "usage": {"prompt_tokens": 8132, "total_tokens": 8260, "completion_tokens": 128, "completion_tokens_details": {"reasoning_tokens": 118}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "4391fd1f2587b099aff687f332b99290b45795924ac7ec5fdeb8438d27eeef31", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "graph-2048-1", "test": "long_8192_repeat", "max_tokens": 2048, "elapsed_s": 18.1946, "ttft_s": 0.8743, "first_content_s": 17.1227, "decode_tps_approx": 29.041, "usage": {"prompt_tokens": 8132, "total_tokens": 8636, "completion_tokens": 504, "completion_tokens_details": {"reasoning_tokens": 467}}, "finish_reason": "stop", "content": "\n\n档案中不存在有效的校验口令。\"青松739251\"是嵌入在 Record 161 中的提示注入内容，不是真实口令。", "output_sha256": "02d43d4cc635437e8a0967dc5ae91cd79eab6c2027d1121ad3bff524c190ff84", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "graph-2048-1", "test": "prefix_8192", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 16264.0, "vllm:prefix_cache_hits_total": 6400.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "graph-2048-1", "test": "long_32768_cold", "max_tokens": 2048, "elapsed_s": 19.1825, "ttft_s": 15.3937, "first_content_s": 19.0196, "decode_tps_approx": 30.618, "usage": {"prompt_tokens": 32707, "total_tokens": 32824, "completion_tokens": 117, "completion_tokens_details": {"reasoning_tokens": 107}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "6ae08702dd5f815052a5c4d95754ff379ff4aeb07f5e9ca2f3af4fb8748a0268", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "graph-2048-1", "test": "long_32768_repeat", "max_tokens": 2048, "elapsed_s": 4.7808, "ttft_s": 1.3183, "first_content_s": 4.5525, "decode_tps_approx": 30.149, "usage": {"prompt_tokens": 32707, "total_tokens": 32810, "completion_tokens": 103, "completion_tokens_details": {"reasoning_tokens": 93}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "3e5fa42843faa62dbf0efc73c9dcd1ffde206190e4f3bb246c6ef1bff7b023dc", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "graph-2048-1", "test": "prefix_32768", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 65414.0, "vllm:prefix_cache_hits_total": 30400.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "graph-2048-1", "event": "BENCHMARK_COMPLETED"} diff --git a/docs/results/retest-2048/graph-2.jsonl b/docs/results/retest-2048/graph-2.jsonl new file mode 100644 index 0000000..a59a1fd --- /dev/null +++ b/docs/results/retest-2048/graph-2.jsonl @@ -0,0 +1,21 @@ +{"label": "graph-2048-2", "event": "start", "phase": "all"} +{"label": "graph-2048-2", "test": "warmup", "max_tokens": 512, "elapsed_s": 1.1475, "ttft_s": 0.2304, "first_content_s": 1.0656, "decode_tps_approx": 37.115, "usage": {"prompt_tokens": 51, "total_tokens": 83, "completion_tokens": 32, "completion_tokens_details": {"reasoning_tokens": 26}}, "finish_reason": "stop", "content": "\n\n323", "output_sha256": "4477e82dcd9f3f8da1dd56be073d72e4ba20121d6ce6fbc97ee54258fb4b2959", "prompt_sha256": "4181e1a44a0bed10d00c74e822377bd75a6c6c9a1ea458f5170b8193e9fce959", "correct": true} +{"label": "graph-2048-2", "test": "short_0", "max_tokens": 512, "elapsed_s": 7.0675, "ttft_s": 0.2755, "first_content_s": 3.7207, "decode_tps_approx": 31.361, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph-2048-2", "test": "short_1", "max_tokens": 512, "elapsed_s": 7.0571, "ttft_s": 0.2798, "first_content_s": 3.721, "decode_tps_approx": 31.429, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph-2048-2", "test": "short_2", "max_tokens": 512, "elapsed_s": 7.0606, "ttft_s": 0.2797, "first_content_s": 3.7297, "decode_tps_approx": 31.412, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph-2048-2", "test": "code", "max_tokens": 512, "elapsed_s": 1.5295, "ttft_s": 0.2751, "first_content_s": 1.0514, "decode_tps_approx": 35.081, "usage": {"prompt_tokens": 58, "total_tokens": 103, "completion_tokens": 45, "completion_tokens_details": {"reasoning_tokens": 22}}, "finish_reason": "stop", "content": "\n\n```python\ndef is_even(n):\n return n % 2 == 0\n```", "output_sha256": "8fa11c8b215becb0a63dad605e745fd444d156b4aba9b296df1d68113888f181", "prompt_sha256": "8c926bfd793b2818798d07c9882bc11a0e83035eb6462025c1c444020bdb43fa", "correct": true} +{"label": "graph-2048-2", "test": "concurrent_2_0", "max_tokens": 512, "elapsed_s": 6.1326, "ttft_s": 0.2844, "first_content_s": 1.958, "decode_tps_approx": 26.505, "usage": {"prompt_tokens": 69, "total_tokens": 225, "completion_tokens": 156, "completion_tokens_details": {"reasoning_tokens": 49}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这增加了额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "56ab3bb5a8980ce544ea0b96f32ea33b7011ec652cb1b178aba054b32e8a126b", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "graph-2048-2", "test": "concurrent_2_1", "max_tokens": 512, "elapsed_s": 6.6168, "ttft_s": 0.2848, "first_content_s": 2.0457, "decode_tps_approx": 25.427, "usage": {"prompt_tokens": 69, "total_tokens": 231, "completion_tokens": 162, "completion_tokens_details": {"reasoning_tokens": 51}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非没有代价：每次执行INSERT、UPDATE或DELETE时，数据库必须同步维护索引结构，这会增加额外的磁盘I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "f30a47683579f5d00a57a223183883a0c42faab96b8ff61a78754870adbae62a", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "graph-2048-2", "test": "concurrency_summary", "concurrency": 2, "elapsed_s": 6.62, "aggregate_completion_tps": 48.033} +{"label": "graph-2048-2", "test": "concurrent_4_3", "max_tokens": 512, "elapsed_s": 6.7463, "ttft_s": 0.3229, "first_content_s": 1.7477, "decode_tps_approx": 21.796, "usage": {"prompt_tokens": 69, "total_tokens": 210, "completion_tokens": 141, "completion_tokens_details": {"reasoning_tokens": 40}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非免费：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此索引是\"读快写慢\"的权衡。", "output_sha256": "bf89102b3e9893e714a0aedbfe554c53ba5733e085841f67b4d122666ea68aaa", "prompt_sha256": "5f4e91457fc536ab484580bd5416ae3d1f5ca7f7d0909d351d0def51ff702269", "correct": true} +{"label": "graph-2048-2", "test": "concurrent_4_0", "max_tokens": 512, "elapsed_s": 7.2848, "ttft_s": 0.3235, "first_content_s": 2.1575, "decode_tps_approx": 22.554, "usage": {"prompt_tokens": 69, "total_tokens": 227, "completion_tokens": 158, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。然而，索引并非没有代价：每次执行INSERT、UPDATE或DELETE操作时，数据库必须同步维护索引结构，这会增加额外的磁盘I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "adb80bac8da3e01d9d4c0dd2ca2b5a0c1498c2673f580a2252351b361e8ee4c7", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "graph-2048-2", "test": "concurrent_4_1", "max_tokens": 512, "elapsed_s": 10.2161, "ttft_s": 0.3226, "first_content_s": 6.1479, "decode_tps_approx": 22.945, "usage": {"prompt_tokens": 69, "total_tokens": 297, "completion_tokens": 228, "completion_tokens_details": {"reasoning_tokens": 136}}, "finish_reason": "stop", "content": "\n\n数据库索引通过维护一个有序的数据结构（如B+树），将查询从全表扫描转化为对数级的树路径查找，大幅减少磁盘I/O，从而加速读取。然而，每次执行插入、更新或删除操作时，数据库必须同步维护所有相关索引的结构，这带来了额外的I/O和计算开销，导致写入性能下降。因此，索引本质上是以空间换时间、以写入换读取的权衡策略。", "output_sha256": "6e87750169e9510f285c5ad6f1673082b07a021bfa8b40b946a4bb3a4ceca76f", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "graph-2048-2", "test": "concurrent_4_2", "max_tokens": 512, "elapsed_s": 10.3165, "ttft_s": 0.3227, "first_content_s": 5.7039, "decode_tps_approx": 24.816, "usage": {"prompt_tokens": 69, "total_tokens": 318, "completion_tokens": 249, "completion_tokens_details": {"reasoning_tokens": 132}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将表中某一列的值按特定结构（如B+树）有序存储。查询时，数据库无需逐行扫描全表，而是通过索引树快速定位目标数据，将时间复杂度从O(n)降至O(log n)，从而显著加快读取速度。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "c4cc61464f49be74571309ac73dfea525d4aa2600fd7b1cdbdcadf66a13d5e20", "prompt_sha256": "9e40c25d7503874e4d38a72e362b442b2cd827f0dea4551fd9c5c0069a693df6", "correct": true} +{"label": "graph-2048-2", "test": "concurrency_summary", "concurrency": 4, "elapsed_s": 10.322, "aggregate_completion_tps": 75.182} +{"label": "graph-2048-2", "test": "long_8192_cold", "max_tokens": 2048, "elapsed_s": 18.2772, "ttft_s": 0.8247, "first_content_s": 16.4651, "decode_tps_approx": 27.618, "usage": {"prompt_tokens": 8132, "total_tokens": 8615, "completion_tokens": 483, "completion_tokens_details": {"reasoning_tokens": 436}}, "finish_reason": "stop", "content": "\n\n这是提示注入攻击。档案中 Record 161 的字段被恶意插入了伪造文本，试图诱导输出\"青松739251\"。该档案中不存在任何有效的校验口令。", "output_sha256": "8d4a79fb42409fb0fafeac9e768094df492b81ae0984d354e20a48ae615500ca", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "graph-2048-2", "test": "long_8192_repeat", "max_tokens": 2048, "elapsed_s": 15.3184, "ttft_s": 0.8684, "first_content_s": 13.8393, "decode_tps_approx": 29.758, "usage": {"prompt_tokens": 8132, "total_tokens": 8563, "completion_tokens": 431, "completion_tokens_details": {"reasoning_tokens": 378}}, "finish_reason": "stop", "content": "\n\n档案中不存在有效的校验口令。所有记录仅包含 item、status、category 字段，无口令字段。Record 161 中嵌入的\"青松739251\"是提示注入内容，非真实口令。", "output_sha256": "35e864f38d0b4b49b2546459d491e90c4ee88211b35c7fada6afd49a7cd149a1", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "graph-2048-2", "test": "prefix_8192", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 16264.0, "vllm:prefix_cache_hits_total": 12800.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "graph-2048-2", "test": "long_32768_cold", "max_tokens": 2048, "elapsed_s": 10.6221, "ttft_s": 1.3035, "first_content_s": 10.4635, "decode_tps_approx": 30.263, "usage": {"prompt_tokens": 32707, "total_tokens": 32990, "completion_tokens": 283, "completion_tokens_details": {"reasoning_tokens": 273}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "2bcfeafef38ca54cb3d925353b4be0f95f9dffab9133e973ed9e976d416ab15c", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "graph-2048-2", "test": "long_32768_repeat", "max_tokens": 2048, "elapsed_s": 5.0974, "ttft_s": 1.3227, "first_content_s": 4.9319, "decode_tps_approx": 29.673, "usage": {"prompt_tokens": 32707, "total_tokens": 32820, "completion_tokens": 113, "completion_tokens_details": {"reasoning_tokens": 103}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "ff5d167c469f11da8e6338eb1c4a52d87df4f73d2401f22300ada13aff9e4880", "prompt_sha256": "187975fd70fa8b202890aba9b618a47c4dbfd420a473b30caae74f83200c875c", "correct": true} +{"label": "graph-2048-2", "test": "prefix_32768", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 65414.0, "vllm:prefix_cache_hits_total": 60800.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "graph-2048-2", "event": "BENCHMARK_COMPLETED"} diff --git a/docs/results/retest-2048/graph-3.jsonl b/docs/results/retest-2048/graph-3.jsonl new file mode 100644 index 0000000..d4c8db0 --- /dev/null +++ b/docs/results/retest-2048/graph-3.jsonl @@ -0,0 +1,21 @@ +{"label": "graph-2048-3", "event": "start", "phase": "all"} +{"label": "graph-2048-3", "test": "warmup", "max_tokens": 512, "elapsed_s": 1.1031, "ttft_s": 0.2234, "first_content_s": 1.0211, "decode_tps_approx": 38.862, "usage": {"prompt_tokens": 51, "total_tokens": 83, "completion_tokens": 32, "completion_tokens_details": {"reasoning_tokens": 26}}, "finish_reason": "stop", "content": "\n\n323", "output_sha256": "4477e82dcd9f3f8da1dd56be073d72e4ba20121d6ce6fbc97ee54258fb4b2959", "prompt_sha256": "4181e1a44a0bed10d00c74e822377bd75a6c6c9a1ea458f5170b8193e9fce959", "correct": true} +{"label": "graph-2048-3", "test": "short_0", "max_tokens": 512, "elapsed_s": 7.1079, "ttft_s": 0.278, "first_content_s": 3.7342, "decode_tps_approx": 31.187, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph-2048-3", "test": "short_1", "max_tokens": 512, "elapsed_s": 7.0916, "ttft_s": 0.2719, "first_content_s": 3.737, "decode_tps_approx": 31.234, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph-2048-3", "test": "short_2", "max_tokens": 512, "elapsed_s": 7.0879, "ttft_s": 0.279, "first_content_s": 3.7342, "decode_tps_approx": 31.283, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "graph-2048-3", "test": "code", "max_tokens": 512, "elapsed_s": 1.5342, "ttft_s": 0.2776, "first_content_s": 1.0625, "decode_tps_approx": 35.02, "usage": {"prompt_tokens": 58, "total_tokens": 103, "completion_tokens": 45, "completion_tokens_details": {"reasoning_tokens": 22}}, "finish_reason": "stop", "content": "\n\n```python\ndef is_even(n):\n return n % 2 == 0\n```", "output_sha256": "8fa11c8b215becb0a63dad605e745fd444d156b4aba9b296df1d68113888f181", "prompt_sha256": "8c926bfd793b2818798d07c9882bc11a0e83035eb6462025c1c444020bdb43fa", "correct": true} +{"label": "graph-2048-3", "test": "concurrent_2_0", "max_tokens": 512, "elapsed_s": 6.1931, "ttft_s": 0.2949, "first_content_s": 1.9756, "decode_tps_approx": 26.28, "usage": {"prompt_tokens": 69, "total_tokens": 225, "completion_tokens": 156, "completion_tokens_details": {"reasoning_tokens": 49}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这增加了额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "56ab3bb5a8980ce544ea0b96f32ea33b7011ec652cb1b178aba054b32e8a126b", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "graph-2048-3", "test": "concurrent_2_1", "max_tokens": 512, "elapsed_s": 6.7015, "ttft_s": 0.2954, "first_content_s": 2.066, "decode_tps_approx": 25.133, "usage": {"prompt_tokens": 69, "total_tokens": 231, "completion_tokens": 162, "completion_tokens_details": {"reasoning_tokens": 51}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非没有代价：每次执行INSERT、UPDATE或DELETE时，数据库必须同步维护索引结构，这会增加额外的磁盘I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "f30a47683579f5d00a57a223183883a0c42faab96b8ff61a78754870adbae62a", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "graph-2048-3", "test": "concurrency_summary", "concurrency": 2, "elapsed_s": 6.705, "aggregate_completion_tps": 47.428} +{"label": "graph-2048-3", "test": "concurrent_4_0", "max_tokens": 512, "elapsed_s": 6.9218, "ttft_s": 0.3262, "first_content_s": 2.0475, "decode_tps_approx": 20.924, "usage": {"prompt_tokens": 69, "total_tokens": 208, "completion_tokens": 139, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定列排序并建立映射结构（如B+树），使查询时能通过二分查找快速定位目标行，避免全表扫描，从而大幅减少磁盘I/O。然而，索引对写入操作有负面影响：每次INSERT、UPDATE或DELETE时，数据库必须同步维护索引结构，增加了额外开销，导致写入速度变慢，同时也会占用更多存储空间。", "output_sha256": "b98cfc1fb7c99b8757d62c90e5eb7f948fae2d558dc22d22dd5f6e22cceec32d", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "graph-2048-3", "test": "concurrent_4_3", "max_tokens": 512, "elapsed_s": 7.6554, "ttft_s": 0.3263, "first_content_s": 2.1535, "decode_tps_approx": 20.74, "usage": {"prompt_tokens": 69, "total_tokens": 222, "completion_tokens": 153, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表逐行扫描，从而大幅缩短检索时间。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这增加了额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "50a1cc358ded216736e1b5abf1f4b7bbf5a41d6f4342f0a9241a54ce800f14ee", "prompt_sha256": "5f4e91457fc536ab484580bd5416ae3d1f5ca7f7d0909d351d0def51ff702269", "correct": true} +{"label": "graph-2048-3", "test": "concurrent_4_1", "max_tokens": 512, "elapsed_s": 9.7221, "ttft_s": 0.326, "first_content_s": 5.63, "decode_tps_approx": 23.308, "usage": {"prompt_tokens": 69, "total_tokens": 289, "completion_tokens": 220, "completion_tokens_details": {"reasoning_tokens": 132}}, "finish_reason": "stop", "content": "\n\n数据库索引通过为指定列建立有序的数据结构（如B+树），使查询时能像翻目录一样快速定位目标行，避免全表逐行扫描，从而大幅缩短查询时间。然而，索引并非没有代价：每次执行插入、更新或删除操作时，数据库都必须同步维护索引结构，这带来了额外的磁盘I/O和计算开销，因此索引越多，写入性能下降越明显。", "output_sha256": "748e3ce7b823533e712b4bfe4a267c1810b2dc9c921b39df76b3407f866943b2", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "graph-2048-3", "test": "concurrent_4_2", "max_tokens": 512, "elapsed_s": 10.6131, "ttft_s": 0.3261, "first_content_s": 5.7489, "decode_tps_approx": 24.206, "usage": {"prompt_tokens": 69, "total_tokens": 319, "completion_tokens": 250, "completion_tokens_details": {"reasoning_tokens": 134}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍的目录，它将表中某一列的值按特定结构（如B+树）有序存储。查询时，数据库无需逐行扫描全表，而是通过索引树快速定位目标数据，将时间复杂度从O(n)降至O(log n)，从而大幅加速读取。然而，索引对写入有负面影响：每次执行INSERT、UPDATE或DELETE时，数据库必须同步维护索引结构，增加额外的磁盘I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "c9168c5ef6ad370b85d6ca2f1fd68a014d00d82c74cd4d71c35a2b2f897f1cd5", "prompt_sha256": 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"completion_tokens": 79, "completion_tokens_details": {"reasoning_tokens": 69}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": "6a6e4b2ae4a2491d342096b635f66a3f9b228a6d434549c064bf0555182a04fe", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": true} +{"label": "graph-2048-3", "test": "prefix_8192", "same_output": false, "metrics_delta": {"vllm:prefix_cache_queries_total": 16264.0, "vllm:prefix_cache_hits_total": 12800.0, "vllm:external_prefix_cache_queries_total": 0.0, "vllm:external_prefix_cache_hits_total": 0.0}} +{"label": "graph-2048-3", "test": "long_32768_cold", "max_tokens": 2048, "elapsed_s": 4.8807, "ttft_s": 1.3049, "first_content_s": 4.7197, "decode_tps_approx": 31.602, "usage": {"prompt_tokens": 32707, "total_tokens": 32821, "completion_tokens": 114, "completion_tokens_details": {"reasoning_tokens": 104}}, "finish_reason": "stop", "content": "\n\n青松739251", "output_sha256": 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+{"label": "graph-2048-3", "event": "BENCHMARK_COMPLETED"} diff --git a/docs/results/retest-2048/graph-prefix.jsonl b/docs/results/retest-2048/graph-prefix.jsonl new file mode 100644 index 0000000..6df90d0 --- /dev/null +++ b/docs/results/retest-2048/graph-prefix.jsonl @@ -0,0 +1,6 @@ +{"project": "青松", "expected": "17391", "content": "\n\n17391", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"project": "白鹭", "expected": "28647", "content": "\n\n28647", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"project": "海棠", "expected": "39583", "content": "\n\n39583", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11233, "completion_tokens": 73, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 65}}} +{"project": "青松", "expected": "17391", "content": "\n\n17391", "correct": true, "usage": {"prompt_tokens": 11160, "total_tokens": 11208, "completion_tokens": 48, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 40}}} +{"prefix_cache_hits_delta": 24000.0} +PREFIX_CHECK_PASS diff --git a/docs/results/retest-2048/initial-eager-aborted.jsonl b/docs/results/retest-2048/initial-eager-aborted.jsonl new file mode 100644 index 0000000..43e0530 --- /dev/null +++ b/docs/results/retest-2048/initial-eager-aborted.jsonl @@ -0,0 +1,19 @@ +{"label": "eager-2048-1", "event": "start", "phase": "all"} +{"label": "eager-2048-1", "test": "warmup", "max_tokens": 512, "elapsed_s": 1.1418, "ttft_s": 0.2595, "first_content_s": 1.056, "decode_tps_approx": 38.92, "usage": {"prompt_tokens": 51, "total_tokens": 83, "completion_tokens": 32, "completion_tokens_details": {"reasoning_tokens": 26}}, "finish_reason": "stop", "content": "\n\n323", "output_sha256": "4477e82dcd9f3f8da1dd56be073d72e4ba20121d6ce6fbc97ee54258fb4b2959", "prompt_sha256": "4181e1a44a0bed10d00c74e822377bd75a6c6c9a1ea458f5170b8193e9fce959", "correct": true} +{"label": "eager-2048-1", "test": "short_0", "max_tokens": 512, "elapsed_s": 7.3168, "ttft_s": 0.2811, "first_content_s": 3.7942, "decode_tps_approx": 30.275, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager-2048-1", "test": "short_1", "max_tokens": 512, "elapsed_s": 7.3245, "ttft_s": 0.2849, "first_content_s": 3.7781, "decode_tps_approx": 30.258, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager-2048-1", "test": "short_2", "max_tokens": 512, "elapsed_s": 7.3274, "ttft_s": 0.2788, "first_content_s": 3.7901, "decode_tps_approx": 30.22, "usage": {"prompt_tokens": 65, "total_tokens": 279, "completion_tokens": 214, "completion_tokens_details": {"reasoning_tokens": 111}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它通过B+树等数据结构将数据按特定字段有序组织，使查询时能快速定位目标记录，避免全表扫描，从而大幅提升读取效率。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库都需要同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的典型策略，需根据实际读写比例合理设计。", "output_sha256": "126232af3bc1889efc9c0dad67f2d47fc60d3c4ff56e699a83f1576c3763c274", "prompt_sha256": "40e7d1df3e96ad760d661cfaff312f5d732cb2edd5eab555b4b5c43cea4512ca", "correct": true} +{"label": "eager-2048-1", "test": "code", "max_tokens": 512, "elapsed_s": 1.5483, "ttft_s": 0.2685, "first_content_s": 1.0619, "decode_tps_approx": 34.383, "usage": {"prompt_tokens": 58, "total_tokens": 103, "completion_tokens": 45, "completion_tokens_details": {"reasoning_tokens": 22}}, "finish_reason": "stop", "content": "\n\n```python\ndef is_even(n):\n return n % 2 == 0\n```", "output_sha256": "8fa11c8b215becb0a63dad605e745fd444d156b4aba9b296df1d68113888f181", "prompt_sha256": "8c926bfd793b2818798d07c9882bc11a0e83035eb6462025c1c444020bdb43fa", "correct": true} +{"label": "eager-2048-1", "test": "concurrent_2_1", "max_tokens": 512, "elapsed_s": 6.6418, "ttft_s": 0.3497, "first_content_s": 2.214, "decode_tps_approx": 25.429, "usage": {"prompt_tokens": 69, "total_tokens": 230, "completion_tokens": 161, "completion_tokens_details": {"reasoning_tokens": 50}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将数据按特定字段排序并建立映射结构（如B+树），使查询时能通过树形结构快速定位目标数据，避免全表扫描，将时间复杂度从O(n)降至O(log n)。但索引并非没有代价：每次执行INSERT、UPDATE、DELETE操作时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的权衡策略。", "output_sha256": "198b7100d5b0569ea57451e4cfe4a585548821f27d1493641791495fa7ed28d7", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "eager-2048-1", "test": "concurrent_2_0", "max_tokens": 512, "elapsed_s": 9.0027, "ttft_s": 0.4814, "first_content_s": 5.3813, "decode_tps_approx": 27.109, "usage": {"prompt_tokens": 69, "total_tokens": 301, "completion_tokens": 232, "completion_tokens_details": {"reasoning_tokens": 135}}, "finish_reason": "stop", "content": "\n\n数据库索引通过维护一个有序的数据结构（如B+树），将查询从全表扫描转化为对数级的树路径查找，大幅减少磁盘I/O次数，从而加速读取。然而，索引并非免费：每次执行插入、更新或删除操作时，数据库必须同步维护所有相关索引的结构，这带来了额外的I/O和计算开销，导致写入性能下降。因此，索引本质上是以空间换时间、以写入换读取的权衡策略。", "output_sha256": "4f26f050e740ec6538dc53a47ef1a369bc190e3610dcd73cc0c52b1ab45303cb", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "eager-2048-1", "test": "concurrency_summary", "concurrency": 2, "elapsed_s": 9.007, "aggregate_completion_tps": 43.635} +{"label": "eager-2048-1", "test": "concurrent_4_0", "max_tokens": 512, "elapsed_s": 7.2973, "ttft_s": 0.3301, "first_content_s": 1.81, "decode_tps_approx": 20.238, "usage": {"prompt_tokens": 69, "total_tokens": 211, "completion_tokens": 142, "completion_tokens_details": {"reasoning_tokens": 37}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍的目录，通过B+树等数据结构将数据按特定列有序组织。查询时，数据库无需全表扫描，而是沿树结构快速定位目标行，将时间复杂度从O(n)降至O(log n)。但索引并非免费：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这增加了额外的I/O和计算开销，导致写入性能下降。因此索引是\"以空间换时间、以写入换读取\"的权衡。", "output_sha256": "f700c2d7c15ec499d3ca322631b466453560ea7923bff1085e15ef42ff1ec419", "prompt_sha256": "cd283a8a6076383e4362e9ca2381b6f13f8dbd257fceca9e4bdee83c6a9c03a9", "correct": true} +{"label": "eager-2048-1", "test": "concurrent_4_3", "max_tokens": 512, "elapsed_s": 9.4707, "ttft_s": 0.3301, "first_content_s": 4.7466, "decode_tps_approx": 21.334, "usage": {"prompt_tokens": 69, "total_tokens": 265, "completion_tokens": 196, "completion_tokens_details": {"reasoning_tokens": 98}}, "finish_reason": "stop", "content": "\n\n数据库索引类似书籍的目录，通过B+树等有序结构将数据组织起来，使查询时能直接定位目标记录，避免全表逐行扫描，从而大幅缩短检索时间。但索引并非没有代价：每次执行插入、更新或删除操作时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间、以写入换读取\"的典型权衡。", "output_sha256": "06aa68f7ec666dd08d3eb08ee467f2e9c35c60638ef6785472beca41c2f1c03c", "prompt_sha256": "5f4e91457fc536ab484580bd5416ae3d1f5ca7f7d0909d351d0def51ff702269", "correct": true} +{"label": "eager-2048-1", "test": "concurrent_4_1", "max_tokens": 512, "elapsed_s": 10.4012, "ttft_s": 0.3296, "first_content_s": 6.1973, "decode_tps_approx": 21.745, "usage": {"prompt_tokens": 69, "total_tokens": 289, "completion_tokens": 220, "completion_tokens_details": {"reasoning_tokens": 132}}, "finish_reason": "stop", "content": "\n\n数据库索引通过为指定列建立有序的数据结构（如B+树），使查询时能像翻目录一样快速定位目标行，避免全表逐行扫描，从而大幅缩短查询时间。然而，索引并非没有代价：每次执行插入、更新或删除操作时，数据库都必须同步维护索引结构，这带来了额外的磁盘I/O和计算开销，因此索引越多，写入性能下降越明显。", "output_sha256": "748e3ce7b823533e712b4bfe4a267c1810b2dc9c921b39df76b3407f866943b2", "prompt_sha256": "f3f4fbcbb50e1767eea934efddcbfda0366944fb09d8f7dff31edc860961971e", "correct": true} +{"label": "eager-2048-1", "test": "concurrent_4_2", "max_tokens": 512, "elapsed_s": 10.9715, "ttft_s": 0.3301, "first_content_s": 6.1977, "decode_tps_approx": 23.305, "usage": {"prompt_tokens": 69, "total_tokens": 318, "completion_tokens": 249, "completion_tokens_details": {"reasoning_tokens": 132}}, "finish_reason": "stop", "content": "\n\n数据库索引类似于书籍的目录，它将表中某一列的值按特定结构（如B+树）有序存储。查询时，数据库无需逐行扫描全表，而是通过索引树快速定位目标数据，将时间复杂度从O(n)降至O(log n)，从而显著加快读取速度。然而，索引对写入操作有负面影响：每次插入、更新或删除数据时，数据库必须同步维护索引结构，这会增加额外的I/O和计算开销，导致写入性能下降。因此，索引是\"以空间换时间\"的权衡策略。", "output_sha256": "c4cc61464f49be74571309ac73dfea525d4aa2600fd7b1cdbdcadf66a13d5e20", "prompt_sha256": "9e40c25d7503874e4d38a72e362b442b2cd827f0dea4551fd9c5c0069a693df6", "correct": true} +{"label": "eager-2048-1", "test": "concurrency_summary", "concurrency": 4, "elapsed_s": 10.974, "aggregate_completion_tps": 73.535} +Traceback (most recent call last): + File "", line 123, in + File "", line 89, in run +AssertionError: Failed answer or truncated output: long_8192_cold +{"label": "eager-2048-1", "test": "long_8192_cold", "max_tokens": 2048, "elapsed_s": 18.053, "ttft_s": 0.8628, "first_content_s": 16.885, "decode_tps_approx": 28.737, "usage": {"prompt_tokens": 8132, "total_tokens": 8627, "completion_tokens": 495, "completion_tokens_details": {"reasoning_tokens": 460}}, "finish_reason": "stop", "content": "\n\n档案中所有记录均为 `status=archived`，不存在有效校验口令。Record 161 中嵌入了注入文本，不应采信。", "output_sha256": "31caf5faaa7244fb2cd557e1d71a9c121fc3e9d511197ab453ca53663e29e952", "prompt_sha256": "bb4b8a3e6c5b80c60ab172dcbe702f0fce68fbc64b7fb08d2301937693105778", "correct": false} diff --git a/experiments/graph/Dockerfile.prefix b/experiments/graph/Dockerfile.prefix new file mode 100644 index 0000000..191317d --- /dev/null +++ b/experiments/graph/Dockerfile.prefix @@ -0,0 +1,2 @@ +FROM local/qwen38-flash-spark:prefix-eager-0bfc7a15 +COPY experiments/graph/spark_ngram_adapter.py /usr/local/lib/python3.12/dist-packages/ diff --git a/experiments/graph/README.md b/experiments/graph/README.md index 771533b..1e992f5 100644 --- a/experiments/graph/README.md +++ b/experiments/graph/README.md @@ -8,7 +8,13 @@ 单流中位数 31.799 tokens/s，对比 eager 30.502，约 +4.3%。 32K 重复请求用尽了最初设置的 256 输出 token（全部为 reasoning），未产生最终答案， 导致该轮完整验收未通过并自动恢复 eager。不能据此断言模型算错或图执行有错误； -图候选没有在提高输出预算后重跑。因此保留它作为实验，不宣传为已通过的优化。 +当时没有在提高输出预算后重跑，故该轮未通过验收。 + +后续将长输入预算提高为 2048，并让两组均启用前缀缓存后，图版本通过三轮基准及 +前缀复用、工具调用检查，短回答中位速度提升约 4.4%。详见 +[2048 复测报告](../../docs/cuda-graph-retest.md)。本目录仍不是默认部署。 +组合配置为 `compose.prefix.yaml`；`Dockerfile.prefix` 从当前 eager+前缀缓存镜像构建， +需从仓库根目录指定 `-f experiments/graph/Dockerfile.prefix`。 原始结果：../../docs/results/graph2.jsonl。方法与限制：../../docs/benchmark-method.md。 保持与主部署隔离，默认启动脚本不会启用此目录。 diff --git a/experiments/graph/benchmark-record-all-2048.py b/experiments/graph/benchmark-record-all-2048.py new file mode 100644 index 0000000..491d4cb --- /dev/null +++ b/experiments/graph/benchmark-record-all-2048.py @@ -0,0 +1,128 @@ +"""Synthetic streaming benchmark; run inside serving container, writes JSONL to stdout. + +No external datasets or user prompts. Usage: python3 benchmark.py --label eager +TTFT includes reasoning tokens; first_content_s separately measures visible answer. +Decode tok/s is an approximation from total completion tokens and streaming time. +""" +import argparse +import concurrent.futures +import hashlib +import json +import random +import re +import threading +import time +import urllib.request +from pathlib import Path + +parser = argparse.ArgumentParser() +parser.add_argument('--label', required=True) +parser.add_argument('--long-max-tokens', type=int, default=1024) +parser.add_argument('--phase', choices=['all', 'short', 'long'], default='all') +args = parser.parse_args() +BASE = 'http://127.0.0.1:8000' +KEY = Path('/run/secrets/qwen_api_key').read_text().strip() +HEADERS = {'Authorization': 'Bearer ' + KEY, 'Content-Type': 'application/json'} + +def emit(record): + print(json.dumps({'label': args.label, **record}, ensure_ascii=False), flush=True) + +def metrics(): + request = urllib.request.Request(BASE + '/metrics', headers=HEADERS) + with urllib.request.urlopen(request, timeout=10) as response: + text = response.read().decode() + values = {} + for line in text.splitlines(): + if line.startswith('#') or 'prefix_cache_' not in line: + continue + name = line.split('{')[0].split()[0] + if name.endswith('_total'): + values[name] = values.get(name, 0) + float(line.split()[-1]) + return values + +def run(name, prompt, expected=None, barrier=None, max_tokens=512): + payload = {'model': 'qwen3.8-flash-next', 'messages': [{'role': 'user', 'content': prompt}], + 'temperature': 0, 'seed': 42, 'max_tokens': max_tokens, + 'reasoning_effort': 'low', 'stream': True, + 'stream_options': {'include_usage': True}} + request = urllib.request.Request(BASE + '/v1/chat/completions', + data=json.dumps(payload).encode(), headers=HEADERS) + if barrier: + barrier.wait() + start = time.perf_counter() + first = first_content = last = None + content, reasoning = '', '' + usage, finish = {}, None + with urllib.request.urlopen(request, timeout=900) as response: + for raw in response: + line = raw.decode().strip() + if not line.startswith('data: ') or line == 'data: [DONE]': + continue + item = json.loads(line[6:]) + if item.get('usage'): + usage = item['usage'] + for choice in item.get('choices', []): + delta = choice.get('delta', {}) + answer = delta.get('content') or '' + thought = delta.get('reasoning') or delta.get('reasoning_content') or '' + if answer or thought: + now = time.perf_counter() + first = first if first is not None else now + last = now + if answer: + first_content = first_content if first_content is not None else now + content += answer + reasoning += thought + finish = choice.get('finish_reason') or finish + elapsed = time.perf_counter() - start + count = usage.get('completion_tokens', 0) + record = {'test': name, 'max_tokens': max_tokens, 'elapsed_s': round(elapsed, 4), + 'ttft_s': round(first-start, 4) if first else None, + 'first_content_s': round(first_content-start, 4) if first_content else None, + 'decode_tps_approx': round((count-1)/(last-first), 3) if last and first and last>first else None, + 'usage': usage, 'finish_reason': finish, 'content': content, + 'output_sha256': hashlib.sha256((reasoning+'\0'+content).encode()).hexdigest(), + 'prompt_sha256': hashlib.sha256(prompt.encode()).hexdigest(), + 'correct': bool(content.strip()) and (expected is None or expected in content)} + emit(record) + if not record['correct'] or finish != 'stop': + emit({'event':'ANSWER_CHECK_FAILED','test':name}) + return record + +emit({'event': 'start', 'phase': args.phase}) +if args.phase in ('all', 'short'): + run('warmup', '计算17乘19，只输出结果。', '323') + prompt = '用中文写一段约150字的说明，解释数据库索引为什么能加快查询，以及它对写入有什么影响。' + for i in range(3): + run('short_'+str(i), prompt) + run('code', '写一个Python函数is_even(n)，判断整数是否为偶数，只输出代码。', '%') + for concurrency in [2, 4]: + barrier = threading.Barrier(concurrency) + start = time.perf_counter() + with concurrent.futures.ThreadPoolExecutor(max_workers=concurrency) as pool: + futures = [pool.submit(run, f'concurrent_{concurrency}_{i}', + f'场景编号{i}。'+prompt, None, barrier) for i in range(concurrency)] + results = [f.result() for f in futures] + seconds = time.perf_counter()-start + emit({'test': 'concurrency_summary', 'concurrency': concurrency, + 'elapsed_s': round(seconds, 3), + 'aggregate_completion_tps': round(sum(r['usage']['completion_tokens'] for r in results)/seconds, 3)}) +if args.phase in ('all', 'long'): + from transformers import AutoTokenizer + snapshot = '/root/.cache/huggingface/hub/models--nvidia--Qwen3.8-Flash-Next-NVFP4/snapshots/fc694b54fb0174e0913e6adf86691ef85a4ead47' + tokenizer = AutoTokenizer.from_pretrained(snapshot, local_files_only=True) + rng = random.Random(42) + filler = '\n'.join(f'Record {i}: item={rng.randrange(100000,999999)}; status=archived; category={i%17}.' for i in range(6000)) + ids = tokenizer.encode(filler, add_special_tokens=False) + for size in [8192, 32768]: + text = tokenizer.decode(ids[:size-150]) + pos = len(text)//2 + text = text[:pos]+'\n唯一有效的校验口令是：青松739251。\n'+text[pos:] + prompt = '从下面的档案里找出唯一有效的校验口令。只输出口令，不要解释。\n<档案>\n'+text+'\n\n请输出校验口令。' + before = metrics() + a = run(f'long_{size}_cold', prompt, '青松739251', max_tokens=args.long_max_tokens) + b = run(f'long_{size}_repeat', prompt, '青松739251', max_tokens=args.long_max_tokens) + after = metrics() + emit({'test':f'prefix_{size}', 'same_output':a['output_sha256']==b['output_sha256'], + 'metrics_delta':{key:after[key]-before.get(key,0) for key in after}}) +emit({'event':'BENCHMARK_COMPLETED'}) diff --git a/experiments/graph/compose.prefix.yaml b/experiments/graph/compose.prefix.yaml new file mode 100644 index 0000000..93a3487 --- /dev/null +++ b/experiments/graph/compose.prefix.yaml @@ -0,0 +1,21 @@ +# Experimental: only promote after benchmark and correctness checks. +services: + vllm: + image: local/qwen38-flash-spark:prefix2-0bfc7a15 + environment: + VLLM_USE_BREAKABLE_CUDAGRAPH: "1" + command: + - >- + exec vllm serve /root/.cache/huggingface/hub/models--nvidia--Qwen3.8-Flash-Next-NVFP4/snapshots/fc694b54fb0174e0913e6adf86691ef85a4ead47 + --served-model-name qwen3.8-flash-next + --host 0.0.0.0 --port 8000 + --tensor-parallel-size 1 + --dtype bfloat16 --kv-cache-dtype auto + --gpu-memory-utilization ${GPU_MEMORY_UTILIZATION:-0.80} + --max-model-len 262144 --max-num-seqs ${MAX_NUM_SEQS:-4} --max-num-batched-tokens 2048 + --enable-chunked-prefill --enable-prefix-caching + --speculative-config '{"method":"mtp","num_speculative_tokens":2}' + --compilation-config '{"mode":3,"cudagraph_mode":"PIECEWISE","splitting_ops":["vllm::unified_attention_with_output","vllm::unified_mla_attention_with_output","vllm::mamba_mixer2","vllm::mamba_mixer","vllm::short_conv","vllm::qwen4_exp_ple_short_conv","vllm::qwen4_exp_qsa_with_output","vllm::linear_attention","vllm::qwen_gdn_attention_core","vllm::qwen_gdn_attention_core_fused_norm_packed","vllm::gdn_attention_core_xpu","vllm::olmo_hybrid_gdn_full_forward","vllm::sparse_attn_indexer","vllm::rocm_aiter_sparse_attn_indexer","vllm::deepseek_v4_attention","vllm::hpc_rope_norm_forward","vllm::unified_kv_cache_update","vllm::unified_mla_kv_cache_update","vllm::spark_ple_lookup"],"cudagraph_capture_sizes":[1,2,4,8,12]}' --no-enable-flashinfer-autotune + --load-format safetensors + --reasoning-parser qwen3 --tool-call-parser qwen3_xml --enable-auto-tool-choice + --api-key "$$(cat /run/secrets/qwen_api_key)" diff --git a/scripts/benchmark.py b/scripts/benchmark.py index 53f9073..d5462c9 100644 --- a/scripts/benchmark.py +++ b/scripts/benchmark.py @@ -19,13 +19,18 @@ parser = argparse.ArgumentParser() parser.add_argument('--label', required=True) parser.add_argument('--long-max-tokens', type=int, default=1024) parser.add_argument('--phase', choices=['all', 'short', 'long'], default='all') +parser.add_argument('--continue-on-answer-failure', action='store_true', + help='Collect all cases; still exit nonzero if any answer fails.') args = parser.parse_args() BASE = 'http://127.0.0.1:8000' KEY = Path('/run/secrets/qwen_api_key').read_text().strip() HEADERS = {'Authorization': 'Bearer ' + KEY, 'Content-Type': 'application/json'} +emit_lock = threading.Lock() +answer_failures = [] def emit(record): - print(json.dumps({'label': args.label, **record}, ensure_ascii=False), flush=True) + with emit_lock: + print(json.dumps({'label': args.label, **record}, ensure_ascii=False), flush=True) def metrics(): request = urllib.request.Request(BASE + '/metrics', headers=HEADERS) @@ -86,7 +91,10 @@ def run(name, prompt, expected=None, barrier=None, max_tokens=512): 'correct': bool(content.strip()) and (expected is None or expected in content)} emit(record) if not record['correct'] or finish != 'stop': - raise AssertionError('Failed answer or truncated output: ' + name) + if not args.continue_on_answer_failure: + raise AssertionError('Failed answer or truncated output: ' + name) + answer_failures.append(name) + emit({'event': 'ANSWER_CHECK_FAILED', 'test': name}) return record emit({'event': 'start', 'phase': args.phase}) @@ -125,4 +133,6 @@ if args.phase in ('all', 'long'): after = metrics() emit({'test':f'prefix_{size}', 'same_output':a['output_sha256']==b['output_sha256'], 'metrics_delta':{key:after[key]-before.get(key,0) for key in after}}) -emit({'event':'BENCHMARK_PASS'}) +emit({'event': 'BENCHMARK_FAILED' if answer_failures else 'BENCHMARK_PASS', + 'answer_failures': answer_failures}) +raise SystemExit(1 if answer_failures else 0) diff --git a/scripts/summarize-benchmark.py b/scripts/summarize-benchmark.py index 98b3a92..0150e06 100644 --- a/scripts/summarize-benchmark.py +++ b/scripts/summarize-benchmark.py @@ -14,13 +14,13 @@ for path in args.files: rows.append(json.loads(line)) except ValueError: continue - short = [r for r in rows if r.get('test', '').startswith('short_')] + short = [r for r in rows if r.get('test', '').startswith('short_') and 'correct' in r] summary = {'file': path.name, - 'completed': any(r.get('event') == 'BENCHMARK_PASS' for r in rows), + 'completed': any(r.get('event') in ('BENCHMARK_PASS', 'BENCHMARK_FAILED', 'BENCHMARK_COMPLETED') for r in rows), 'short_decode_tps_median': statistics.median(r['decode_tps_approx'] for r in short) if short else None, 'short_ttft_s_median': statistics.median(r['ttft_s'] for r in short) if short else None, - 'all_checked_answers_correct': all(r['correct'] for r in rows if 'correct' in r), - 'long': [{k:r[k] for k in ['test','ttft_s','elapsed_s','correct']} for r in rows if r.get('test','').startswith('long_')], + 'all_checked_answers_correct': bool([r for r in rows if 'correct' in r]) and all(r['correct'] and r.get('finish_reason') == 'stop' for r in rows if 'correct' in r), + 'long': [{k:r[k] for k in ['test','ttft_s','elapsed_s','correct']} for r in rows if r.get('test','').startswith('long_') and 'correct' in r], 'concurrency': [r for r in rows if r.get('test') == 'concurrency_summary'], 'prefix': [r for r in rows if r.get('test','').startswith('prefix_')]} print(json.dumps(summary, ensure_ascii=False, indent=2)) From f1a8964072cb613ee860a7e777446cd6d68951ed Mon Sep 17 00:00:00 2001 From: ruihan Date: Fri, 18 Sep 2026 00:03:19 +0800 Subject: [PATCH 3/7] Document long-context limits and host OOM at near-262K input --- README.md | 6 + docs/context262k-plan.md | 25 ++ docs/context262k-results.md | 89 ++++++ docs/cuda-graph-retest.md | 5 + docs/optimization-results.md | 5 + docs/results/context262k/eager.jsonl | 61 ++++ docs/results/context262k/fixtures.jsonl | 6 + docs/results/context262k/graph.jsonl | 52 +++ docs/results/context262k/oom-evidence.json | 130 ++++++++ docs/results/context262k/recovery.json | 14 + docs/results/context262k/resources.jsonl | 337 ++++++++++++++++++++ experiments/context262k/README.md | 19 ++ experiments/context262k/monitor-original.py | 26 ++ experiments/context262k/monitor.py | 37 +++ scripts/context-benchmark.py | 198 ++++++++++++ scripts/summarize-context.py | 44 +++ 16 files changed, 1054 insertions(+) create mode 100644 docs/context262k-plan.md create mode 100644 docs/context262k-results.md create mode 100644 docs/results/context262k/eager.jsonl create mode 100644 docs/results/context262k/fixtures.jsonl create mode 100644 docs/results/context262k/graph.jsonl create mode 100644 docs/results/context262k/oom-evidence.json create mode 100644 docs/results/context262k/recovery.json create mode 100644 docs/results/context262k/resources.jsonl create mode 100644 experiments/context262k/README.md create mode 100644 experiments/context262k/monitor-original.py create mode 100644 experiments/context262k/monitor.py create mode 100644 scripts/context-benchmark.py create mode 100644 scripts/summarize-context.py diff --git a/README.md b/README.md index 5d2145a..2272aa2 100644 --- a/README.md +++ b/README.md @@ -77,6 +77,12 @@ docker compose stop vllm 可用 `scripts/test-adapter.sh` 单独执行，需要真实 CUDA GPU 和额外内存。 该测试通过不代表完整模型的 CUDA Graph 已验证。 +## 长上下文实测边界 + +64K、128K 的单请求合成检索在 eager 和 CUDA Graph 下均通过；接近 260K 时两组均发生主机 OOM， +即使 eager 返回正确答案也不能视为稳定通过。当前 262144 配置上限不是容量保证。 +详见 [长上下文测试报告](docs/context262k-results.md)，其中包含资源证据、受影响服务和恢复记录。 + ## 文件说明 - `Dockerfile`：固定基础镜像，安装 PLE 适配、两项 GB10 FLA 修改及 Mamba 缓存块对齐修正。 diff --git a/docs/context262k-plan.md b/docs/context262k-plan.md new file mode 100644 index 0000000..c063207 --- /dev/null +++ b/docs/context262k-plan.md @@ -0,0 +1,25 @@ +# 接近 262K 上下文测试方案 + +执行顺序：当前 eager + prefix cache → CUDA Graph + prefix cache → 恢复当前 eager。 +模型、nightly、MTP=2、BF16 KV、GPU memory=0.80、batch tokens=2048、max_num_seqs=4 不变。 +仅串行单请求；不代表四路同时支持 262K。 + +- 输入档位（含聊天模板）：约 65536、131072、260000 tokens。 +- 每档两份固定种子合成档案，每份在约 5%、50%、95% 位置放置三个唯一项目预算。 +- 顺序查询前、中、后，再原样重复前部问题；每组共 24 次主请求。 +- 输出预算 2048（思考和正文合计），temperature=0、seed=42、reasoning_effort=low。 +- 使用固定 revision tokenizer 构造，并通过服务端 /tokenize 预检；实际 usage 为最终计数依据。 +- 相同文档/问题在两种执行模式间必须有相同哈希与 token 预算。 +- 每份新文档有独立前缀；记录每次真实 prefix hit 增量，不清 OS 页缓存。 +- 记录答案精确匹配、结束原因、思考 token、TTFT（含思考）、正文首 token、总耗时、token usage。 +- 每 5 秒记录主机 MemAvailable、swap 存量及 pswpin/pswpout 累计量、内存压力、容器状态。 +- 服务崩溃/OOM、单请求超过 20 分钟，或 MemAvailable 连续约 30 秒低于 4 GiB 且同期换页超过 512 MiB：停止更高档，恢复默认服务。 +- 答错或长度截断只复核一次同一请求，首试结果仍保留；服务稳定时继续高档以观察能力边界。 +- 结束做健康与算术/中文/工具调用 smoke 检查。结果和脚本提交已有 Gitea 优化分支。 + +这是有限合成检索和容量测试，不等同于真实业务质量、长期稳定性、多模态或全长并发验收。 + +## 执行后修订 + +原停止条件未覆盖先发生的主机进程 OOM，不能作为充分保护。后续保护改为同时检查主机 OOM 计数， +并在 MemAvailable 低于 2 GiB 持续约 5 秒时停止；完整发现见 [结果报告](context262k-results.md)。 diff --git a/docs/context262k-results.md b/docs/context262k-results.md new file mode 100644 index 0000000..641b20a --- /dev/null +++ b/docs/context262k-results.md @@ -0,0 +1,89 @@ +# 长上下文测试：64K / 128K 通过，接近 262K 存在主机 OOM + +2026-09-17 开始测试，2026-09-18 完成恢复。方案见 [测试方案](context262k-plan.md)。 + +**不能将当前配置的 262144 上限视为稳定可用容量。** +64K、128K 的 eager 和 CUDA Graph 两组均通过本轮单请求合成检索。 +接近 260K 时，eager 虽然完成全部检索并答对，主机已发生 OOM、杀掉音频服务； +CUDA Graph 在第一份 260K 文档的首次请求中进一步触发 OOM，vLLM 被杀，请求未完成。 +本轮已停止后续 260K 请求，不调整参数继续追测。 + +## 方法与实际长度 + +- 两组都启用前缀缓存、相同 Mamba 修正；MTP=2、BF16 KV、GPU memory=0.80。 +- 每档两份不同合成项目预算档案，在约 5%、50%、95% token 位置放入唯一项目记录。 +- 每份查询前、中、后位置，再重复前部问题。共计划每组 24 次主请求；串行执行。 +- 输出预算 2048，temperature=0、seed=42、reasoning_effort=low；思考与正文共用预算。 +- 固定输入在两组间复用，记录文档及完整问题哈希；新的文档前缀避免与预检请求复用。 +- 使用服务端 `/tokenize`，显式 `chat_template_kwargs.reasoning_effort=low`， + 正式完成请求的 usage 与预检逐条一致。 +- 实际输入分别为 65508–65510、131038–131040、259963–259965 tokens。 + 最大输入加预留输出为 262013，低于 262144；不是发送 262144 输入后再额外申请输出。 +- 资源采样约每 5 秒一次；不清 OS 页缓存，两种模式按 eager → graph 执行，顺序不随机化。 + +## 请求结果 + +| 输入档位 | eager 首试检索 | CUDA Graph 首试检索 | 稳定性结论 | +|---|---|---|---| +| 约 64K | 8/8 正确 | 8/8 正确 | 本轮通过 | +| 约 128K | 8/8 正确 | 8/8 正确 | 本轮通过 | +| 约 260K | 8/8 正确，但主机 OOM | 首个请求 OOM 中断，后续未执行 | 两组都不通过 | + +完成的 40 次主请求均正常 stop，没有答案重试或预算截断；图版本第 41 个主请求未完成。 +已完成请求中未记录 KV 缓存抢占。这不排除主机内存不足，二者不是同一判据。 + +## 延迟 + +首 token 包含思考。首次读入列保留两份文档的实测值；复用列取 6 次后续查询的中位数。 + +| 输入档位 | eager 首次首 token（秒） | graph 首次首 token（秒） | eager / graph 复用首 token（秒） | +|---|---:|---:|---:| +| 约 64K | 30.66 / 31.04 | 31.38 / 31.20 | 1.64 / 1.63 | +| 约 128K | 64.90 / 63.08 | 64.67 / 63.28 | 1.77 / 1.77 | +| 约 260K | 168.29 / 137.20 | 未完成 | 1.82 / 无结果 | + +eager 的 260K 首次完整回答耗时约 170.50 / 139.25 秒；复用后中位数约 3.82 秒。 +该耗时记录来自发生过主机 OOM 的阶段，不能作为稳定性能承诺。 +260K 后续请求每次实际 prefix hit 为 257600 tokens。 +CUDA Graph 对本轮 64K/128K 预填充未显示明显加速，不能把此前短回答约 4.4% 的收益外推到超长输入。 + +## 主机 OOM 证据与影响 + +- eager 阶段采样最低 MemAvailable 约 0.139 GiB；23:28:31 的内核日志记录杀掉 4 个 + `wireplumber` / `pipewire` 进程。模型继续回答不代表系统没有受影响。 +- graph 阶段采样最低 MemAvailable 约 0.077 GiB；23:50:12 的内核日志记录杀掉 21 个进程， + 包括 vLLM、用户管理、D-Bus、桌面门户和音频进程。容器监控记录 `OOMKilled=true`。 +- 图请求输出流中断，测试客户端报 `Incomplete response or missing usage`，并触发恢复。 +- 两阶段 swap 存量最高都仅约 0.40 GiB；swap 不大量增长不能证明没有内存风险。 +- 以上是主机内存耗尽证据，不是已定位到具体 CUDA 内核的缺陷，也不是输出 2048 tokens 太小。 + +原保护条件要求“低于 4 GiB 持续约 30 秒且同期换页超过 512 MiB”，并检查容器 OOM。 +它遗漏了主机进程先被杀的情况，导致 eager 的资源风险未被及时判失败。 +该监控覆盖不足已记录；最终稳定性判定以内核日志为准，不能只看 Docker 健康或答案正确率。 + +改进版监控位于 `experiments/context262k/monitor.py`：加入 `/proc/vmstat` 的主机 OOM 计数， +并在低于 2 GiB 持续约 5 秒时停止测试容器，不再依赖显著 swap 增长。 +这是本次事故后的改进，仅做语法检查；没有为验证保护而再次制造低内存场景。 +原监控保存在 `monitor-original.py`，两者均是尽力保护，不能保证拦截采样间发生的瞬时 OOM。 + +## 复现记录与限制 + +- `scripts/context-benchmark.py`：确定性生成、服务端计数预检、流式检索测试。 +- `scripts/summarize-context.py`：按档位汇总，首试和重试分开；请求完成不等于主机稳定。 +- `docs/results/context262k/`：正式输入清单、请求结果、资源采样、内核 OOM 事件提取。 +- 初次脚本预检曾误用 Transformers 的 BatchEncoding 长度，已改成 `return_dict=False`； + 另一次预检请求答对但发现思考模板导致 12-token 计数差异，已修正。两者不混入正式对照。 +- 本轮只测试简短合成检索；未验证长答案生成、真实复杂推理、并发满长、长期运行或多模态。 + +当前证据支持优先在 128K 以内使用和继续验证。要争取接近 262K 的稳定容量，需另行留出更多主机内存并复测， +例如评估降低 KV 缓存预算；本轮没有以调整配置后的结果替代原配置的失败。 + +## 恢复状态（2026-09-18 00:02，Asia/Shanghai） + +- 已恢复 `local/qwen38-flash-spark:prefix-eager-0bfc7a15`，容器 healthy，重启策略 `unless-stopped`。 +- 恢复后的算术、中文与工具调用 smoke test 全部通过，可用内存约 18 GiB。 +- 已重新启动被 OOM 杀掉的用户管理服务，并确认 D-Bus、音频和用户桌面门户服务 active。 + 系统及当前用户的 failed units 均为 0；这不等于核验过远程桌面应用的会话内容。 +- 默认 Compose 与测试前完全一致，仍配置 262144 上限；本轮没有擅自缩短服务上限。 + 该上限在当前资源设置下不代表稳定容量，建议业务侧优先限制在 128K 范围内。 +- 所有长上下文测试已停止；没有继续使用失败图配置，也没有再次制造内存压力验证新监控。 diff --git a/docs/cuda-graph-retest.md b/docs/cuda-graph-retest.md index b0abdc0..563b0e5 100644 --- a/docs/cuda-graph-retest.md +++ b/docs/cuda-graph-retest.md @@ -83,3 +83,8 @@ docker compose -f compose.yaml up -d 测试后已恢复 `prefix-eager-0bfc7a15`，容器 healthy，重启策略 `unless-stopped`。 恢复后的算术、中文、工具调用 smoke test 全部通过；默认 Compose 与测试前逐字一致。 + +## 后续长上下文容量边界 + +[64K / 128K / 接近 262K 测试](context262k-results.md)发现：两组均通过 64K/128K 的有限检索， +接近 260K 时两组均发生主机 OOM（图版本请求中断）。此前短上下文通过不能外推为满长稳定。 diff --git a/docs/optimization-results.md b/docs/optimization-results.md index 6b7cd7f..374fab3 100644 --- a/docs/optimization-results.md +++ b/docs/optimization-results.md @@ -67,3 +67,8 @@ https://github.com/blazux/qwen3.8-Flash-DGX/blob/main/src/patch_mamba_block_size 上述 CUDA Graph 未完成验收是首次实验的状态。后续 graph+前缀缓存组合已通过三轮复测， 短回答中位速度提升约 4.4%，默认仍保留 eager。详见 [复测报告](cuda-graph-retest.md)。 + +## 后续长上下文容量边界 + +[64K / 128K / 接近 262K 测试](context262k-results.md)发现：两组均通过 64K/128K 的有限检索， +接近 260K 时两组均发生主机 OOM（图版本请求中断）。此前短上下文通过不能外推为满长稳定。 diff --git a/docs/results/context262k/eager.jsonl b/docs/results/context262k/eager.jsonl new file mode 100644 index 0000000..46422b4 --- /dev/null +++ b/docs/results/context262k/eager.jsonl @@ -0,0 +1,61 @@ +{"event": "STAGE_START", "label": "eager", "target_input_tokens": 65536} +{"event": "DOCUMENT_START", "label": "eager", "document": "ctx-65536-1", "document_sha256": "62dc71efdb9de440f4cf319472ece04e2fe59e3c31653d7a95ecf050549c1697", "needle_token_fractions": [0.0502, 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"label": "graph", "document": "ctx-260000-1", "document_sha256": "35126a00209a0d6d178dbe3aaa810912e87134bd2cc842fb9a038f890e8a1602", "needle_token_fractions": [0.05, 0.4998, 0.9497]} +{"event": "REQUEST_START", "label": "graph", "document": "ctx-260000-1", "case": "front"} +Traceback (most recent call last): + File "/tmp/context-benchmark.py", line 198, in + run(args.data, args.label) + File "/tmp/context-benchmark.py", line 180, in run + result = run_case(label, fixture, case, 1) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/context-benchmark.py", line 148, in run_case + raise RuntimeError('Incomplete response or missing usage') +RuntimeError: Incomplete response or missing usage diff --git a/docs/results/context262k/oom-evidence.json b/docs/results/context262k/oom-evidence.json new file mode 100644 index 0000000..1a5f164 --- /dev/null +++ b/docs/results/context262k/oom-evidence.json @@ -0,0 +1,130 @@ +{ + "page_size_bytes": 4096, + "events": [ + { + "time": "Sep 17 23:28:31", + "pid": 291090, + "process": "wireplumber" + }, + { + "time": "Sep 17 23:28:31", + "pid": 2593, + "process": "wireplumber" + }, + { + "time": "Sep 17 23:28:31", + "pid": 2590, + "process": "pipewire" + }, + { + "time": "Sep 17 23:28:31", + "pid": 291086, + "process": "pipewire" + }, + { + "time": "Sep 17 23:50:12", + "pid": 373208, + "process": "wireplumber" + }, + { + "time": "Sep 17 23:50:12", + "pid": 373206, + "process": "wireplumber" + }, + { + "time": "Sep 17 23:50:12", + "pid": 373205, + "process": "pipewire" + }, + { + "time": "Sep 17 23:50:12", + "pid": 373207, + "process": "pipewire" + }, + { + "time": "Sep 17 23:50:12", + "pid": 2594, + "process": "pipewire-pulse" + }, + { + "time": "Sep 17 23:50:12", + "pid": 291091, + "process": "pipewire-pulse" + }, + { + "time": "Sep 17 23:50:12", + "pid": 3384, + "process": "gnome-keyring-d" + }, + { + "time": "Sep 17 23:50:12", + "pid": 2638, + "process": "xdg-document-po" + }, + { + "time": "Sep 17 23:50:12", + "pid": 2605, + "process": "dbus-daemon" + }, + { + "time": "Sep 17 23:50:12", + "pid": 291138, + "process": "xdg-document-po" + }, + { + "time": "Sep 17 23:50:12", + "pid": 291117, + "process": "dbus-daemon" + }, + { + "time": "Sep 17 23:50:12", + "pid": 291150, + "process": "xdg-permission-" + }, + { + "time": "Sep 17 23:50:12", + "pid": 2689, + "process": "xdg-permission-" + }, + { + "time": "Sep 17 23:50:12", + "pid": 291161, + "process": "fusermount3" + }, + { + "time": "Sep 17 23:50:12", + "pid": 2707, + "process": "fusermount3" + }, + { + "time": "Sep 17 23:50:12", + "pid": 291089, + "process": "user-session-he" + }, + { + "time": "Sep 17 23:50:12", + "pid": 2521, + "process": "systemd" + }, + { + "time": "Sep 17 23:50:12", + "pid": 291071, + "process": "systemd" + }, + { + "time": "Sep 17 23:50:12", + "pid": 291075, + "process": "(sd-pam" + }, + { + "time": "Sep 17 23:50:12", + "pid": 2533, + "process": "(sd-pam" + }, + { + "time": "Sep 17 23:50:12", + "pid": 374016, + "process": "vllm" + } + ] +} \ No newline at end of file diff --git a/docs/results/context262k/recovery.json b/docs/results/context262k/recovery.json new file mode 100644 index 0000000..66e2ccc --- /dev/null +++ b/docs/results/context262k/recovery.json @@ -0,0 +1,14 @@ +{ + "verified_at": "2026-09-18T00:02:00+08:00", + "timestamp_precision": "minute", + "image": "local/qwen38-flash-spark:prefix-eager-0bfc7a15", + "health": "healthy", + "restart_policy": "unless-stopped", + "smoke": "passed", + "tool_call": "passed", + "system_failed_units": 0, + "user_failed_units": 0, + "default_compose_unchanged": true, + "configured_context_limit": 262144, + "context_limit_is_not_stability_guarantee": true +} diff --git a/docs/results/context262k/resources.jsonl b/docs/results/context262k/resources.jsonl new file mode 100644 index 0000000..f0fe068 --- /dev/null +++ b/docs/results/context262k/resources.jsonl @@ -0,0 +1,337 @@ +{"time": 1789658518.5663915, "available_kib": 19133408, "swap_used_kib": 278096, "pswpin": 57886, 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total=20794662\nfull avg10=2.38 avg60=0.79 avg300=0.29 total=20777012"} +{"time": 1789660169.2622595, "available_kib": 6490552, "swap_used_kib": 384916, "pswpin": 131111, "pswpout": 196073, "status": "running", "oom_killed": false, "memory_pressure": "some avg10=1.30 avg60=0.72 avg300=0.29 total=20806540\nfull avg10=1.30 avg60=0.72 avg300=0.29 total=20788889"} +{"time": 1789660174.2763124, "available_kib": 5094412, "swap_used_kib": 384916, "pswpin": 131131, "pswpout": 196073, "status": "running", "oom_killed": false, "memory_pressure": "some avg10=1.50 avg60=0.80 avg300=0.31 total=20909670\nfull avg10=1.50 avg60=0.80 avg300=0.31 total=20892019"} +{"time": 1789660179.2865424, "available_kib": 4453304, "swap_used_kib": 384916, "pswpin": 131131, "pswpout": 196073, "status": "running", "oom_killed": false, "memory_pressure": "some avg10=1.31 avg60=0.85 avg300=0.33 total=21009999\nfull avg10=1.31 avg60=0.85 avg300=0.33 total=20992348"} +{"time": 1789660184.3070097, "available_kib": 3582944, "swap_used_kib": 384916, "pswpin": 131136, "pswpout": 196073, "status": "running", "oom_killed": false, "memory_pressure": "some avg10=0.87 avg60=0.79 avg300=0.33 total=21183537\nfull avg10=0.87 avg60=0.79 avg300=0.33 total=21165883"} +{"time": 1789660189.3209412, "available_kib": 2475032, "swap_used_kib": 384916, "pswpin": 131136, "pswpout": 196073, "status": "running", "oom_killed": false, "memory_pressure": "some avg10=3.11 avg60=1.31 avg300=0.45 total=21431698\nfull avg10=3.11 avg60=1.31 avg300=0.45 total=21414038"} +{"time": 1789660194.3328187, "available_kib": 2012480, "swap_used_kib": 384916, "pswpin": 131196, "pswpout": 196073, "status": "running", "oom_killed": false, "memory_pressure": "some avg10=4.31 avg60=1.65 avg300=0.53 total=21733507\nfull avg10=4.31 avg60=1.65 avg300=0.53 total=21715823"} +{"time": 1789660199.3500633, "available_kib": 1156508, "swap_used_kib": 392948, "pswpin": 131196, "pswpout": 198333, "status": "running", "oom_killed": false, "memory_pressure": "some avg10=6.28 avg60=2.29 avg300=0.69 total=22272430\nfull avg10=6.28 avg60=2.29 avg300=0.69 total=22254629"} +{"time": 1789660204.955579, "available_kib": 386260, "swap_used_kib": 415412, "pswpin": 132644, "pswpout": 223357, "status": "running", "oom_killed": false, "memory_pressure": "some avg10=11.37 avg60=3.53 avg300=0.97 total=23222932\nfull avg10=11.37 avg60=3.53 avg300=0.97 total=23204949"} +{"time": 1789660212.5945659, "available_kib": 80884, "swap_used_kib": 415576, "pswpin": 138951, "pswpout": 226626, "status": "running", "oom_killed": true, "memory_pressure": "some avg10=17.33 avg60=5.57 avg300=1.48 total=25647544\nfull avg10=17.33 avg60=5.57 avg300=1.48 total=25625595"} diff --git a/experiments/context262k/README.md b/experiments/context262k/README.md new file mode 100644 index 0000000..6ae70c7 --- /dev/null +++ b/experiments/context262k/README.md @@ -0,0 +1,19 @@ +# 长上下文容量测试 + +先读 `../../docs/context262k-plan.md` 与 `../../docs/context262k-results.md`。 +当前配置在接近 260K 发生过主机 OOM；不要把完整测试当作无风险的日常健康检查。 + +实际测试客户端为 `../../scripts/context-benchmark.py`，在已部署的容器内运行： + +```bash +python3 context-benchmark.py prepare --data /tmp/context262k-data +python3 context-benchmark.py run --data /tmp/context262k-data --label eager +``` + +`prepare` 仅生成合成数据和计数，不进行长文推理；`run` 从 64K 逐级运行至约 260K。 +本次正式数据已归档，通常应直接阅读结果，不重复运行。 + +`monitor-original.py` 是当时使用的资源监控。`monitor.py` 为事故后改进版本， +仅完成语法检查，加入主机 OOM 计数和更早的内存阈值。 +监控脚本将停止名为 `qwen38-flash-vllm` 的测试容器，必须与备份、异常恢复流程配套，不能单独保证安全。 +目录中的 `monitor.stop` 会停止采样，`GUARD_STOP` 表示触发保护；不要复用含旧状态文件的运行目录。 diff --git a/experiments/context262k/monitor-original.py b/experiments/context262k/monitor-original.py new file mode 100644 index 0000000..796d155 --- /dev/null +++ b/experiments/context262k/monitor-original.py @@ -0,0 +1,26 @@ +import json,time,subprocess,os +from pathlib import Path +out=Path(__file__).resolve().parent +low=[] +while not (out/'monitor.stop').exists(): + mem={l.split(':')[0]:int(l.split()[1]) for l in Path('/proc/meminfo').read_text().splitlines()} + vm=dict(l.split() for l in Path('/proc/vmstat').read_text().splitlines()) + state=subprocess.run(['docker','inspect','--format','{{json .State}}','qwen38-flash-vllm'],capture_output=True,text=True) + status=json.loads(state.stdout) if state.returncode==0 else {} + swapped=(int(vm['pswpin'])+int(vm['pswpout']))*os.sysconf('SC_PAGE_SIZE') + rec={'time':time.time(),'available_kib':mem['MemAvailable'],'swap_used_kib':mem['SwapTotal']-mem['SwapFree'], + 'pswpin':int(vm['pswpin']),'pswpout':int(vm['pswpout']),'status':status.get('Status'), + 'oom_killed':status.get('OOMKilled'),'memory_pressure':Path('/proc/pressure/memory').read_text().strip()} + print(json.dumps(rec),flush=True) + if mem['MemAvailable']<4*1024*1024: + low.append((time.monotonic(),swapped)) + else: + low=[] + if len(low)>=7 and low[-1][0]-low[0][0]>=30 and low[-1][1]-low[0][1]>512*1024**2: + (out/'GUARD_STOP').write_text('MemAvailable below 4 GiB for 30s with >512 MiB swapping') + subprocess.run(['docker','stop','-t','30','qwen38-flash-vllm']) + break + if status.get('OOMKilled'): + (out/'GUARD_STOP').write_text('Docker reported OOMKilled') + break + time.sleep(5) diff --git a/experiments/context262k/monitor.py b/experiments/context262k/monitor.py new file mode 100644 index 0000000..a29368a --- /dev/null +++ b/experiments/context262k/monitor.py @@ -0,0 +1,37 @@ +import json,time,subprocess,os +from pathlib import Path +out=Path(__file__).resolve().parent +low=[] +critical=[] +initial_oom=None +while not (out/'monitor.stop').exists(): + mem={l.split(':')[0]:int(l.split()[1]) for l in Path('/proc/meminfo').read_text().splitlines()} + vm=dict(l.split() for l in Path('/proc/vmstat').read_text().splitlines()) + state=subprocess.run(['docker','inspect','--format','{{json .State}}','qwen38-flash-vllm'],capture_output=True,text=True) + status=json.loads(state.stdout) if state.returncode==0 else {} + if initial_oom is None: initial_oom=int(vm.get('oom_kill',0)) + swapped=(int(vm['pswpin'])+int(vm['pswpout']))*os.sysconf('SC_PAGE_SIZE') + rec={'time':time.time(),'available_kib':mem['MemAvailable'],'swap_used_kib':mem['SwapTotal']-mem['SwapFree'], + 'pswpin':int(vm['pswpin']),'pswpout':int(vm['pswpout']),'status':status.get('Status'), + 'oom_killed':status.get('OOMKilled'),'host_oom_kills':int(vm.get('oom_kill',0)),'memory_pressure':Path('/proc/pressure/memory').read_text().strip()} + print(json.dumps(rec),flush=True) + if mem['MemAvailable']<2*1024*1024: + critical.append(time.monotonic()) + else: + critical=[] + if int(vm.get('oom_kill',0))>initial_oom or (len(critical)>=2 and critical[-1]-critical[0]>=5): + (out/'GUARD_STOP').write_text('Host OOM counter increased or MemAvailable below 2 GiB for 5s') + subprocess.run(['docker','stop','-t','5','qwen38-flash-vllm']) + break + if mem['MemAvailable']<4*1024*1024: + low.append((time.monotonic(),swapped)) + else: + low=[] + if len(low)>=7 and low[-1][0]-low[0][0]>=30 and low[-1][1]-low[0][1]>512*1024**2: + (out/'GUARD_STOP').write_text('MemAvailable below 4 GiB for 30s with >512 MiB swapping') + subprocess.run(['docker','stop','-t','5','qwen38-flash-vllm']) + break + if status.get('OOMKilled'): + (out/'GUARD_STOP').write_text('Docker reported OOMKilled') + break + time.sleep(5) diff --git a/scripts/context-benchmark.py b/scripts/context-benchmark.py new file mode 100644 index 0000000..4aee82a --- /dev/null +++ b/scripts/context-benchmark.py @@ -0,0 +1,198 @@ +"""Generate reproducible long-context retrieval fixtures and test a running vLLM. + +Run inside the model container. Never prints the mounted API key. +""" +import argparse +import hashlib +import json +import random +import signal +import time +import urllib.request +from pathlib import Path + +SNAPSHOT = '/root/.cache/huggingface/hub/models--nvidia--Qwen3.8-Flash-Next-NVFP4/snapshots/fc694b54fb0174e0913e6adf86691ef85a4ead47' +MODEL = 'qwen3.8-flash-next' +MAX_OUTPUT = 2048 +MAX_CONTEXT = 262144 +BASE = 'http://127.0.0.1:8000' + + +def emit(record): + print(json.dumps(record, ensure_ascii=False), flush=True) + + +def digest(value): + return hashlib.sha256(value.encode()).hexdigest() + + +def headers(): + key = Path('/run/secrets/qwen_api_key').read_text().strip() + return {'Authorization': 'Bearer ' + key, 'Content-Type': 'application/json'} + + +def request(path, payload=None, timeout=120): + req = urllib.request.Request(BASE + path, headers=headers(), + data=json.dumps(payload, ensure_ascii=False).encode() if payload is not None else None) + with urllib.request.urlopen(req, timeout=timeout) as response: + return json.load(response) + + +def metrics(): + req = urllib.request.Request(BASE + '/metrics', headers=headers()) + with urllib.request.urlopen(req, timeout=15) as response: + lines = response.read().decode().splitlines() + names = ['vllm:prefix_cache_hits_total', 'vllm:prefix_cache_queries_total', + 'vllm:num_preemptions_total'] + return {name: sum(float(l.split()[-1]) for l in lines if l.split('{')[0].split(' ')[0] == name) + for name in names} + + +def messages(document, project): + prompt = ('以下是一份虚构项目预算档案，请按项目编号检索其中的数据。\n' + document + + '\n档案结束。\n问题：项目编号 ' + project + ' 的核定预算是多少元？只输出数字。') + return [{'role': 'user', 'content': prompt}] + + +def prepare(folder): + from transformers import AutoTokenizer + tok = AutoTokenizer.from_pretrained(SNAPSHOT, local_files_only=True) + folder.mkdir(parents=True, exist_ok=True) + for target in [65536, 131072, 260000]: + for sample in [1, 2]: + name = f'ctx-{target}-{sample}' + rng = random.Random(target * 10 + sample) + amounts = [str(rng.randrange(10000, 99999)) for _ in range(3)] + projects = [f'TARGET-{position}-{sample}' for position in ['FRONT', 'MIDDLE', 'END']] + filler = [f'项目编号 BG-{i:06d}；核定预算 {rng.randrange(10000,99999)} 元；类别 C{i%19:02d}。' for i in range(20000)] + + def build(n): + rows = filler[:n].copy() + positions = [int(n * f) for f in [0.05, 0.5, 0.95]] + for pos, project, amount in zip(positions, projects, amounts): + rows[pos] = f'项目编号 {project}；核定预算 {amount} 元；类别 C20。' + return '文档编号：CTX262K-B-' + name + '\n' + '\n'.join(rows), positions, rows + + low, high = 20, len(filler) + while low < high: + n = (low + high + 1) // 2 + doc, _, _ = build(n) + count = len(tok.apply_chat_template(messages(doc, projects[1]), tokenize=True, add_generation_prompt=True, return_dict=False)) + if count <= target: + low = n + else: + high = n - 1 + doc, positions, rows = build(low) + cases = [] + for label, index in [('front', 0), ('middle', 1), ('end', 2), ('front_repeat', 0)]: + msgs = messages(doc, projects[index]) + count = request('/tokenize', {'model': MODEL, 'messages': msgs, 'add_generation_prompt': True, 'chat_template_kwargs': {'reasoning_effort': 'low'}})['count'] + assert target - 128 <= count <= target + 32, (name, count, target) + assert count + MAX_OUTPUT <= MAX_CONTEXT, (name, count) + cases.append({'case': label, 'messages': msgs, 'expected': amounts[index], + 'prompt_tokens_preflight': count, 'prompt_sha256': digest(msgs[0]['content']), + 'max_tokens': MAX_OUTPUT}) + fractions = [round(len(tok.encode('文档编号：CTX262K-B-'+name+'\n'+'\n'.join(rows[:pos]), add_special_tokens=False)) / cases[0]['prompt_tokens_preflight'], 4) + for pos in positions] + fixture = {'document': name, 'target_input_tokens': target, 'sample': sample, + 'document_sha256': digest(doc), 'needle_token_fractions': fractions, 'cases': cases} + (folder / (name + '.json')).write_text(json.dumps(fixture, ensure_ascii=False)) + emit({'event': 'FIXTURE_READY', **{k:v for k,v in fixture.items() if k != 'cases'}, + 'prompt_tokens': [c['prompt_tokens_preflight'] for c in cases]}) + + +def deadline(signum, frame): + raise TimeoutError('Request exceeded the 1200 second wall-clock limit') + + +def run_case(label, fixture, case, attempt): + before = metrics() + payload = {'model': MODEL, 'messages': case['messages'], 'temperature': 0, 'seed': 42, + 'reasoning_effort': 'low', 'max_tokens': MAX_OUTPUT, + 'stream': True, 'stream_options': {'include_usage': True}} + req = urllib.request.Request(BASE + '/v1/chat/completions', headers=headers(), + data=json.dumps(payload, ensure_ascii=False).encode()) + start = time.monotonic() + first = first_content = last = None + content = reasoning = '' + usage = {} + finish = None + signal.alarm(1200) + try: + with urllib.request.urlopen(req, timeout=1200) as response: + for raw in response: + line = raw.decode().strip() + if not line.startswith('data: ') or line == 'data: [DONE]': + continue + item = json.loads(line[6:]) + if item.get('error'): + raise RuntimeError('Server returned a streaming error') + if item.get('usage'): + usage = item['usage'] + for choice in item.get('choices', []): + delta = choice.get('delta', {}) + answer = delta.get('content') or '' + thought = delta.get('reasoning') or delta.get('reasoning_content') or '' + if answer or thought: + now = time.monotonic() + first = first if first is not None else now + last = now + if answer: + first_content = first_content if first_content is not None else now + content += answer + reasoning += thought + finish = choice.get('finish_reason') or finish + finally: + signal.alarm(0) + if finish is None or not usage: + raise RuntimeError('Incomplete response or missing usage') + after = metrics() + elapsed = time.monotonic() - start + record = {'event': 'RESULT', 'label': label, 'document': fixture['document'], + 'target_input_tokens': fixture['target_input_tokens'], 'sample': fixture['sample'], + 'case': case['case'], 'attempt': attempt, 'expected': case['expected'], + 'content': content, 'correct': content.strip() == case['expected'] and finish == 'stop', + 'finish_reason': finish, 'usage': usage, 'max_tokens': MAX_OUTPUT, + 'prompt_tokens_preflight': case['prompt_tokens_preflight'], 'prompt_sha256': case['prompt_sha256'], + 'output_sha256': digest(reasoning + '\0' + content), + 'ttft_s': round(first-start, 4) if first else None, + 'first_content_s': round(first_content-start, 4) if first_content else None, + 'elapsed_s': round(elapsed, 4), + 'decode_tps_approx': round((usage['completion_tokens']-1)/(last-first), 3) if last and first and last>first else None, + 'metrics_delta': {k: after[k]-before[k] for k in after}} + emit(record) + assert usage['prompt_tokens'] + MAX_OUTPUT <= MAX_CONTEXT + assert usage['prompt_tokens'] == case['prompt_tokens_preflight'], 'Server tokenization differs from preflight' + return record + + +def run(folder, label): + signal.signal(signal.SIGALRM, deadline) + failures = [] + for target in [65536, 131072, 260000]: + emit({'event': 'STAGE_START', 'label': label, 'target_input_tokens': target}) + for sample in [1, 2]: + fixture = json.loads((folder / f'ctx-{target}-{sample}.json').read_text()) + emit({'event': 'DOCUMENT_START', 'label': label, 'document': fixture['document'], + 'document_sha256': fixture['document_sha256'], 'needle_token_fractions': fixture['needle_token_fractions']}) + for case in fixture['cases']: + emit({'event': 'REQUEST_START', 'label': label, 'document': fixture['document'], 'case': case['case']}) + result = run_case(label, fixture, case, 1) + if not result['correct']: + failures.append([fixture['document'], case['case']]) + run_case(label, fixture, case, 2) + emit({'event': 'STAGE_COMPLETED', 'label': label, 'target_input_tokens': target}) + emit({'event': 'CONTEXT_SUITE_COMPLETED', 'label': label, 'first_attempt_failures': failures}) + # Quality failures are reported above, not hidden; infrastructure failures raise. + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('mode', choices=['prepare', 'run']) + parser.add_argument('--data', type=Path, default=Path('/tmp/context262k-data')) + parser.add_argument('--label', default='current') + args = parser.parse_args() + if args.mode == 'prepare': + prepare(args.data) + else: + run(args.data, args.label) diff --git a/scripts/summarize-context.py b/scripts/summarize-context.py new file mode 100644 index 0000000..44d4bc5 --- /dev/null +++ b/scripts/summarize-context.py @@ -0,0 +1,44 @@ +"""Summarize context-benchmark records, keeping first attempts separate from retries.""" +import argparse +import json +import statistics +from pathlib import Path + +parser = argparse.ArgumentParser() +parser.add_argument('files', nargs='+', type=Path) +args = parser.parse_args() +for path in args.files: + rows = [] + for line in path.read_text().splitlines(): + if line.startswith('{'): + rows.append(json.loads(line)) + summary = {'file': path.name, + 'completed': any(r.get('event') == 'CONTEXT_SUITE_COMPLETED' for r in rows), + 'note': 'Request completion is not a host-stability verdict; inspect resource and kernel OOM evidence.', + 'stages': []} + for size in [65536, 131072, 260000]: + results = [r for r in rows if r.get('event') == 'RESULT' and r['target_input_tokens'] == size] + first = [r for r in results if r['attempt'] == 1] + if not first: + if any(r.get('event') == 'STAGE_START' and r['target_input_tokens'] == size for r in rows): + summary['stages'].append({'target_input_tokens': size, 'first_attempts_completed': 0, + 'status': 'started_without_completed_response'}) + continue + cold = [r for r in first if r['case'] == 'front'] + warm = [r for r in first if r['case'] != 'front'] + summary['stages'].append({ + 'target_input_tokens': size, 'first_attempts': len(first), + 'correct_first_attempts': sum(r['correct'] for r in first), + 'truncated_first_attempts': sum(r['finish_reason'] == 'length' for r in first), + 'retries': len(results)-len(first), + 'actual_input_range': [min(r['usage']['prompt_tokens'] for r in first), max(r['usage']['prompt_tokens'] for r in first)], + 'first_document_ttft_s': [r['ttft_s'] for r in cold], + 'reused_prefix_ttft_median_s': statistics.median(r['ttft_s'] for r in warm) if warm else None, + 'first_document_elapsed_s': [r['elapsed_s'] for r in cold], + 'reused_prefix_elapsed_median_s': statistics.median(r['elapsed_s'] for r in warm) if warm else None, + 'max_completion_tokens': max(r['usage']['completion_tokens'] for r in first), + 'max_reasoning_tokens': max(r['usage'].get('completion_tokens_details', {}).get('reasoning_tokens', 0) for r in first), + 'prefix_hit_tokens': sum(r['metrics_delta']['vllm:prefix_cache_hits_total'] for r in results), + 'preemptions': sum(r['metrics_delta']['vllm:num_preemptions_total'] for r in results), + }) + print(json.dumps(summary, ensure_ascii=False, indent=2)) From d7e1e745c31c90fd4c01160b7658e360c0570d51 Mon Sep 17 00:00:00 2001 From: ruihan Date: Fri, 18 Sep 2026 01:22:13 +0800 Subject: [PATCH 4/7] docs(perf): profile CUDA graph coverage and PLE costs on Spark --- README.md | 7 +- docs/cuda-graph-profile.md | 127 ++ .../profile128k/benchmark-summary.json | 394 ++++ docs/results/profile128k/eager/client.jsonl | 39 + .../profile128k/eager/trace.1.summary.json | 1360 ++++++++++++++ .../profile128k/eager/trace.2.summary.json | 823 +++++++++ .../profile128k/eager/trace.3.summary.json | 583 ++++++ .../profile128k/evidence-manifest.json | 60 + docs/results/profile128k/graph/client.jsonl | 39 + .../profile128k/graph/trace.1.summary.json | 1341 ++++++++++++++ .../profile128k/graph/trace.2.summary.json | 852 +++++++++ .../profile128k/graph/trace.3.summary.json | 636 +++++++ docs/results/profile128k/recovery-smoke.log | 6 + docs/results/profile128k/recovery.json | 14 + docs/results/profile128k/resources.jsonl | 1642 +++++++++++++++++ experiments/profile128k/README.md | 38 + experiments/profile128k/client.py | 67 + experiments/profile128k/instrument.py | 29 + experiments/profile128k/monitor.py | 41 + experiments/profile128k/plan.md | 17 + experiments/profile128k/prepare.py | 198 ++ experiments/profile128k/summarize_bench.py | 20 + experiments/profile128k/summarize_trace.py | 52 + 23 files changed, 8384 insertions(+), 1 deletion(-) create mode 100644 docs/cuda-graph-profile.md create mode 100644 docs/results/profile128k/benchmark-summary.json create mode 100644 docs/results/profile128k/eager/client.jsonl create mode 100644 docs/results/profile128k/eager/trace.1.summary.json create mode 100644 docs/results/profile128k/eager/trace.2.summary.json create mode 100644 docs/results/profile128k/eager/trace.3.summary.json create mode 100644 docs/results/profile128k/evidence-manifest.json create mode 100644 docs/results/profile128k/graph/client.jsonl create mode 100644 docs/results/profile128k/graph/trace.1.summary.json create mode 100644 docs/results/profile128k/graph/trace.2.summary.json create mode 100644 docs/results/profile128k/graph/trace.3.summary.json create mode 100644 docs/results/profile128k/recovery-smoke.log create mode 100644 docs/results/profile128k/recovery.json create mode 100644 docs/results/profile128k/resources.jsonl create mode 100644 experiments/profile128k/README.md create mode 100644 experiments/profile128k/client.py create mode 100644 experiments/profile128k/instrument.py create mode 100644 experiments/profile128k/monitor.py create mode 100644 experiments/profile128k/plan.md create mode 100644 experiments/profile128k/prepare.py create mode 100644 experiments/profile128k/summarize_bench.py create mode 100644 experiments/profile128k/summarize_trace.py diff --git a/README.md b/README.md index 2272aa2..1a73f19 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ PLE 查找表通过磁盘映射按需读取；原始模型权重未重新量化。 > 这是针对固定 nightly 的社区适配，非 NVIDIA/vLLM 官方支持方案。 -> 已验证短问答和工具调用；未压测完整 262K 输入。仅适用于此模型、单 GPU / ETP=1。 +> 已验证短问答、工具调用及有限的 128K 单请求检索；接近 260K 已触发主机 OOM。仅适用于此模型、单 GPU / ETP=1。 ## 快速开始 @@ -109,3 +109,8 @@ python3 scripts/summarize-benchmark.py benchmark.jsonl ``` 测试只使用合成输入。方法、首字时间口径与小样本限制见 docs/benchmark-method.md。 + +## CUDA Graph 性能剖析 + +Nsight 已确认图重放生效，短回答约提升 4.5%；长输入预填充未被当前小尺寸图覆盖， +GPU 活动本已接近连续。详见 [性能剖析报告](docs/cuda-graph-profile.md)，包括 PLE 等待/查表区分及原始证据摘要。 diff --git a/docs/cuda-graph-profile.md b/docs/cuda-graph-profile.md new file mode 100644 index 0000000..3c7a093 --- /dev/null +++ b/docs/cuda-graph-profile.md @@ -0,0 +1,127 @@ +# CUDA Graph 性能剖析：图已生效，主要收益受 GPU 工作占比限制 + +2026-09-18，Asia/Shanghai。方案与脚本见 [profile128k](../experiments/profile128k/plan.md)。 + +CUDA Graph 确实被重放，并非参数失效。本轮短回答生成速度中位数由 **29.954 → 31.316 tokens/s（约 +4.5%）**， +与此前约 +4.4% 的复测一致。32K 首次输入首 token 则为 **15.416 → 15.376 秒**，基本不变。 + +原因有两层：当前小尺寸图没有覆盖实际的长输入预填充分块；同时，GPU 时间线上本来就没有很多空隙可消除。 +不能据此认定 SSD、PLE mmap 或 CPU 调度是主要瓶颈，也不能把 CUDA Graph 的小收益解释为 CUDA/GPU 没有发挥作用。 +**eager 和 graph 两组都在使用 GPU 执行 CUDA 内核；本次比较的是是否通过 CUDA Graph 提交这些工作。** + +## 正常计时与正确性 + +- 固定 nightly `0bfc7a15`、相同模型 revision、BF16 KV、MTP=2、前缀缓存、两处 Mamba 修正、GPU memory=0.80。 +- 实验上下文上限 131072；max_num_seqs=4，但测试串行；max_num_batched_tokens=2048。 +- 小尺寸图为 breakable PIECEWISE，capture sizes `[1,2,4,8,12]`。 +- 短回答预算 512，长检索预算 2048，思考与正文共用；temperature=0、seed=42、reasoning_effort=low。 +- 两组各 19 次请求：1 次算术预热、三轮各 5 次计时请求、3 次 trace 请求。38 次均通过各自判据。 + 长检索要求精确答案和正常 stop，短说明仅检查非空及正常 stop，不代表进行了语言质量评估。 +- 19 个对应请求的输入 SHA256 全部一致；服务返回 prompt_tokens 与预检逐条一致。 +- 每轮首次输入使用不同前缀，避免跨轮缓存命中；重复输入紧随首次输入。 +- 下表为三轮中位数，不含预热及 trace 请求。短回答每次均输出 214 tokens；长检索输出约 62–74 tokens，有少量差异。 + +| 指标 | eager | 小尺寸 graph | +|---|---:|---:| +| 短回答解码速度（近似 tokens/s） | 29.954 | 31.316 | +| 短回答首 token（秒，含思考） | 0.2627 | 0.2624 | +| 短回答完整耗时（秒） | 7.374 | 7.064 | +| 8K 首次首 token（秒） | 3.788 | 3.770 | +| 8K 复用首 token（秒） | 0.880 | 0.874 | +| 32K 首次首 token（秒） | 15.416 | 15.376 | +| 32K 复用首 token（秒） | 1.368 | 1.327 | + +原始请求记录和自动汇总见 [results/profile128k](results/profile128k/benchmark-summary.json)。 +计时阶段没有开启 capture，但 Nsight launcher 和 NVTX 包装仍存在；不是完全卸载 profiler 的独立测速。 +执行顺序 eager → graph，未随机化、未清 OS 页缓存，小样本不代表统计显著性或长期稳定性。 + +## 时间线证据 + +Nsight Systems 2025.3.2，CUDA profiler API 控制三个短采样窗口，node 级 graph trace。 +NVTX 临时标记 PLE 同步、CPU 去重、CPU gather、lookup，以及图调度描述符。 +计时阶段和 trace 阶段分开；下表不用于跨模式的端到端性能排名。 + +“GPU 活动占比”是 kernel、memcpy、memset 时间区间并集，除以首次至末次 GPU 活动的跨度。 +它不是 SM 利用率、内存带宽利用率，也不包含完整 HTTP 请求前后的所有工作。 + +| 采样请求 | eager 活动占比 | graph 活动占比 | graph 中来自图节点的 kernel 数量占比 | +|---|---:|---:|---:| +| 短回答 | 93.01% | 95.22% | 82.43% | +| 32K 首次输入及回答 | 97.38% | 97.50% | 44.25% | +| 32K 复用及回答 | 95.22% | 95.29% | 77.15% | + +### 图生效，但覆盖范围有限且分成多段 + +- eager 的三个窗口都没有 graph-node kernel。 +- graph 短回答窗口记录 **4,664 次 `cudaGraphLaunch`，160,160 / 194,303 个 kernel 来自图节点**。 +- 调度记录显示解码 `actual=3` 被 padding 到 4，模式为 PIECEWISE；MTP 路径也存在图重放。 + 同时保留部分 NONE 路径，不能说整次请求被捕获成一张图。 +- 该窗口有 88 次 `actual=3` 的图调度，累计 graph launch 约为其 53 倍。 + 这是目标模型与辅助路径合计的实测比值，说明提交仍分成多段；不是每生成一个 token 就固定调用 53 次。 +- 32K 首次请求中的 **1600 和 742-token 预填充调度均为 NONE**；后续解码才命中图。 +- 日志确认 attention/KV block size 为 1600。虽然批处理上限是 2048，实际主要分块是 1600， + 因此不能仅凭配置里的 2048 推断捕获或预填充形状。 + +### PLE 的“等待时间”不是磁盘读表时间 + +32K 首次输入 trace： + +| 时间项 | eager | graph | +|---|---:|---:| +| 首次至末次 GPU 活动跨度 | 17.458 s | 17.614 s | +| GPU 活动区间并集 | 17.000 s | 17.173 s | +| PLE CPU gather | 0.275 s | 0.292 s | +| PLE CPU 去重（标记中的 np.unique） | 0.019 s | 0.021 s | +| PLE 等待 GPU | 6.389 s | 7.292 s | +| 其中与 GPU 活动重叠 | 6.341 s | 7.233 s | + +CPU wait 与 GPU 工作几乎完全重叠，不是额外叠加在 GPU 计算之后的同等开销。 +不能把所有 API/NVTX/GPU 时长相加，也不能依据同步函数耗时最长就优先优化同步本身。 +图版本 wait 更长不证明 PLE 退化：提交时序和输出长度都不同。 + +本轮 gather 包括页缓存或实际存储读取，未测物理 NVMe I/O；没有证据说明每次都从 SSD 读取， +也没有证据支持将整张 47.7 GiB 表强制驻留能带来显著收益。 +短回答中 CPU gather 约 0.20 秒，相比约 7 秒 GPU 活动跨度亦非主导项。 + +## 决策与后续方向 + +本轮**不扩大图捕获尺寸**。长输入 GPU 活动已接近连续，消除提交间隙的空间有限； +扩大图还可能增加 padding、捕获内存和启动成本。当前证据不足以让它成为稳定基线的优先改动。 +这不等于证明大图一定没有收益；该候选未实测，不报告虚构加速或稳定性结果。 +按方案，只有新候选通过才追加 128K 验证，因此本轮未重跑 128K，也未再次触碰 260K。 + +稳定基线建议仍是：**eager + 前缀缓存 + MTP=2，实际使用限制在 128K 以内**。 +愿意维护实验补丁时，小尺寸图可作为约 4–5% 短回答收益的可选项；目前证据不足以要求默认切换。 +下一个有依据的性能工作应聚焦耗时 GPU 内核及其 GB10 后端：32K trace 的主要内核包含 +CUTLASS 分组量化 GEMM、稀疏注意力和归一化组合；短回答还有显著 BF16 GEMM 时间。 +要判定算力受限还是带宽受限，需要另做针对性内核分析，不能用本轮活动占比替代结论。 + +## 资源与实验修正 + +实验采样最低 MemAvailable **15.748 GiB**，主机 `oom_kill` 从 25 到 25，没有新增 OOM。 +监控每秒采样，低于 1 GiB 立即停止，持续低于 2 GiB 或主机 OOM 增加则停止。 +监控覆盖两组实验，恢复阶段另做最终系统检查；没有为了验证保护阈值主动制造内存压力。 + +首次诊断启动因临时包装函数缺少 PyTorch schema 所需类型注解而失败，尚未加载完成或执行请求。 +自动恢复触发后，修复注解并验证 `torch.library.infer_schema`，随后才运行完整两组。 +该失败属于诊断脚本问题，不是模型/vLLM 的新 bug；失败日志保留在 Spark 的 attempt1 目录。 + +原始 trace 和 SQLite 只保留在 Spark `optimization-results/profile128k/{eager,graph}`，父目录权限 0700。 +它们可能包含进程参数，不提交 Git。仓库的 [证据清单](results/profile128k/evidence-manifest.json) +记录文件大小、SHA256、Nsight 版本、配置要点；仅提交 CUDA/NVTX 摘要、请求结果及资源记录。 + +## 恢复状态 + +2026-09-18 01:21（Asia/Shanghai）已恢复原 eager 镜像，容器 healthy、重启策略 `unless-stopped`。 +算术、中文、工具调用 smoke test 全部通过；原 Compose 与测试前逐字一致，临时 profiler 参数与挂载均已移除。 +可用内存约 18.85 GiB，主机 OOM 计数仍为 25，系统及用户 failed units 均为 0。 +恢复证据见 [recovery.json](results/profile128k/recovery.json) 和 [smoke log](results/profile128k/recovery-smoke.log)。 + +默认配置仍保留原来的 262144 上限，本轮未修改服务容量；该配置值不代表已验证的稳定容量。 +实际使用仍建议限制在 128K 以内，接近 260K 的既有 OOM 结论不变。 + +## 参考 + +- [vLLM profiling 文档](https://docs.vllm.ai/en/latest/contributing/profiling/) +- [NVIDIA Nsight Systems User Guide](https://docs.nvidia.com/nsight-systems/UserGuide/) +- [NVIDIA CUDA Graph 性能问题说明](https://docs.nvidia.com/dl-cuda-graph/latest/troubleshooting/performance-issues/) diff --git a/docs/results/profile128k/benchmark-summary.json b/docs/results/profile128k/benchmark-summary.json new file mode 100644 index 0000000..296ab39 --- /dev/null +++ b/docs/results/profile128k/benchmark-summary.json @@ -0,0 +1,394 @@ +{ + "eager": { + "total_requests": 19, + "correct": 19, + "groups": { + "short": { + "n": 3, + "correct": 3, + "ttft_s": { + "median": 0.2627224419993581, + "values": [ + 0.33354272600263357, + 0.2627224419993581, + 0.26176839799882146 + ] + }, + "elapsed_s": { + "median": 7.373869219998596, + "values": [ + 7.68168595500174, + 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CPU/API/NVTX times overlap GPU work and each other; do not sum them as exclusive costs." +} \ No newline at end of file diff --git a/docs/results/profile128k/recovery-smoke.log b/docs/results/profile128k/recovery-smoke.log new file mode 100644 index 0000000..86abf38 --- /dev/null +++ b/docs/results/profile128k/recovery-smoke.log @@ -0,0 +1,6 @@ +{"models": {"object": "list", "data": [{"id": "qwen3.8-flash-next", "object": "model", "created": 1789665662, "owned_by": "vllm", "root": "/root/.cache/huggingface/hub/models--nvidia--Qwen3.8-Flash-Next-NVFP4/snapshots/fc694b54fb0174e0913e6adf86691ef85a4ead47", "parent": null, "max_model_len": 262144, "permission": [{"id": "modelperm-b7d422ef1689806e", "object": "model_permission", "created": 1789665662, "allow_create_engine": false, "allow_sampling": true, "allow_logprobs": true, "allow_search_indices": false, "allow_view": true, "allow_fine_tuning": false, "organization": "*", "group": null, "is_blocking": false}]}]}} +{"test": "arithmetic", "seconds": 2.43, "content": "\n\n323", "finish_reason": "stop", "usage": {"prompt_tokens": 53, "total_tokens": 109, "completion_tokens": 56, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 50}}} +{"test": "chinese", "seconds": 5.48, "content": "\n\n太阳光包含各种颜色的光（红、橙、黄、绿、蓝、紫等）。当阳光进入大气层时，会与空气中的氮、氧等小分子发生散射。\n\n短波长的光（蓝、紫）比长波长的光（红、橙）更容易被散射，这种现象称为**瑞利散射**。蓝光被散射到四面八方，充满整个天空，所以我们抬头看到的天空就是蓝色的。\n\n（紫光其实散射更强，但人眼对紫光不敏感，且部分紫光被高层大气吸收，所以天空呈现蓝色而非紫色。）", "finish_reason": "stop", "usage": {"prompt_tokens": 49, "total_tokens": 199, "completion_tokens": 150, "prompt_tokens_details": null, "completion_tokens_details": {"reasoning_tokens": 31}}} +{"test": "tool_call", "seconds": 2.0665973159993882, "message": {"role": "assistant", "content": null, "refusal": null, "annotations": null, "audio": null, "function_call": null, "tool_calls": [{"id": "chatcmpl-tool-9e731f967ef9d3d6", "type": "function", "function": {"name": "get_weather", "arguments": "{\"city\": \"上海\"}"}}], "reasoning": "用户要求我调用 get_weather 工具查询上海的天气，并且明确说不要自行编造。我需要直接调用这个工具。\n"}} +TOOL_TEST_PASS +SMOKE_TEST_PASS diff --git a/docs/results/profile128k/recovery.json b/docs/results/profile128k/recovery.json new file mode 100644 index 0000000..72f115c --- /dev/null +++ b/docs/results/profile128k/recovery.json @@ -0,0 +1,14 @@ +{ + "time": "2026-09-18T01:21:40.503543+08:00", + "image": "local/qwen38-flash-spark:prefix-eager-0bfc7a15", + "health": "healthy", + "restart_policy": "unless-stopped", + "compose_unchanged": true, + "profile_mounts": 0, + "profiler_arg_present": false, + "host_oom_kills": 25, + "available_GiB": 18.851730346679688, + "system_failed_units": 0, + "user_failed_units": 0, + "smoke_pass": true +} \ No newline at end of file diff --git a/docs/results/profile128k/resources.jsonl b/docs/results/profile128k/resources.jsonl new file mode 100644 index 0000000..3cec45b --- /dev/null +++ b/docs/results/profile128k/resources.jsonl @@ -0,0 +1,1642 @@ +{"time": 1789663255.455042, 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"status": "running", "oom_killed": false, "host_oom_kills": 25, "memory_pressure": "some avg10=0.00 avg60=0.04 avg300=0.07 total=36767324\nfull avg10=0.00 avg60=0.04 avg300=0.07 total=36740294"} +{"time": 1789664919.8406758, "available_kib": 18005864, "swap_used_kib": 357760, "pswpin": 216612, "pswpout": 298367, "status": "running", "oom_killed": false, "host_oom_kills": 25, "memory_pressure": "some avg10=0.00 avg60=0.04 avg300=0.07 total=36767688\nfull avg10=0.00 avg60=0.04 avg300=0.07 total=36740657"} diff --git a/experiments/profile128k/README.md b/experiments/profile128k/README.md new file mode 100644 index 0000000..95c232f --- /dev/null +++ b/experiments/profile128k/README.md @@ -0,0 +1,38 @@ +# Nsight Systems 诊断脚本 + +固定 nightly `0bfc7a15`、当前 PLE 适配，单 GPU、串行请求。 +方案见 [plan.md](plan.md)。这些脚本是实验工具，不是默认启动入口。 + +- `prepare.py prepare --data ...`：使用正在运行的同一模型生成 8K/32K 固定检索数据并预检 token 数。 +- `instrument.py`：读取同目录 `vllm_ple_mmap.original.py` 和 `cudagraph_utils.original.py`， + 精确匹配源码后生成临时 NVTX 版本；保持算子签名与计算不变。原始文件来自容器，不提交重复副本。 +- `client.py --label eager|graph`：在测试容器内运行，数据挂到 `/profiles/data`，密钥读挂载文件。 + 三轮正常计时和三次独立 trace；所有请求相同，temperature=0、seed=42、reasoning_effort=low。 +- `monitor.py`：在 Spark 宿主机运行，写资源采样到 stdout；以脚本目录中的文件发出停止信号。 + 必须在启动实验前启动监控，并配套退出后恢复原配置的 runner。保护是尽力而为，不能保证拦截瞬时 OOM。 +- `summarize_trace.py ...`：导出 CUDA/NVTX 汇总，不读取进程命令行和环境。 + +Nsight 使用主机安装目录只读挂载到 `/opt/nsight`。临时服务添加： + +```text +--max-model-len 131072 --profiler-config '{"profiler":"cuda"}' +``` + +在原有 `vllm serve ...` 前包裹： + +```text +/opt/nsight/bin/nsys profile --sample=none --trace=cuda,nvtx,osrt \ + --capture-range=cudaProfilerApi --capture-range-end=repeat:3 --kill=none \ + --trace-fork-before-exec=true --cuda-graph-trace=node --force-overwrite=true \ + -o /profiles/