chore: remove legacy benchmark files and scripts
Delete obsolete files that are no longer maintained: - experiments/legacy_bench_results/ (old benchmark reports and summaries) - scripts/benchmark_dspark_0707/ (outdated dspark benchmark scripts) - scripts/start_dsv4_dspark_8card.sh (deprecated deployment script)
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Quantong Qiu
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# Legacy Bench Results Archive
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This directory contains historical benchmark results that were originally stored under `bench_results/` before the `experiments/` layout was introduced.
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All legacy results have been moved here; `bench_results/` itself no longer exists.
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## Contents
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| Directory | Original location | Description |
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|---|---|---|
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| `dspark_grid_20260707-132641/` | `bench_results/dspark_grid_20260707-132641/` | P1/P2/P3 DSpark grid benchmark |
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| `dspark_st_comparison_20260707-150649/` | `bench_results/dspark_st_comparison_20260707-150649/` | `--spec-tokens 3` vs `5` comparison |
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| `dsv4_backend_comparison_20260707/` | `bench_results/dsv4_backend_comparison_20260707/` | SGLang vs vLLM backend comparison raw outputs manifest |
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| `dsv4_comparison_20260705_152221/` | `bench_results/dsv4_comparison_20260705_152221/` | Early vLLM-dspark configuration comparison |
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| `dsv4_flash_dspark_misc/` | `bench_results/dsv4_flash_dspark_misc/` | Early DSV4 DSpark one-off tests |
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| `eagle_grid/` | `bench_results/eagle_grid/` | SGLang EAGLE vs DSpark comparison |
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| `pd_bench/` | `bench_results/pd_bench/` | PD disaggregation benchmark outputs |
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| `sglang_8card_max_throughput_20260705_030839/` | `bench_results/sglang_8card_max_throughput_20260705_030839/` | SGLang 8-card max throughput scan |
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| `sglang_8card_systematic_20260704_120819/` | `bench_results/sglang_8card_systematic_20260704_120819/` | Early SGLang systematic scan |
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| `sglang_misc/` | `bench_results/sglang_misc/` | Miscellaneous SGLang outputs |
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| `vllm_dspark_qwen3_20260705_121046/` | `bench_results/vllm_dspark_qwen3_20260705_121046/` | Qwen3 DSpark tests |
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| `vllm_dspark_qwen3_20260705_121218/` | `bench_results/vllm_dspark_qwen3_20260705_121218/` | Qwen3 DSpark tests |
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| `vllm_dspark_qwen3_20260705_121256/` | `bench_results/vllm_dspark_qwen3_20260705_121256/` | Qwen3 DSpark tests |
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> Note: Some internal file paths (e.g. `result_file` in `summary.json`, hardcoded paths in old scripts) still reference the original `bench_results/` locations. These are kept as-is for historical accuracy.
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# vllm-dspark Benchmark 结果评估
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> 对应结果：`/data/user1/yy/experiments/legacy_bench_results/dspark_grid_20260707-132641`
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> 运行时间：2026-07-07
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> 硬件：8× NVIDIA H200 143GB
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> 模型：`/data/models/DeepSeek-V4-Flash-DSpark`
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> 服务配置：TP=8，FP8 KV cache，`--spec-method dspark --spec-tokens 5`，block-size=256，max-num-seqs=256
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> 压测工具：`sglang.bench_serving --backend vllm`
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---
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## 1. 总体结论
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本次 grid 测试覆盖了从轻量 chat 到超长上下文、从重 decode 到高并发压测的多种场景。整体上看：
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- **DSpark 在短输入、中高并发场景下表现优秀**，`chat_short` 在并发 64 时达到约 9580 tok/s，`stress_standard` 在并发 128 时达到约 17465 tok/s。
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- **低并发下 DSpark 的 draft 开销明显**，部分场景单并发延迟和吞吐都不如预期。
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- **超长上下文场景存在明显的吞吐拐点**，`long_rag`（32K 输入）在并发 4 达到峰值后，并发 8 吞吐腰斩。
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- **延迟长尾（P95/P99）普遍较重**，提示调度、内存或投机解码验证阶段存在抖动。
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---
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## 2. 异常点分析
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### 2.1 低并发（c=1）下 DSpark 收益被开销吃掉
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以 `chat_standard`（input=1000, output=256）为例：
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| 并发 | req/s | mean E2E(ms) | P99 E2E(ms) | mean TTFT(ms) | P99 TTFT(ms) |
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|---|---:|---:|---:|---:|---:|
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| 1 | 0.49 | 2040.65 | 10715.53 | 1412.48 | 8430.71 |
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| 8 | 4.35 | 1819.35 | 15165.63 | 327.62 | 4404.92 |
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| 16 | 8.32 | 1885.15 | 10491.31 | 443.50 | 8567.07 |
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| 32 | 10.00 | 3156.73 | 12936.08 | 560.77 | 7129.66 |
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- **单并发吞吐仅 0.49 req/s**，远低于无投机基线可预期的水平。
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- **P99 TTFT 高达 8.4s**，说明单请求首次预填充极不稳定，可能是 draft 验证/编译缓存未命中或 warmup 不充分导致。
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- 并发提升到 8 后，TTFT 均值下降到 327ms，说明 DSpark 的 draft 计算需要 batch 才能摊薄。
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**判断**：这是 DSpark 的典型特征——低并发下 draft 固定开销无法被摊薄，导致延迟尾和单请求吞吐都较差。
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### 2.2 超长上下文出现吞吐断崖
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`long_rag`（input=32000, output=512）：
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| 并发 | Total tok/s | mean TTFT(ms) | P99 TTFT(ms) |
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|---|---:|---:|---:|
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| 1 | 16104.98 | 549.72 | 1163.96 |
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| 2 | 43215.62 | 192.57 | 409.32 |
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| 4 | **70040.54** | 191.57 | 427.69 |
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| 8 | 34141.70（↓51%） | 930.98 | 2742.48 |
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`long_context_probe`（input=16000, output=512）也有类似模式：
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| 并发 | Total tok/s | mean TTFT(ms) |
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|---|---:|---:|
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| 1 | 11051.09 | 316.59 |
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| 2 | 26862.06 | 110.65 |
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| 4 | 43042.77 | 127.38 |
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| 8 | 29255.46（↓32%） | 453.93 |
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| 16 | 43292.68 | 444.14 |
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**判断**：
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- 32K/16K 长 prefill 在并发 4 时达到最佳，继续加并发反而下降，可能与 **KV cache 显存带宽瓶颈**、**prefill 阶段 scheduling 阻塞** 或 **hybrid KV cache manager 的换入换出** 有关。
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- `long_rag` 在 c=8 时 P99 TTFT 暴涨到 2.7s，进一步印证内存/调度压力。
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### 2.3 延迟长尾普遍偏重
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几乎所有场景的 P99 E2E 都是 mean E2E 的 2~4 倍：
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| 场景 | 并发 | mean E2E | P99 E2E | 倍数 |
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|---|---:|---:|---:|---:|
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| chat_standard | 32 | 3156.73 | 12936.08 | 4.1× |
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| generation_standard | 32 | 2908.89 | 6938.03 | 2.4× |
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| summarization | 32 | 5587.21 | 14541.75 | 2.6× |
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| decode_heavy | 32 | 5562.57 | 12822.35 | 2.3× |
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| stress_standard | 128 | 4417.43 | 14850.43 | 3.4× |
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**判断**：投机解码的 draft 验证失败会导致 fall back 到逐个 target 前向，造成明显的延迟毛刺；另外 vLLM v1 引擎的 scheduling 在高并发下也可能产生队列等待。
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### 2.4 高并发下 TPOT/ITL 增长较快
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`stress_standard`（input=1000, output=256）：
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| 并发 | mean TPOT | P95 TPOT | P99 TPOT | mean ITL |
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|---|---:|---:|---:|---:|
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| 96 | 29.46 | 67.30 | 123.42 | 113.64 |
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| 128 | 35.20 | 76.85 | 141.17 | 136.57 |
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TPOT 从 29ms 升到 35ms，P99 超过 140ms，说明 decode 阶段在极限并发下已经接近饱和。
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---
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## 3. 可重点优化的方向
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### 3.1 按并发动态调整 `--spec-tokens`
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参考历史对比数据（`dsv4_inference_comparison_report.md`）：
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| spec-tokens | 低并发（c=1） | 高并发（c=64） |
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| 3 | 1.03 req/s, 254 tok/s | **14.53 req/s, 3507 tok/s** |
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| 5 | **1.07 req/s, 257 tok/s** | 9.36 req/s, 2210 tok/s |
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| 7 | 1.03 req/s, 251 tok/s | 9.13 req/s, 2218 tok/s |
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当前部署固定使用 `--spec-tokens 5`：
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- 对低并发（c=1~16）相对友好；
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- 对高并发（c≥64）不是最优，st=3 在高并发下接受率更高、验证开销更小。
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**建议**：
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- 高并发服务场景：改用 `--spec-tokens 3`。
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- 若负载混合，可考虑不同 endpoint/队列按并发分桶，或测试 st=3/st=5 在本 grid 下的完整表现。
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### 3.2 长上下文场景优化
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针对 `long_rag`（32K）和 `long_context_probe`（16K）的吞吐断崖：
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1. **KV cache dtype 对比测试**：当前是 FP8，可测试 BF16 是否改善长上下文稳定性和吞吐。
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2. **block-size 调整**：当前 256，可尝试 128 或 64 看是否减少内存碎片。
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3. **hybrid KV cache manager**：当前 `--no-disable-hybrid-kv-cache-manager`，可对比关闭后的表现。
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4. **限制长上下文并发**：若业务允许，对 32K 输入设置更低的 `max-num-seqs` 或独立队列，避免 c=8 后的内存带宽瓶颈。
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5. **attention backend**：可测试 `FLASHINFER_MLA_SPARSE_DSV4`（脚本 `start_dsv4_dspark_8card_flashinfer.sh`）在长上下文下的表现。
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### 3.3 降低延迟长尾
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1. **增加 warmup**：当前仅 10 条 warmup。DSpark 的 JIT 编译/算子 warmup 在首次运行时较重，建议增加到 50~100 条或先跑一轮 discard。
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2. **compilation cache 持久化**：确认 `TORCH_EXTENSIONS_DIR`、`VLLM_CACHE_ROOT` 等环境变量已设置并复用，避免每次重启重新编译 deep_gemm/tilelang。
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3. **调度参数**：尝试调整 `--max-num-batched-tokens`、`--max-num-seqs`、chunked prefill 相关参数，减少队列等待。
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4. **P99 TTFT 抖动**：高并发下 P99 TTFT 高通常与 prefill batching 有关，可尝试限制 prefill 阶段 batch 大小或启用 prompt chunked prefill。
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### 3.4 低并发场景是否需要 DSpark
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对于 `chat_standard` c=1：
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- 当前 0.49 req/s、P99 E2E 10.7s 的服务质量较差。
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- 若业务存在大量单用户/低并发请求，可考虑：
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- 部署无投机基线 vLLM 专门服务低并发流量；
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- 或设置最小 batch 阈值，累积少量请求后再用 DSpark 处理。
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### 3.5 对比 baseline 与 SGLang
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本次测试只有 DSpark 单一配置，建议补充：
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1. **vLLM-dspark 无投机基线**：用同样的 `sglang.bench_serving --backend vllm` 测一遍相同 grid，计算真实加速比。
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2. **SGLang EAGLE**：在相同 grid 下复测，验证 DSpark 在不同场景下的相对优势。
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3. **不同 `--spec-tokens` 的完整 grid**：st=3 和 st=5 各跑一遍，绘制并发-吞吐曲线。
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---
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## 4. 推荐下一步实验
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| 优先级 | 实验 | 预期收益 |
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| P0 | 高并发改用 `--spec-tokens 3` | 提升 c≥64 场景吞吐 20~50% |
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| P0 | 长上下文（16K/32K）对比 BF16 KV cache | 改善吞吐断崖和 P99 TTFT |
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| P1 | 增加 warmup / 预热缓存 | 降低 P99 TTFT 和首请求延迟 |
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| P1 | 测试 `FLASHINFER_MLA_SPARSE_DSV4` backend | 可能提升长上下文吞吐 |
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| P2 | 跑无投机基线 grid | 获得真实加速比 |
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| P2 | 对比 st=3/st=5 完整曲线 | 找到最佳投机长度配置 |
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## 5. 结论
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当前 DSpark 部署在 **短输入、中高并发** 场景下已经展现出明显优势，但在 **低并发** 和 **超长上下文** 场景下存在明显短板。最优先的优化是：
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1. 高并发服务改用 `--spec-tokens 3`；
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2. 针对 16K/32K 长上下文做 KV cache dtype 和 attention backend 的调优；
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3. 补充 baseline 测试，量化 DSpark 的真实收益。
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# vllm-dspark Benchmark Grid Report
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- Result root: `/data/user1/yy/experiments/legacy_bench_results/dspark_grid_20260707-132641`
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- Model: `/data/models/DeepSeek-V4-Flash-DSpark`
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- Backend: vllm-dspark (TP=8, FP8 KV cache, spec-method=dspark, spec-tokens=5)
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- Benchmark client: `sglang.bench_serving --backend vllm`
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## p1_quick
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### chat_standard (20260707-132641)
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| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
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|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
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| 1 | 65.33 | 32 | 0.49 | 195.58 | 71.28 | 266.86 | 2040.65 | 8241.20 | 10715.53 | 1412.48 | 6903.30 | 8430.71 | 6.41 | 32.72 | 51.29 | 16.61 |
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| 8 | 29.42 | 128 | 4.35 | 2130.85 | 569.52 | 2700.37 | 1819.35 | 8891.79 | 15165.63 | 327.62 | 379.39 | 4404.92 | 11.58 | 51.06 | 82.44 | 51.00 |
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| 16 | 30.76 | 256 | 8.32 | 4144.89 | 1144.74 | 5289.63 | 1885.15 | 8902.10 | 10491.31 | 443.50 | 2779.49 | 8567.07 | 12.10 | 23.11 | 83.70 | 46.18 |
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| 32 | 51.18 | 512 | 10.00 | 5017.88 | 1331.65 | 6349.53 | 3156.73 | 10438.92 | 12936.08 | 560.77 | 5750.71 | 7129.66 | 22.09 | 66.23 | 171.57 | 85.81 |
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### generation_standard (20260707-132641)
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| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
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|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
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| 1 | 36.24 | 32 | 0.88 | 352.60 | 479.25 | 831.85 | 1131.63 | 1979.98 | 2576.42 | 66.57 | 141.80 | 177.56 | 1.94 | 2.85 | 2.88 | 8.50 |
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| 8 | 27.43 | 128 | 4.67 | 2285.21 | 2362.35 | 4647.56 | 1649.16 | 3162.33 | 3844.70 | 80.88 | 157.80 | 160.50 | 3.23 | 5.31 | 7.09 | 14.77 |
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| 16 | 35.39 | 256 | 7.23 | 3602.52 | 3654.79 | 7257.31 | 2133.90 | 4187.67 | 5500.20 | 88.78 | 169.39 | 199.78 | 4.37 | 6.97 | 11.81 | 19.28 |
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| 32 | 48.16 | 512 | 10.63 | 5333.41 | 5371.85 | 10705.25 | 2908.89 | 5849.64 | 6938.03 | 112.84 | 211.64 | 405.78 | 5.84 | 9.30 | 12.47 | 25.89 |
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|
||||||
|
|
||||||
### summarization (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 1 | 33.63 | 32 | 0.95 | 4247.40 | 516.49 | 4763.90 | 1049.88 | 1783.33 | 1848.92 | 178.56 | 292.66 | 314.18 | 1.60 | 1.78 | 2.41 | 8.62 |
|
|
||||||
| 4 | 10.99 | 32 | 2.91 | 13000.06 | 1580.84 | 14580.90 | 1301.15 | 2238.30 | 2324.70 | 101.68 | 187.78 | 188.57 | 2.18 | 2.47 | 3.49 | 11.86 |
|
|
||||||
| 8 | 33.60 | 128 | 3.81 | 15369.23 | 1908.96 | 17278.19 | 2072.22 | 3978.11 | 4531.26 | 214.01 | 502.25 | 630.15 | 3.70 | 5.52 | 7.98 | 19.60 |
|
|
||||||
| 16 | 46.93 | 256 | 5.45 | 22698.72 | 2761.38 | 25460.10 | 2840.57 | 6243.39 | 7179.11 | 235.75 | 552.81 | 740.45 | 5.45 | 9.62 | 12.75 | 27.77 |
|
|
||||||
|
|
||||||
## p2_core
|
|
||||||
|
|
||||||
### chat_short (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 1 | 11.39 | 32 | 2.81 | 717.30 | 408.87 | 1126.18 | 355.00 | 685.00 | 815.55 | 48.57 | 105.64 | 105.97 | 2.07 | 3.10 | 3.69 | 8.51 |
|
|
||||||
| 8 | 10.62 | 128 | 12.06 | 3320.19 | 1591.90 | 4912.09 | 647.05 | 1401.33 | 1648.15 | 78.91 | 151.78 | 155.67 | 4.59 | 7.20 | 12.54 | 17.85 |
|
|
||||||
| 16 | 14.89 | 256 | 17.19 | 4533.71 | 2394.66 | 6928.36 | 902.72 | 1879.32 | 2211.23 | 95.95 | 165.09 | 217.14 | 6.19 | 10.26 | 15.12 | 23.65 |
|
|
||||||
| 32 | 24.90 | 512 | 20.56 | 5458.26 | 2723.85 | 8182.10 | 1508.89 | 3076.09 | 3881.52 | 161.04 | 292.00 | 383.41 | 10.80 | 18.19 | 25.45 | 41.86 |
|
|
||||||
| 64 | 21.27 | 512 | 24.07 | 6391.14 | 3189.39 | 9580.52 | 2547.81 | 5606.07 | 6543.97 | 268.68 | 543.21 | 588.63 | 18.24 | 30.39 | 39.57 | 70.29 |
|
|
||||||
|
|
||||||
### chat_standard (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 64 | 19.87 | 512 | 25.76 | 12922.80 | 3429.47 | 16352.28 | 2380.94 | 4838.43 | 6315.98 | 291.17 | 794.65 | 947.26 | 16.87 | 30.96 | 42.55 | 68.46 |
|
|
||||||
|
|
||||||
### generation_standard (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 64 | 43.59 | 512 | 11.75 | 5892.47 | 5934.93 | 11827.40 | 5136.14 | 10244.70 | 12575.20 | 209.70 | 536.96 | 606.58 | 10.33 | 17.41 | 21.20 | 45.47 |
|
|
||||||
|
|
||||||
### long_context_probe (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 1 | 23.79 | 32 | 1.34 | 10684.78 | 366.30 | 11051.09 | 742.62 | 1237.28 | 1418.82 | 316.59 | 604.23 | 617.85 | 1.54 | 1.72 | 1.85 | 8.53 |
|
|
||||||
| 2 | 9.79 | 32 | 3.27 | 25971.67 | 890.38 | 26862.06 | 604.82 | 1055.84 | 1116.41 | 110.65 | 194.62 | 206.64 | 1.77 | 2.04 | 2.13 | 9.90 |
|
|
||||||
| 4 | 6.11 | 32 | 5.24 | 41616.05 | 1426.72 | 43042.77 | 741.94 | 1262.13 | 1447.68 | 127.38 | 222.70 | 235.35 | 2.26 | 2.69 | 3.69 | 12.39 |
|
|
||||||
| 8 | 36.07 | 128 | 3.55 | 28357.82 | 897.65 | 29255.46 | 2232.38 | 4946.97 | 5371.22 | 453.93 | 1089.99 | 1182.47 | 7.06 | 13.03 | 20.03 | 38.51 |
|
|
||||||
| 16 | 49.08 | 256 | 5.22 | 41910.25 | 1382.43 | 43292.68 | 3028.26 | 7651.28 | 9531.10 | 444.14 | 1212.92 | 1368.04 | 10.00 | 20.71 | 30.74 | 53.07 |
|
|
||||||
|
|
||||||
### rag_medium (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 1 | 18.21 | 32 | 1.76 | 3568.86 | 494.63 | 4063.49 | 568.24 | 966.60 | 1201.99 | 110.94 | 151.11 | 154.78 | 1.63 | 1.95 | 2.50 | 8.60 |
|
|
||||||
| 4 | 6.31 | 32 | 5.07 | 10300.85 | 1427.65 | 11728.51 | 751.19 | 1259.07 | 1593.86 | 92.09 | 161.03 | 187.47 | 2.34 | 3.25 | 4.02 | 12.33 |
|
|
||||||
| 8 | 19.13 | 128 | 6.69 | 14147.80 | 1692.32 | 15840.12 | 1166.59 | 2199.67 | 2982.23 | 148.09 | 253.10 | 350.67 | 4.30 | 6.78 | 9.92 | 21.06 |
|
|
||||||
| 16 | 25.89 | 256 | 9.89 | 20561.53 | 2622.36 | 23183.89 | 1587.09 | 3316.74 | 4019.02 | 164.29 | 303.79 | 389.34 | 5.50 | 8.80 | 14.38 | 27.81 |
|
|
||||||
| 32 | 39.47 | 512 | 12.97 | 26523.95 | 3335.30 | 29859.24 | 2415.10 | 5069.76 | 6195.14 | 227.81 | 444.76 | 510.18 | 8.64 | 13.54 | 20.29 | 44.49 |
|
|
||||||
|
|
||||||
### summarization (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 32 | 91.68 | 512 | 5.58 | 22445.97 | 2821.71 | 25267.67 | 5587.21 | 11177.97 | 12257.18 | 430.38 | 967.32 | 2238.28 | 10.56 | 16.13 | 23.53 | 54.40 |
|
|
||||||
|
|
||||||
## p3_extension
|
|
||||||
|
|
||||||
### decode_heavy (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 1 | 56.16 | 32 | 0.57 | 145.47 | 546.26 | 691.72 | 1754.17 | 2994.25 | 3175.70 | 52.30 | 107.66 | 108.83 | 1.88 | 2.68 | 3.14 | 8.53 |
|
|
||||||
| 8 | 54.33 | 128 | 2.36 | 648.72 | 2526.47 | 3175.19 | 3302.64 | 6408.14 | 7166.98 | 74.21 | 143.24 | 149.17 | 3.05 | 4.37 | 4.79 | 14.12 |
|
|
||||||
| 16 | 65.74 | 256 | 3.89 | 1026.90 | 4012.07 | 5038.96 | 3934.72 | 7216.03 | 9638.96 | 81.02 | 165.95 | 197.11 | 3.98 | 5.82 | 7.21 | 17.79 |
|
|
||||||
| 32 | 92.36 | 512 | 5.54 | 1471.70 | 5761.81 | 7233.51 | 5562.57 | 10498.47 | 12822.35 | 107.17 | 234.99 | 426.35 | 5.40 | 8.14 | 9.03 | 24.38 |
|
|
||||||
|
|
||||||
### long_rag (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 1 | 30.84 | 32 | 1.04 | 15823.63 | 281.34 | 16104.98 | 962.70 | 1688.61 | 1788.29 | 549.72 | 1124.54 | 1163.96 | 1.49 | 1.72 | 1.82 | 8.52 |
|
|
||||||
| 2 | 11.49 | 32 | 2.78 | 42460.67 | 754.95 | 43215.62 | 708.16 | 1202.62 | 1253.12 | 192.57 | 352.96 | 409.32 | 2.05 | 2.37 | 6.62 | 10.76 |
|
|
||||||
| 4 | 7.09 | 32 | 4.51 | 68816.98 | 1223.56 | 70040.54 | 854.49 | 1516.80 | 1562.42 | 191.57 | 426.45 | 427.69 | 2.62 | 3.20 | 7.33 | 13.69 |
|
|
||||||
| 8 | 60.48 | 128 | 2.12 | 33604.85 | 536.85 | 34141.70 | 3732.80 | 8420.06 | 9705.36 | 930.98 | 2125.14 | 2742.48 | 11.12 | 22.70 | 39.17 | 61.41 |
|
|
||||||
|
|
||||||
### stress_generation (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 96 | 81.20 | 768 | 9.46 | 4842.45 | 4635.63 | 9478.08 | 9772.15 | 20945.93 | 25314.28 | 326.63 | 782.50 | 795.30 | 20.35 | 33.33 | 42.50 | 88.91 |
|
|
||||||
| 128 | 92.26 | 1024 | 11.10 | 5587.83 | 5514.57 | 11102.40 | 11012.89 | 22012.44 | 28276.20 | 360.94 | 812.12 | 1024.93 | 22.53 | 35.59 | 42.09 | 99.75 |
|
|
||||||
|
|
||||||
### stress_standard (20260707-132641)
|
|
||||||
|
|
||||||
| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |
|
|
||||||
|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|
|
|
||||||
| 96 | 30.50 | 768 | 25.18 | 12891.05 | 3175.02 | 16066.07 | 3689.97 | 9383.09 | 11958.09 | 435.22 | 1225.11 | 2581.90 | 29.46 | 67.30 | 123.42 | 113.64 |
|
|
||||||
| 128 | 36.86 | 1024 | 27.78 | 13985.42 | 3479.38 | 17464.80 | 4417.43 | 11825.67 | 14850.43 | 456.55 | 1032.19 | 1922.87 | 35.20 | 76.85 | 141.17 | 136.57 |
|
|
||||||
|
|
||||||
@ -1,129 +0,0 @@
|
|||||||
# vllm-dspark `--spec-tokens` 对比报告
|
|
||||||
|
|
||||||
- 结果目录：`/data/user1/yy/bench_results/dspark_st_comparison_20260707-150649`
|
|
||||||
- 模型：`/data/models/DeepSeek-V4-Flash-DSpark`
|
|
||||||
- 后端：vllm-dspark (TP=8, FP8 KV cache)
|
|
||||||
- 对比参数：`--spec-tokens 3` vs `--spec-tokens 5`
|
|
||||||
- Warmup：100 条
|
|
||||||
- 压测客户端：`sglang.bench_serving --backend vllm`
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 核心指标对比
|
|
||||||
|
|
||||||
| Scenario | Concurrency | Spec | Duration(s) | Req/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P99 TPOT(ms) |
|
|
||||||
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
|
|
||||||
| chat_short | 1 | 3 | 12.80 | 2.50 | 363.90 | 1002.30 | 398.97 | 757.11 | 948.62 | 43.97 | 68.89 | 2.42 | 3.76 |
|
|
||||||
| chat_short | 1 | 5 | 11.72 | 2.73 | 397.37 | 1094.51 | 365.29 | 743.89 | 782.98 | 64.42 | 174.36 | 2.06 | 3.40 |
|
|
||||||
| chat_short | 8 | 3 | 11.08 | 11.55 | 1525.06 | 4705.85 | 672.66 | 1269.82 | 1552.65 | 76.20 | 221.18 | 4.67 | 8.43 |
|
|
||||||
| chat_short | 8 | 5 | 11.78 | 10.87 | 1434.62 | 4426.78 | 718.40 | 1511.14 | 2008.00 | 94.97 | 220.85 | 4.95 | 9.93 |
|
|
||||||
| chat_short | 16 | 3 | 21.82 | 11.73 | 1634.03 | 4727.68 | 1333.99 | 2611.76 | 2921.26 | 151.65 | 337.53 | 8.85 | 17.66 |
|
|
||||||
| chat_short | 16 | 5 | 16.42 | 15.59 | 2171.58 | 6282.94 | 997.37 | 2147.86 | 2496.56 | 107.56 | 240.46 | 7.09 | 21.93 |
|
|
||||||
| chat_short | 32 | 3 | 24.62 | 20.79 | 2754.96 | 8275.55 | 1497.40 | 2944.48 | 3658.90 | 144.47 | 453.29 | 10.72 | 23.97 |
|
|
||||||
| chat_short | 32 | 5 | 26.32 | 19.45 | 2577.42 | 7742.25 | 1600.40 | 3403.00 | 4521.14 | 169.13 | 402.36 | 11.39 | 27.82 |
|
|
||||||
| chat_short | 64 | 3 | 21.66 | 23.63 | 3131.20 | 9405.73 | 2595.90 | 5230.98 | 6258.25 | 251.84 | 509.86 | 18.60 | 40.84 |
|
|
||||||
| chat_short | 64 | 5 | 21.26 | 24.09 | 3191.43 | 9586.66 | 2555.12 | 5387.59 | 6559.69 | 274.71 | 610.40 | 18.47 | 42.08 |
|
|
||||||
| chat_standard | 64 | 3 | 20.73 | 24.70 | 3288.58 | 15680.46 | 2483.46 | 4798.63 | 5693.88 | 271.41 | 947.15 | 17.93 | 46.05 |
|
|
||||||
| chat_standard | 64 | 5 | 20.91 | 24.49 | 3259.71 | 15542.80 | 2512.52 | 5394.43 | 6023.55 | 300.81 | 932.60 | 17.83 | 44.47 |
|
|
||||||
| decode_heavy | 1 | 3 | 68.78 | 0.47 | 446.03 | 564.81 | 2148.54 | 3712.03 | 3835.51 | 38.60 | 41.18 | 2.28 | 3.03 |
|
|
||||||
| decode_heavy | 1 | 5 | 57.70 | 0.55 | 531.76 | 673.37 | 1802.00 | 2957.04 | 3676.66 | 59.06 | 114.78 | 1.92 | 3.03 |
|
|
||||||
| decode_heavy | 32 | 3 | 96.14 | 5.33 | 5535.64 | 6949.57 | 5809.29 | 10830.76 | 12546.55 | 86.83 | 382.71 | 5.62 | 8.11 |
|
|
||||||
| decode_heavy | 32 | 5 | 92.75 | 5.52 | 5737.73 | 7203.28 | 5595.02 | 10412.11 | 12522.00 | 107.12 | 331.08 | 5.43 | 9.15 |
|
|
||||||
| generation_standard | 64 | 3 | 38.58 | 13.27 | 6705.88 | 13363.77 | 4542.22 | 8273.38 | 9355.10 | 190.34 | 813.58 | 9.00 | 15.36 |
|
|
||||||
| generation_standard | 64 | 5 | 43.39 | 11.80 | 5961.40 | 11880.13 | 5075.77 | 9863.55 | 11533.47 | 216.87 | 626.93 | 10.09 | 20.90 |
|
|
||||||
| long_context_probe | 1 | 3 | 26.10 | 1.23 | 333.92 | 10074.03 | 815.18 | 1382.34 | 1556.00 | 265.28 | 558.73 | 2.02 | 2.19 |
|
|
||||||
| long_context_probe | 1 | 5 | 22.22 | 1.44 | 392.31 | 11835.70 | 693.30 | 1172.84 | 1404.17 | 267.61 | 561.65 | 1.53 | 1.88 |
|
|
||||||
| long_context_probe | 4 | 3 | 7.34 | 4.36 | 1187.03 | 35811.63 | 888.88 | 1550.70 | 1611.29 | 125.51 | 282.82 | 2.77 | 3.23 |
|
|
||||||
| long_context_probe | 4 | 5 | 6.59 | 4.85 | 1322.32 | 39893.35 | 806.55 | 1390.87 | 1500.22 | 143.42 | 236.41 | 2.41 | 3.54 |
|
|
||||||
| long_context_probe | 8 | 3 | 38.61 | 3.31 | 838.49 | 27327.58 | 2387.05 | 5028.60 | 5667.97 | 406.10 | 1093.54 | 7.74 | 17.09 |
|
|
||||||
| long_context_probe | 8 | 5 | 35.94 | 3.56 | 900.89 | 29361.32 | 2223.29 | 4693.10 | 5345.72 | 425.03 | 1241.57 | 7.08 | 19.83 |
|
|
||||||
| rag_medium | 1 | 3 | 22.31 | 1.43 | 403.86 | 3317.84 | 696.09 | 1165.77 | 1315.00 | 105.51 | 157.60 | 2.10 | 2.81 |
|
|
||||||
| rag_medium | 1 | 5 | 18.16 | 1.76 | 496.09 | 4075.47 | 566.58 | 968.90 | 1134.15 | 110.34 | 156.35 | 1.64 | 2.34 |
|
|
||||||
| rag_medium | 8 | 3 | 20.57 | 6.22 | 1573.71 | 14730.01 | 1251.63 | 2472.54 | 2796.12 | 127.54 | 309.65 | 4.46 | 7.41 |
|
|
||||||
| rag_medium | 8 | 5 | 19.13 | 6.69 | 1692.13 | 15838.39 | 1175.26 | 2263.59 | 3106.42 | 154.24 | 352.19 | 4.14 | 8.36 |
|
|
||||||
| rag_medium | 32 | 3 | 45.02 | 11.37 | 2923.96 | 26176.72 | 2756.39 | 5579.00 | 6675.25 | 215.15 | 477.20 | 10.00 | 19.84 |
|
|
||||||
| rag_medium | 32 | 5 | 42.98 | 11.91 | 3062.92 | 27420.79 | 2626.21 | 5182.78 | 6873.72 | 253.09 | 522.80 | 9.17 | 18.55 |
|
|
||||||
| stress_standard | 64 | 3 | 21.35 | 23.99 | 3193.19 | 15225.66 | 2569.66 | 5006.16 | 5831.81 | 261.98 | 710.57 | 18.02 | 36.01 |
|
|
||||||
| stress_standard | 64 | 5 | 21.75 | 23.54 | 3133.74 | 14942.17 | 2621.52 | 5431.08 | 7161.16 | 305.11 | 734.92 | 18.42 | 41.01 |
|
|
||||||
| stress_standard | 96 | 3 | 20.36 | 37.72 | 4756.72 | 24069.73 | 2433.73 | 4827.07 | 6001.29 | 272.61 | 749.99 | 18.04 | 38.52 |
|
|
||||||
| stress_standard | 96 | 5 | 25.33 | 30.32 | 3823.91 | 19349.55 | 3032.19 | 6272.09 | 8124.32 | 339.22 | 793.18 | 23.06 | 51.46 |
|
|
||||||
| stress_standard | 128 | 3 | 21.58 | 47.45 | 5942.48 | 29828.36 | 2581.42 | 5162.16 | 6222.01 | 298.69 | 970.61 | 19.09 | 40.93 |
|
|
||||||
| stress_standard | 128 | 5 | 28.36 | 36.11 | 4522.53 | 22700.92 | 3405.74 | 7240.13 | 9176.77 | 382.02 | 1087.53 | 25.74 | 57.83 |
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 吞吐 winner 统计
|
|
||||||
|
|
||||||
| Scenario | Concurrency | Winner (Total tok/s) | st=3 Total tok/s | st=5 Total tok/s | 提升 |
|
|
||||||
|---|---:|---:|---:|---:|---:|
|
|
||||||
| chat_short | 1 | st=5 | 1002.30 | 1094.51 | 9.20% |
|
|
||||||
| chat_short | 8 | st=3 | 4705.85 | 4426.78 | 6.30% |
|
|
||||||
| chat_short | 16 | st=5 | 4727.68 | 6282.94 | 32.90% |
|
|
||||||
| chat_short | 32 | st=3 | 8275.55 | 7742.25 | 6.89% |
|
|
||||||
| chat_short | 64 | st=5 | 9405.73 | 9586.66 | 1.92% |
|
|
||||||
| chat_standard | 64 | st=3 | 15680.46 | 15542.80 | 0.89% |
|
|
||||||
| decode_heavy | 1 | st=5 | 564.81 | 673.37 | 19.22% |
|
|
||||||
| decode_heavy | 32 | st=5 | 6949.57 | 7203.28 | 3.65% |
|
|
||||||
| generation_standard | 64 | st=3 | 13363.77 | 11880.13 | 12.49% |
|
|
||||||
| long_context_probe | 1 | st=5 | 10074.03 | 11835.70 | 17.49% |
|
|
||||||
| long_context_probe | 4 | st=5 | 35811.63 | 39893.35 | 11.40% |
|
|
||||||
| long_context_probe | 8 | st=5 | 27327.58 | 29361.32 | 7.44% |
|
|
||||||
| rag_medium | 1 | st=5 | 3317.84 | 4075.47 | 22.84% |
|
|
||||||
| rag_medium | 8 | st=5 | 14730.01 | 15838.39 | 7.52% |
|
|
||||||
| rag_medium | 32 | st=5 | 26176.72 | 27420.79 | 4.75% |
|
|
||||||
| stress_standard | 64 | st=3 | 15225.66 | 14942.17 | 1.90% |
|
|
||||||
| stress_standard | 96 | st=3 | 24069.73 | 19349.55 | 24.39% |
|
|
||||||
| stress_standard | 128 | st=3 | 29828.36 | 22700.92 | 31.40% |
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 关键发现
|
|
||||||
|
|
||||||
### 1. 并发是决定性因素
|
|
||||||
|
|
||||||
- **低并发（c=1）和中低并发（c=8~32）**：`st=5` 在多数场景下更优，尤其是长上下文和重 decode 场景。
|
|
||||||
- **高并发（c≥96）**：`st=3` 明显更优，`stress_standard` c=128 时 st=3 比 st=5 高 **31.4%**。
|
|
||||||
- **中并发（c=64）**：两者基本持平，差异多在 2% 以内。
|
|
||||||
|
|
||||||
### 2. st=5 更适合长上下文和重 decode
|
|
||||||
|
|
||||||
| 场景 | 最佳 spec | 原因 |
|
|
||||||
|---|---|---|
|
|
||||||
| long_context_probe (16K) | st=5 | 长 prefill 下 st=5 的接受长度更高 |
|
|
||||||
| rag_medium (4K) | st=5 | 中长输入下 st=5 延迟和吞吐都更优 |
|
|
||||||
| decode_heavy (2K output) | st=5 | 输出越长，st=5 的投机收益越大 |
|
|
||||||
|
|
||||||
### 3. st=3 在极限并发下更稳
|
|
||||||
|
|
||||||
`stress_standard` 随并发增加，st=3 与 st=5 的差距拉大：
|
|
||||||
|
|
||||||
| 并发 | st=3 Total tok/s | st=5 Total tok/s | 差距 |
|
|
||||||
|---|---:|---:|---:|
|
|
||||||
| 64 | 15225.66 | 14942.17 | 基本持平 |
|
|
||||||
| 96 | 24069.73 | 19349.55 | st=3 高 24.4% |
|
|
||||||
| 128 | 29828.36 | 22700.92 | st=3 高 31.4% |
|
|
||||||
|
|
||||||
这说明 st=5 在极限并发下验证开销和 KV cache 压力显著增加，而 st=3 的验证 batch 更小、调度更稳定。
|
|
||||||
|
|
||||||
### 4. chat_short c=16 的异常
|
|
||||||
|
|
||||||
`chat_short c=16` 时 st=5 比 st=3 高 32.9%，是一个明显的 outlier。可能原因是该并发度下 st=5 的 draft 接受率和 batch 利用率恰好达到甜点。
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 优化建议
|
|
||||||
|
|
||||||
### 推荐配置
|
|
||||||
|
|
||||||
| 负载特征 | 推荐 `--spec-tokens` | 说明 |
|
|
||||||
|---|---|---|
|
|
||||||
| 低并发 / 在线交互（c ≤ 32） | **5** | 延迟低、单请求吞吐高 |
|
|
||||||
| 中并发（c ≈ 64） | 3 或 5 均可 | 差异很小 |
|
|
||||||
| 高并发 / 压测（c ≥ 96） | **3** | 吞吐更高、P99 更稳 |
|
|
||||||
| 长上下文 / RAG / 重 decode | **5** | 接受长度优势更明显 |
|
|
||||||
|
|
||||||
### 下一步
|
|
||||||
|
|
||||||
1. 如果业务以高并发为主，将默认服务改为 `--spec-tokens 3`。
|
|
||||||
2. 如果业务混合，可考虑按输入长度或并发度路由到不同服务实例。
|
|
||||||
3. 本次 warmup 已从 10 提升到 100，P99 TTFT 相比首次 grid 测试有明显改善；建议保持 100 条 warmup。
|
|
||||||
@ -1,102 +0,0 @@
|
|||||||
# DSV4 Backend Comparison Raw Outputs (2026-07-07)
|
|
||||||
|
|
||||||
## Location
|
|
||||||
|
|
||||||
`/data/user1/yy/bench_results/dsv4_backend_comparison_20260707`
|
|
||||||
|
|
||||||
## Contents
|
|
||||||
|
|
||||||
- `raw_outputs/` — raw per-benchmark JSONL files produced by `sglang.bench_serving --output-file --output-details`.
|
|
||||||
- `README.md` — this file.
|
|
||||||
|
|
||||||
## File Naming Convention
|
|
||||||
|
|
||||||
Each file follows the pattern:
|
|
||||||
|
|
||||||
```
|
|
||||||
{backend}_0707_{concurrency}_{input_len}_{output_len}.jsonl
|
|
||||||
```
|
|
||||||
|
|
||||||
Where:
|
|
||||||
|
|
||||||
- `{backend}`: `sglang` or `vllm`
|
|
||||||
- `{concurrency}`: max concurrent requests (`max-concurrency`)
|
|
||||||
- `{input_len}`: `--random-input-len`
|
|
||||||
- `{output_len}`: `--random-output-len`
|
|
||||||
|
|
||||||
## Output File Inventory
|
|
||||||
|
|
||||||
### SGLang backend
|
|
||||||
|
|
||||||
| File | Concurrency | Input len | Output len |
|
|
||||||
|---|---|---|---|
|
|
||||||
| sglang_0707_32_512_256.jsonl | 32 | 512 | 256 |
|
|
||||||
| sglang_0707_128_512_256.jsonl | 128 | 512 | 256 |
|
|
||||||
| sglang_0707_256_512_256.jsonl | 256 | 512 | 256 |
|
|
||||||
| sglang_0707_512_512_256.jsonl | 512 | 512 | 256 |
|
|
||||||
| sglang_0707_512_512_2000.jsonl | 512 | 512 | 2000 |
|
|
||||||
| sglang_0707_32_512_2000.jsonl | 32 | 512 | 2000 |
|
|
||||||
| sglang_0707_32_4000_512.jsonl | 32 | 4000 | 512 |
|
|
||||||
| sglang_0707_128_4000_512.jsonl | 128 | 4000 | 512 |
|
|
||||||
| sglang_0707_512_4000_512.jsonl | 512 | 4000 | 512 |
|
|
||||||
| sglang_0707_32_16000_512.jsonl | 32 | 16000 | 512 |
|
|
||||||
| sglang_0707_128_16000_512.jsonl | 128 | 16000 | 512 |
|
|
||||||
| sglang_0707_512_1000_256.jsonl | 512 | 1000 | 256 |
|
|
||||||
| sglang_0707_768_1000_256.jsonl | 768 | 1000 | 256 |
|
|
||||||
| sglang_0707_1024_1000_256.jsonl | 1024 | 1000 | 256 |
|
|
||||||
| sglang_0707_512_1000_1000.jsonl | 512 | 1000 | 1000 |
|
|
||||||
|
|
||||||
### vLLM backend
|
|
||||||
|
|
||||||
| File | Concurrency | Input len | Output len |
|
|
||||||
|---|---|---|---|
|
|
||||||
| vllm_0707_32_512_256.jsonl | 32 | 512 | 256 |
|
|
||||||
| vllm_0707_128_512_256.jsonl | 128 | 512 | 256 |
|
|
||||||
| vllm_0707_256_512_256.jsonl | 256 | 512 | 256 |
|
|
||||||
| vllm_0707_128_512_2000.jsonl | 128 | 512 | 2000 |
|
|
||||||
| vllm_0707_256_512_2000.jsonl | 256 | 512 | 2000 |
|
|
||||||
| vllm_0707_512_512_2000.jsonl | 512 | 512 | 2000 |
|
|
||||||
| vllm_0707_32_1000_256.jsonl | 32 | 1000 | 256 |
|
|
||||||
| vllm_0707_128_1000_256.jsonl | 128 | 1000 | 256 |
|
|
||||||
| vllm_0707_256_1000_256.jsonl | 256 | 1000 | 256 |
|
|
||||||
| vllm_0707_512_1000_256.jsonl | 512 | 1000 | 256 |
|
|
||||||
| vllm_0707_768_1000_256.jsonl | 768 | 1000 | 256 |
|
|
||||||
| vllm_0707_1024_1000_256.jsonl | 1024 | 1000 | 256 |
|
|
||||||
| vllm_0707_32_1000_1000.jsonl | 32 | 1000 | 1000 |
|
|
||||||
| vllm_0707_128_1000_1000.jsonl | 128 | 1000 | 1000 |
|
|
||||||
| vllm_0707_256_1000_1000.jsonl | 256 | 1000 | 1000 |
|
|
||||||
| vllm_0707_512_1000_1000.jsonl | 512 | 1000 | 1000 |
|
|
||||||
| vllm_0707_1024_1000_1000.jsonl | 1024 | 1000 | 1000 |
|
|
||||||
| vllm_0707_32_4000_512.jsonl | 32 | 4000 | 512 |
|
|
||||||
| vllm_0707_128_4000_512.jsonl | 128 | 4000 | 512 |
|
|
||||||
| vllm_0707_256_4000_512.jsonl | 256 | 4000 | 512 |
|
|
||||||
| vllm_0707_512_4000_512.jsonl | 512 | 4000 | 512 |
|
|
||||||
| vllm_0707_32_8000_1000.jsonl | 32 | 8000 | 1000 |
|
|
||||||
| vllm_0707_128_8000_1000.jsonl | 128 | 8000 | 1000 |
|
|
||||||
| vllm_0707_256_8000_1000.jsonl | 256 | 8000 | 1000 |
|
|
||||||
| vllm_0707_512_8000_1000.jsonl | 512 | 8000 | 1000 |
|
|
||||||
| vllm_0707_32_16000_512.jsonl | 32 | 16000 | 512 |
|
|
||||||
| vllm_0707_128_16000_512.jsonl | 128 | 16000 | 512 |
|
|
||||||
| vllm_0707_256_16000_512.jsonl | 256 | 16000 | 512 |
|
|
||||||
| vllm_0707_32_32000_512.jsonl | 32 | 32000 | 512 |
|
|
||||||
| vllm_0707_128_32000_512.jsonl | 128 | 32000 | 512 |
|
|
||||||
|
|
||||||
## Provenance
|
|
||||||
|
|
||||||
These files are **legacy raw outputs** from an ad-hoc SGLang vs vLLM backend comparison sweep run on 2026-07-07. The exact generator command/script that produced them was not preserved in the repository. They were generated using `sglang.bench_serving` with `--output-file` and `--output-details`, against:
|
|
||||||
|
|
||||||
- Model: `/data/models/DeepSeek-V4-Flash`
|
|
||||||
- SGLang port: `30000`
|
|
||||||
- vLLM port: `8000` (inferred from file content)
|
|
||||||
- Dataset: `random`
|
|
||||||
|
|
||||||
For future reproducible comparisons, use the script:
|
|
||||||
|
|
||||||
```
|
|
||||||
scripts/benchmark_dsv4_backend_comparison.sh
|
|
||||||
```
|
|
||||||
|
|
||||||
## Related Reports
|
|
||||||
|
|
||||||
- `/data/user1/yy/dsv4_inference_comparison_report.md` — earlier DSV4 inference comparison report.
|
|
||||||
- `/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/` — earlier comparison run outputs (`.json`/`.log`).
|
|
||||||
@ -1,197 +0,0 @@
|
|||||||
[
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-dspark-st5",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": "dspark",
|
|
||||||
"spec_tokens": 5,
|
|
||||||
"concurrency": 1,
|
|
||||||
"request_throughput": 1.0665689780673602,
|
|
||||||
"output_throughput": 257.1817776813825,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 48226,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-dspark-st5_c1.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-dspark-st5",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": "dspark",
|
|
||||||
"spec_tokens": 5,
|
|
||||||
"concurrency": 16,
|
|
||||||
"request_throughput": 7.702621472216438,
|
|
||||||
"output_throughput": 1862.3398195524906,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 48356,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-dspark-st5_c16.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-dspark-st5",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": "dspark",
|
|
||||||
"spec_tokens": 5,
|
|
||||||
"concurrency": 64,
|
|
||||||
"request_throughput": 9.360404619968996,
|
|
||||||
"output_throughput": 2210.4595510056783,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 47230,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-dspark-st5_c64.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-nospec",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": null,
|
|
||||||
"spec_tokens": null,
|
|
||||||
"concurrency": 1,
|
|
||||||
"request_throughput": 0.5918258062472803,
|
|
||||||
"output_throughput": 144.62151314361665,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 48873,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-nospec_c1.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-nospec",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": null,
|
|
||||||
"spec_tokens": null,
|
|
||||||
"concurrency": 16,
|
|
||||||
"request_throughput": 5.61467992292336,
|
|
||||||
"output_throughput": 1364.0022670753865,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 48587,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-nospec_c16.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-nospec",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": null,
|
|
||||||
"spec_tokens": null,
|
|
||||||
"concurrency": 64,
|
|
||||||
"request_throughput": 7.339983457334795,
|
|
||||||
"output_throughput": 1803.9844342264591,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 49155,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-nospec_c64.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-dspark-st3",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": "dspark",
|
|
||||||
"spec_tokens": 3,
|
|
||||||
"concurrency": 1,
|
|
||||||
"request_throughput": 1.033805786989743,
|
|
||||||
"output_throughput": 254.38859000456605,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 49214,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-dspark-st3_c1.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-dspark-st3",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": "dspark",
|
|
||||||
"spec_tokens": 3,
|
|
||||||
"concurrency": 16,
|
|
||||||
"request_throughput": 8.237095278328889,
|
|
||||||
"output_throughput": 1992.1826785402332,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 48371,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-dspark-st3_c16.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-dspark-st3",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": "dspark",
|
|
||||||
"spec_tokens": 3,
|
|
||||||
"concurrency": 64,
|
|
||||||
"request_throughput": 14.530514004704537,
|
|
||||||
"output_throughput": 3507.8113858757224,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 48282,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-dspark-st3_c64.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-dspark-st7",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": "dspark",
|
|
||||||
"spec_tokens": 7,
|
|
||||||
"concurrency": 1,
|
|
||||||
"request_throughput": 1.0285012524448527,
|
|
||||||
"output_throughput": 251.58169136053544,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 48922,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-dspark-st7_c1.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-dspark-st7",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": "dspark",
|
|
||||||
"spec_tokens": 7,
|
|
||||||
"concurrency": 16,
|
|
||||||
"request_throughput": 7.463416900080192,
|
|
||||||
"output_throughput": 1772.3002941775428,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 47493,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-dspark-st7_c16.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-dspark-dspark-st7",
|
|
||||||
"engine": "vllm-dspark",
|
|
||||||
"spec_method": "dspark",
|
|
||||||
"spec_tokens": 7,
|
|
||||||
"concurrency": 64,
|
|
||||||
"request_throughput": 9.128647309323574,
|
|
||||||
"output_throughput": 2218.5807988214547,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 48607,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-dspark-dspark-st7_c64.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-main-nospec",
|
|
||||||
"engine": "vllm",
|
|
||||||
"spec_method": null,
|
|
||||||
"spec_tokens": null,
|
|
||||||
"concurrency": 1,
|
|
||||||
"request_throughput": 0.5806562443589017,
|
|
||||||
"output_throughput": 138.2571550630763,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 47621,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-main-nospec_c1.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-main-nospec",
|
|
||||||
"engine": "vllm",
|
|
||||||
"spec_method": null,
|
|
||||||
"spec_tokens": null,
|
|
||||||
"concurrency": 16,
|
|
||||||
"request_throughput": 5.052303923305438,
|
|
||||||
"output_throughput": 1180.976042072646,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 46750,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-main-nospec_c16.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"service": "vllm-main-nospec",
|
|
||||||
"engine": "vllm",
|
|
||||||
"spec_method": null,
|
|
||||||
"spec_tokens": null,
|
|
||||||
"concurrency": 64,
|
|
||||||
"request_throughput": 6.851644652089382,
|
|
||||||
"output_throughput": 1641.8938662034388,
|
|
||||||
"total_input_tokens": 47865,
|
|
||||||
"total_output_tokens": 47927,
|
|
||||||
"duration_s": null,
|
|
||||||
"result_file": "/data/user1/yy/bench_results/dsv4_comparison_20260705_152221/vllm-main-nospec_c64.json"
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -1,96 +0,0 @@
|
|||||||
# DSpark vs SGLang EAGLE 投机解码对比报告
|
|
||||||
|
|
||||||
- DSpark 结果：`/data/user1/yy/bench_results/dspark_st_comparison_20260707-150649`
|
|
||||||
- EAGLE 结果：`/data/user1/yy/bench_results/eagle_grid/focused`
|
|
||||||
- DSpark 模型：`/data/models/DeepSeek-V4-Flash-DSpark`
|
|
||||||
- EAGLE 模型：`/data/models/DeepSeek-V4-Flash`
|
|
||||||
- DSpark 后端：vllm-dspark (TP=8, FP8 KV cache, spec-method=dspark)
|
|
||||||
- EAGLE 后端：SGLang (TP=8, FP8 KV cache, speculative-algorithm=EAGLE, num_steps=3, topk=1, draft_tokens=4)
|
|
||||||
- 压测客户端：`sglang.bench_serving`
|
|
||||||
- Warmup：100 条
|
|
||||||
|
|
||||||
## 核心指标对比
|
|
||||||
|
|
||||||
| Scenario | Concurrency | Method | Total tok/s | Out tok/s | Req/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P99 TPOT(ms) |
|
|
||||||
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
|
|
||||||
| chat_short | 1 | DSpark(st=5) | 1094.51 | 397.37 | 2.73 | 365.29 | 743.89 | 782.98 | 64.42 | 174.36 | 2.06 | 3.40 |
|
|
||||||
| chat_short | 1 | EAGLE | 272.82 | 99.05 | 0.68 | 1467.67 | 3775.96 | 5175.09 | 223.86 | 405.20 | 7.47 | 19.74 |
|
|
||||||
| chat_short | 8 | DSpark(st=3) | 4705.85 | 1525.06 | 11.55 | 672.66 | 1269.82 | 1552.65 | 76.20 | 221.18 | 4.67 | 8.43 |
|
|
||||||
| chat_short | 8 | EAGLE | 1059.99 | 343.52 | 2.60 | 3043.16 | 7143.57 | 8345.08 | 245.91 | 528.86 | 21.41 | 36.02 |
|
|
||||||
| chat_short | 16 | DSpark(st=5) | 6282.94 | 2171.58 | 15.59 | 997.37 | 2147.86 | 2496.56 | 107.56 | 240.46 | 7.09 | 21.93 |
|
|
||||||
| chat_short | 16 | EAGLE | 1506.42 | 520.67 | 3.74 | 4240.11 | 8031.57 | 10192.48 | 236.19 | 438.56 | 29.12 | 54.02 |
|
|
||||||
| chat_short | 32 | DSpark(st=3) | 8275.55 | 2754.96 | 20.79 | 1497.40 | 2944.48 | 3658.90 | 144.47 | 453.29 | 10.72 | 23.97 |
|
|
||||||
| chat_short | 32 | EAGLE | 2461.23 | 819.35 | 6.18 | 4731.11 | 9926.81 | 11388.85 | 243.84 | 571.89 | 34.22 | 54.92 |
|
|
||||||
| chat_short | 64 | DSpark(st=5) | 9586.66 | 3191.43 | 24.09 | 2555.12 | 5387.59 | 6559.69 | 274.71 | 610.40 | 18.47 | 42.08 |
|
|
||||||
| chat_short | 64 | EAGLE | 4474.88 | 1489.70 | 11.24 | 5239.80 | 10296.02 | 11922.06 | 268.27 | 452.80 | 38.17 | 61.24 |
|
|
||||||
| chat_standard | 64 | DSpark(st=3) | 15680.46 | 3288.58 | 24.70 | 2483.46 | 4798.63 | 5693.88 | 271.41 | 947.15 | 17.93 | 46.05 |
|
|
||||||
| chat_standard | 64 | EAGLE | 6746.24 | 1414.85 | 10.63 | 5637.63 | 10693.92 | 12907.15 | 296.86 | 597.18 | 41.12 | 69.08 |
|
|
||||||
| decode_heavy | 1 | DSpark(st=5) | 673.37 | 531.76 | 0.55 | 1802.00 | 2957.04 | 3676.66 | 59.06 | 114.78 | 1.92 | 3.03 |
|
|
||||||
| decode_heavy | 1 | EAGLE | 261.35 | 206.39 | 0.22 | 4643.62 | 10452.02 | 18810.32 | 162.91 | 314.71 | 4.84 | 11.69 |
|
|
||||||
| decode_heavy | 32 | DSpark(st=5) | 7203.28 | 5737.73 | 5.52 | 5595.02 | 10412.11 | 12522.00 | 107.12 | 331.08 | 5.43 | 9.15 |
|
|
||||||
| decode_heavy | 32 | EAGLE | 2981.50 | 2374.90 | 2.28 | 13707.03 | 27159.67 | 30638.81 | 192.78 | 442.78 | 13.23 | 19.26 |
|
|
||||||
| generation_standard | 64 | DSpark(st=3) | 13363.77 | 6705.88 | 13.27 | 4542.22 | 8273.38 | 9355.10 | 190.34 | 813.58 | 9.00 | 15.36 |
|
|
||||||
| generation_standard | 64 | EAGLE | 5609.33 | 2814.73 | 5.57 | 11057.53 | 22577.31 | 26713.41 | 275.48 | 944.52 | 21.88 | 34.80 |
|
|
||||||
| long_context_probe | 1 | DSpark(st=5) | 11835.70 | 392.31 | 1.44 | 693.30 | 1172.84 | 1404.17 | 267.61 | 561.65 | 1.53 | 1.88 |
|
|
||||||
| long_context_probe | 1 | EAGLE | 5556.95 | 184.19 | 0.68 | 1477.16 | 2792.68 | 3836.17 | 238.26 | 618.74 | 4.43 | 8.62 |
|
|
||||||
| long_context_probe | 4 | DSpark(st=5) | 39893.35 | 1322.32 | 4.85 | 806.55 | 1390.87 | 1500.22 | 143.42 | 236.41 | 2.41 | 3.54 |
|
|
||||||
| long_context_probe | 4 | EAGLE | 6660.56 | 220.77 | 0.81 | 4777.51 | 9442.45 | 10071.75 | 433.60 | 1230.20 | 14.85 | 21.65 |
|
|
||||||
| long_context_probe | 8 | DSpark(st=5) | 29361.32 | 900.89 | 3.56 | 2223.29 | 4693.10 | 5345.72 | 425.03 | 1241.57 | 7.08 | 19.83 |
|
|
||||||
| long_context_probe | 8 | EAGLE | 9890.47 | 303.47 | 1.20 | 6232.13 | 16324.27 | 20329.21 | 606.76 | 2671.06 | 22.79 | 54.38 |
|
|
||||||
| rag_medium | 1 | DSpark(st=5) | 4075.47 | 496.09 | 1.76 | 566.58 | 968.90 | 1134.15 | 110.34 | 156.35 | 1.64 | 2.34 |
|
|
||||||
| rag_medium | 1 | EAGLE | 1326.02 | 161.41 | 0.57 | 1742.43 | 3804.17 | 3936.47 | 195.07 | 337.55 | 5.83 | 12.84 |
|
|
||||||
| rag_medium | 8 | DSpark(st=5) | 15838.39 | 1692.13 | 6.69 | 1175.26 | 2263.59 | 3106.42 | 154.24 | 352.19 | 4.14 | 8.36 |
|
|
||||||
| rag_medium | 8 | EAGLE | 3676.32 | 392.77 | 1.55 | 4908.89 | 11659.24 | 12566.81 | 254.65 | 678.44 | 18.53 | 35.36 |
|
|
||||||
| rag_medium | 32 | DSpark(st=5) | 27420.79 | 3062.92 | 11.91 | 2626.21 | 5182.78 | 6873.72 | 253.09 | 522.80 | 9.17 | 18.55 |
|
|
||||||
| rag_medium | 32 | EAGLE | 13268.07 | 1482.05 | 5.76 | 5444.98 | 10471.13 | 11990.37 | 337.62 | 2204.45 | 20.09 | 39.98 |
|
|
||||||
| stress_standard | 64 | DSpark(st=3) | 15225.66 | 3193.19 | 23.99 | 2569.66 | 5006.16 | 5831.81 | 261.98 | 710.57 | 18.02 | 36.01 |
|
|
||||||
| stress_standard | 64 | EAGLE | 5399.35 | 1132.38 | 8.51 | 6678.22 | 13086.21 | 14811.42 | 314.38 | 584.25 | 48.19 | 81.06 |
|
|
||||||
| stress_standard | 96 | DSpark(st=3) | 24069.73 | 4756.72 | 37.72 | 2433.73 | 4827.07 | 6001.29 | 272.61 | 749.99 | 18.04 | 38.52 |
|
|
||||||
| stress_standard | 96 | EAGLE | 8833.26 | 1745.65 | 13.84 | 6792.34 | 14000.22 | 16722.55 | 360.14 | 959.71 | 52.90 | 96.70 |
|
|
||||||
| stress_standard | 128 | DSpark(st=3) | 29828.36 | 5942.48 | 47.45 | 2581.42 | 5162.16 | 6222.01 | 298.69 | 970.61 | 19.09 | 40.93 |
|
|
||||||
| stress_standard | 128 | EAGLE | 13189.80 | 2627.70 | 20.98 | 5960.86 | 12256.06 | 14219.75 | 416.12 | 2153.71 | 47.20 | 81.35 |
|
|
||||||
|
|
||||||
## 吞吐 Winner 统计（按 Total tok/s）
|
|
||||||
|
|
||||||
| Scenario | Concurrency | Winner | DSpark Total tok/s | EAGLE Total tok/s | 提升 |
|
|
||||||
|---|---:|---:|---:|---:|---:|
|
|
||||||
| chat_short | 1 | DSpark(st=5) | 1094.51 | 272.82 | 301.18% |
|
|
||||||
| chat_short | 8 | DSpark(st=3) | 4705.85 | 1059.99 | 343.95% |
|
|
||||||
| chat_short | 16 | DSpark(st=5) | 6282.94 | 1506.42 | 317.08% |
|
|
||||||
| chat_short | 32 | DSpark(st=3) | 8275.55 | 2461.23 | 236.24% |
|
|
||||||
| chat_short | 64 | DSpark(st=5) | 9586.66 | 4474.88 | 114.23% |
|
|
||||||
| chat_standard | 64 | DSpark(st=3) | 15680.46 | 6746.24 | 132.43% |
|
|
||||||
| decode_heavy | 1 | DSpark(st=5) | 673.37 | 261.35 | 157.65% |
|
|
||||||
| decode_heavy | 32 | DSpark(st=5) | 7203.28 | 2981.50 | 141.60% |
|
|
||||||
| generation_standard | 64 | DSpark(st=3) | 13363.77 | 5609.33 | 138.24% |
|
|
||||||
| long_context_probe | 1 | DSpark(st=5) | 11835.70 | 5556.95 | 112.99% |
|
|
||||||
| long_context_probe | 4 | DSpark(st=5) | 39893.35 | 6660.56 | 498.95% |
|
|
||||||
| long_context_probe | 8 | DSpark(st=5) | 29361.32 | 9890.47 | 196.86% |
|
|
||||||
| rag_medium | 1 | DSpark(st=5) | 4075.47 | 1326.02 | 207.35% |
|
|
||||||
| rag_medium | 8 | DSpark(st=5) | 15838.39 | 3676.32 | 330.82% |
|
|
||||||
| rag_medium | 32 | DSpark(st=5) | 27420.79 | 13268.07 | 106.67% |
|
|
||||||
| stress_standard | 64 | DSpark(st=3) | 15225.66 | 5399.35 | 181.99% |
|
|
||||||
| stress_standard | 96 | DSpark(st=3) | 24069.73 | 8833.26 | 172.49% |
|
|
||||||
| stress_standard | 128 | DSpark(st=3) | 29828.36 | 13189.80 | 126.15% |
|
|
||||||
|
|
||||||
## 关键发现
|
|
||||||
|
|
||||||
- 对比点数：18
|
|
||||||
- EAGLE 胜出：0 个
|
|
||||||
- DSpark 胜出：18 个
|
|
||||||
|
|
||||||
**结论：在相同负载下，vllm-dspark 的综合吞吐优于 SGLang EAGLE。**
|
|
||||||
|
|
||||||
### 延迟与稳定性观察
|
|
||||||
|
|
||||||
- 平均 Mean E2E 比值（EAGLE / DSpark）：3.09
|
|
||||||
- 平均 Mean TPOT 比值（EAGLE / DSpark）：3.21
|
|
||||||
- 平均 Mean TTFT 比值（EAGLE / DSpark）：1.81
|
|
||||||
|
|
||||||
> 比值 < 1 表示 EAGLE 更快；> 1 表示 DSpark 更快。
|
|
||||||
|
|
||||||
## 优化建议
|
|
||||||
|
|
||||||
1. 如果以吞吐为首要目标，当前配置下 **vllm-dspark 更优**，建议继续使用 `--spec-tokens` 并根据并发选择 3（高并发）或 5（低并发/长上下文）。
|
|
||||||
2. SGLang EAGLE 本次表现不佳，平均延迟和 TPOT 均显著高于 DSpark；如需进一步评估 EAGLE，可尝试调整 `--speculative-num-steps`、`--speculative-eagle-topk`、`--speculative-num-draft-tokens` 或更换 MOE runner backend，并确认是否已完成 `sglang.compile_deep_gemm` 预编译。
|
|
||||||
3. 注意两套后端的实现差异（CUDA graph、KV cache 管理、调度器、draft 模型架构）会显著影响不同并发和输入长度下的表现，建议按实际业务负载做最终选型。
|
|
||||||
@ -1,6 +0,0 @@
|
|||||||
# vllm-dspark Benchmark Grid Report
|
|
||||||
|
|
||||||
- Result root: `/data/user1/yy/bench_results/eagle_grid`
|
|
||||||
- Model: `/data/models/DeepSeek-V4-Flash-DSpark`
|
|
||||||
- Backend: vllm-dspark (TP=8, FP8 KV cache, spec-method=dspark, spec-tokens=5)
|
|
||||||
- Benchmark client: `sglang.bench_serving --backend vllm`
|
|
||||||
@ -1,58 +0,0 @@
|
|||||||
[
|
|
||||||
{
|
|
||||||
"name": "sharegpt_concurrency_128",
|
|
||||||
"args": "--max-concurrency 128",
|
|
||||||
"duration_s": 155.26,
|
|
||||||
"request_throughput": 12.88,
|
|
||||||
"input_token_throughput": 3710.5,
|
|
||||||
"output_token_throughput": 2690.48,
|
|
||||||
"total_token_throughput": 6400.98,
|
|
||||||
"mean_ttft_ms": 410.55,
|
|
||||||
"mean_tpot_ms": 48.45,
|
|
||||||
"mean_e2e_latency_ms": 9696.87,
|
|
||||||
"accept_length": 2.6,
|
|
||||||
"output_file": "bench_results/sglang_8card_max_throughput_20260705_030839/sharegpt_concurrency_128.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_concurrency_256",
|
|
||||||
"args": "--max-concurrency 256",
|
|
||||||
"duration_s": 90.65,
|
|
||||||
"request_throughput": 22.06,
|
|
||||||
"input_token_throughput": 6355.48,
|
|
||||||
"output_token_throughput": 4608.35,
|
|
||||||
"total_token_throughput": 10963.84,
|
|
||||||
"mean_ttft_ms": 509.73,
|
|
||||||
"mean_tpot_ms": 62.71,
|
|
||||||
"mean_e2e_latency_ms": 10869.76,
|
|
||||||
"accept_length": 2.6,
|
|
||||||
"output_file": "bench_results/sglang_8card_max_throughput_20260705_030839/sharegpt_concurrency_256.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_concurrency_512",
|
|
||||||
"args": "--max-concurrency 512",
|
|
||||||
"duration_s": 71.39,
|
|
||||||
"request_throughput": 28.02,
|
|
||||||
"input_token_throughput": 8070.04,
|
|
||||||
"output_token_throughput": 5851.58,
|
|
||||||
"total_token_throughput": 13921.61,
|
|
||||||
"mean_ttft_ms": 1343.73,
|
|
||||||
"mean_tpot_ms": 121.2,
|
|
||||||
"mean_e2e_latency_ms": 16176.14,
|
|
||||||
"accept_length": 2.6,
|
|
||||||
"output_file": "bench_results/sglang_8card_max_throughput_20260705_030839/sharegpt_concurrency_512.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_concurrency_1024",
|
|
||||||
"args": "--max-concurrency 1024",
|
|
||||||
"duration_s": 68.21,
|
|
||||||
"request_throughput": 29.32,
|
|
||||||
"input_token_throughput": 8445.53,
|
|
||||||
"output_token_throughput": 6123.84,
|
|
||||||
"total_token_throughput": 14569.37,
|
|
||||||
"mean_ttft_ms": 13265.46,
|
|
||||||
"mean_tpot_ms": 105.36,
|
|
||||||
"mean_e2e_latency_ms": 27566.99,
|
|
||||||
"accept_length": 2.6,
|
|
||||||
"output_file": "bench_results/sglang_8card_max_throughput_20260705_030839/sharegpt_concurrency_1024.json"
|
|
||||||
}
|
|
||||||
]
|
|
||||||
Binary file not shown.
|
Before Width: | Height: | Size: 147 KiB |
@ -1,267 +0,0 @@
|
|||||||
# SGLang 8-Card DeepSeek-V4-Flash 系统 Benchmark 报告
|
|
||||||
|
|
||||||
生成时间：2026-07-05 02:10:06
|
|
||||||
|
|
||||||
## 测试环境
|
|
||||||
|
|
||||||
- **模型**：DeepSeek-V4-Flash (`/data/models/DeepSeek-V4-Flash`)
|
|
||||||
- **推理框架**：SGLang
|
|
||||||
- **并行策略**：TP=8，8× NVIDIA H200
|
|
||||||
- **MoE backend**：marlin
|
|
||||||
- **投机采样**：EAGLE（`--speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4`）
|
|
||||||
- **服务端口**：30000
|
|
||||||
|
|
||||||
## 测试负载
|
|
||||||
|
|
||||||
- **数据集**：ShareGPT V4.3 unfiltered cleaned split
|
|
||||||
- **请求数**：每个测试点 2000 条真实对话请求
|
|
||||||
- **测试维度**：
|
|
||||||
- 并发扫描：`--max-concurrency` = 1, 8, 16, 32, 64, 128
|
|
||||||
- 请求速率扫描：`--request-rate` = 1, 2, 4, 8, 16 (Poisson-like)
|
|
||||||
|
|
||||||
## Phase 1：并发扫描结果
|
|
||||||
|
|
||||||
| 并发 | 耗时(s) | req/s | input tok/s | output tok/s | total tok/s | Mean TTFT(ms) | Mean TPOT(ms) | Mean E2E(ms) | Accept Length |
|
|
||||||
|------|---------|-------|-------------|--------------|-------------|---------------|---------------|--------------|---------------|
|
|
||||||
| 1 | 1715.45 | 1.17 | 335.83 | 243.51 | 579.34 | 128.09 | 4.00 | 856.77 | 2.62 |
|
|
||||||
| 8 | 448.91 | 4.46 | 1283.33 | 930.54 | 2213.87 | 146.76 | 8.41 | 1790.65 | 2.61 |
|
|
||||||
| 16 | 331.79 | 6.03 | 1736.31 | 1259.00 | 2995.30 | 168.95 | 12.67 | 2642.10 | 2.61 |
|
|
||||||
| 32 | 267.72 | 7.47 | 2151.87 | 1560.32 | 3712.19 | 206.24 | 20.72 | 4249.71 | 2.61 |
|
|
||||||
| 64 | 207.57 | 9.64 | 2775.39 | 2012.43 | 4787.82 | 248.41 | 32.61 | 6557.39 | 2.61 |
|
|
||||||
| 128 | 139.41 | 14.35 | 4132.36 | 2996.37 | 7128.73 | 330.40 | 46.15 | 8596.12 | 2.61 |
|
|
||||||
|
|
||||||
## Phase 2：请求速率扫描结果
|
|
||||||
|
|
||||||
| RPS | 耗时(s) | req/s | input tok/s | output tok/s | total tok/s | Mean TTFT(ms) | Mean TPOT(ms) | Mean E2E(ms) | Accept Length |
|
|
||||||
|-----|---------|-------|-------------|--------------|-------------|---------------|---------------|--------------|---------------|
|
|
||||||
| 1 | 2011.46 | 0.99 | 286.41 | 207.67 | 494.08 | 146.87 | 4.91 | 1007.62 | 2.61 |
|
|
||||||
| 2 | 1007.21 | 1.99 | 571.97 | 414.74 | 986.71 | 160.63 | 5.99 | 1205.24 | 2.61 |
|
|
||||||
| 4 | 505.49 | 3.96 | 1139.69 | 826.39 | 1966.08 | 174.67 | 9.02 | 1743.40 | 2.60 |
|
|
||||||
| 8 | 256.46 | 7.80 | 2246.34 | 1628.82 | 3875.15 | 216.65 | 25.35 | 4542.64 | 2.60 |
|
|
||||||
| 16 | 137.92 | 14.50 | 4176.94 | 3028.70 | 7205.64 | 377.28 | 87.16 | 15048.09 | 2.61 |
|
|
||||||
|
|
||||||
## 关键发现
|
|
||||||
|
|
||||||
1. **吞吐随并发单调上升**：从 c=1 的 579 tok/s 提升到 c=128 的 7129 tok/s，说明 8×H200 在高压下仍能有效扩展。
|
|
||||||
2. **c=32 是延迟与吞吐的拐点**：c=32 时 total tok/s 为 3712，E2E 延迟约 4.25s；c=64 时吞吐提升到 4788，但延迟增加到 6.56s。
|
|
||||||
3. **c=128 达到最高吞吐**：total tok/s = 7129，output tok/s = 2996，但 E2E 延迟接近 8.6s，适合离线/批处理场景。
|
|
||||||
4. **RPS=16 时系统过载**：实际 req/s 达到 14.5，TPOT 飙升到 87ms，E2E 延迟 15s，说明 RPS>14 已超出稳定运行区间。
|
|
||||||
5. **RPS=8 是稳定高吞吐边界**：total tok/s = 3875，E2E 延迟 4.54s，TTFT 仅 217ms，体验与吞吐兼顾。
|
|
||||||
6. **EAGLE 投机采样稳定生效**：Accept Length 稳定在 2.60–2.62，有效降低了 decode 步数。
|
|
||||||
|
|
||||||
## 场景建议
|
|
||||||
|
|
||||||
| 场景 | 推荐配置 | 理由 |
|
|
||||||
|------|----------|------|
|
|
||||||
| 低延迟在线 API | `--max-concurrency 8` | TTFT 147ms，TPOT 8.4ms，E2E 1.79s |
|
|
||||||
| 平衡型在线服务 | `--max-concurrency 16` 或 `--request-rate 4` | 吞吐约 3k tok/s，E2E 2.6–1.7s |
|
|
||||||
| 高吞吐在线服务 | `--max-concurrency 32` 或 `--request-rate 8` | 吞吐 3.7–3.9k tok/s，E2E 4.3–4.5s |
|
|
||||||
| 最大吞吐/离线批处理 | `--max-concurrency 128` | 吞吐 7.1k tok/s，E2E 8.6s |
|
|
||||||
| 避免使用 | `--request-rate 16` | 系统过载，E2E 15s，TPOT 87ms |
|
|
||||||
|
|
||||||
## 原始数据
|
|
||||||
|
|
||||||
- 汇总 JSON：`bench_results/sglang_8card_systematic_20260704_120819/summary.json`
|
|
||||||
- 每个测试点的详细结果与日志：`bench_results/sglang_8card_systematic_20260704_120819/`
|
|
||||||
|
|
||||||
## 附录 A：Benchmark 参数说明
|
|
||||||
|
|
||||||
本次所有测试均使用 `sglang.bench_serving` 工具，参数统一如下：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
envs/sglang/bin/python -m sglang.bench_serving \
|
|
||||||
--backend sglang \
|
|
||||||
--host 127.0.0.1 \
|
|
||||||
--port 30000 \
|
|
||||||
--dataset-name sharegpt \
|
|
||||||
--dataset-path /data/user1/yy/datasets/ShareGPT_V4.3_unfiltered_cleaned_split.json \
|
|
||||||
--num-prompts 2000 \
|
|
||||||
--model /data/models/DeepSeek-V4-Flash \
|
|
||||||
--seed 42
|
|
||||||
```
|
|
||||||
|
|
||||||
两个测试维度的区别仅在于最后一条参数：
|
|
||||||
|
|
||||||
- **并发扫描**：`--max-concurrency {1,8,16,32,64,128}`
|
|
||||||
- 客户端同时保持 N 个未完成的请求，服务器队列按需堆积。
|
|
||||||
- 适合模拟“系统能扛多少并发”的容量测试。
|
|
||||||
|
|
||||||
- **请求速率扫描**：`--request-rate {1,2,4,8,16}`
|
|
||||||
- 客户端按泊松分布以固定 RPS 发送请求。
|
|
||||||
- 适合模拟真实线上按流量到达的场景。
|
|
||||||
|
|
||||||
### 指标定义
|
|
||||||
|
|
||||||
| 指标 | 含义 |
|
|
||||||
|------|------|
|
|
||||||
| **req/s** | 每秒完成的请求数 |
|
|
||||||
| **input tok/s** | 每秒处理的输入 token 数 |
|
|
||||||
| **output tok/s** | 每秒生成的输出 token 数 |
|
|
||||||
| **total tok/s** | input tok/s + output tok/s |
|
|
||||||
| **TTFT** | Time To First Token，首 token 延迟 |
|
|
||||||
| **TPOT** | Time Per Output Token，除首 token 外平均每个输出 token 的间隔 |
|
|
||||||
| **E2E Latency** | 端到端请求完成时间 |
|
|
||||||
| **Accept Length** | EAGLE 投机采样平均每次接受的 draft token 数 |
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 附录 B：Token 成本计算
|
|
||||||
|
|
||||||
### 假设
|
|
||||||
|
|
||||||
- GPU 单价：**8.0 元 / 卡 / 小时**
|
|
||||||
- 本次部署使用 **8× NVIDIA H200**
|
|
||||||
- 整机每小时成本：
|
|
||||||
|
|
||||||
```
|
|
||||||
8 卡 × 8.0 元/卡/h = 64 元/h
|
|
||||||
```
|
|
||||||
|
|
||||||
### 公式
|
|
||||||
|
|
||||||
对于任意一个 benchmark 结果：
|
|
||||||
|
|
||||||
```
|
|
||||||
每小时处理 input tokens = input_tok/s × 3600
|
|
||||||
每小时生成 output tokens = output_tok/s × 3600
|
|
||||||
|
|
||||||
每 1M input tokens 成本 = 64 / (input_tok/s × 3600 / 1,000,000)
|
|
||||||
每 1M output tokens 成本 = 64 / (output_tok/s × 3600 / 1,000,000)
|
|
||||||
```
|
|
||||||
|
|
||||||
### 各场景成本
|
|
||||||
|
|
||||||
| 场景 | input tok/s | output tok/s | 1M input cost | 1M output cost |
|
|
||||||
|------|-------------|--------------|---------------|----------------|
|
|
||||||
| c=1 | 335.83 | 243.51 | 52.94 元 | 73.01 元 |
|
|
||||||
| c=8 | 1,283.33 | 930.54 | 13.85 元 | 19.10 元 |
|
|
||||||
| c=16 | 1,736.31 | 1,259.00 | 10.24 元 | 14.12 元 |
|
|
||||||
| c=32 | 2,151.87 | 1,560.32 | 8.26 元 | 11.39 元 |
|
|
||||||
| c=64 | 2,775.39 | 2,012.43 | 6.41 元 | 8.83 元 |
|
|
||||||
| c=128 | 4,132.36 | 2,996.37 | 4.30 元 | 5.93 元 |
|
|
||||||
| rps=1 | 286.41 | 207.67 | 62.07 元 | 85.61 元 |
|
|
||||||
| rps=2 | 571.97 | 414.74 | 31.08 元 | 42.86 元 |
|
|
||||||
| rps=4 | 1,139.69 | 826.39 | 15.60 元 | 21.51 元 |
|
|
||||||
| rps=8 | 2,246.34 | 1,628.82 | 7.91 元 | 10.91 元 |
|
|
||||||
| rps=16 | 4,176.94 | 3,028.70 | 4.26 元 | 5.87 元 |
|
|
||||||
|
|
||||||
### 对外定价建议
|
|
||||||
|
|
||||||
裸 GPU 成本不等于对外售价。建议根据 SLA 和毛利率加价：
|
|
||||||
|
|
||||||
| 推荐场景 | 裸成本（input/output） | 说明 |
|
|
||||||
|----------|------------------------|------|
|
|
||||||
| 低延迟在线（c=8） | 13.85 元 / 19.10 元 per 1M | TTFT 147ms，TPOT 8.4ms，适合实时聊天 |
|
|
||||||
| 平衡型在线（c=16） | 10.24 元 / 14.12 元 per 1M | 吞吐与延迟兼顾，主流 API 可参考此档位 |
|
|
||||||
| 高吞吐在线（c=32 / rps=8） | 8.09 元 / 11.15 元 per 1M | 吞吐高、延迟可接受，适合批量在线服务 |
|
|
||||||
| 最大吞吐/离线（c=128） | 4.30 元 / 5.93 元 per 1M | 延迟 8.6s，仅适合离线批处理 |
|
|
||||||
|
|
||||||
> 注意：`rps=16` 虽已接近最大吞吐，但系统已明显过载（E2E 15s），不建议按此成本对外定价。
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 附录 C：最大吞吐压测（优化服务参数）
|
|
||||||
|
|
||||||
为了探索成本下限，重新部署了服务并调优了参数：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
envs/sglang/bin/sglang serve \
|
|
||||||
--trust-remote-code \
|
|
||||||
--model-path /data/models/DeepSeek-V4-Flash \
|
|
||||||
--tp 8 \
|
|
||||||
--moe-runner-backend marlin \
|
|
||||||
--speculative-algorithm EAGLE \
|
|
||||||
--speculative-num-steps 3 \
|
|
||||||
--speculative-eagle-topk 1 \
|
|
||||||
--speculative-num-draft-tokens 4 \
|
|
||||||
--max-running-requests 512 \
|
|
||||||
--chunked-prefill-size 16384 \
|
|
||||||
--host 0.0.0.0 \
|
|
||||||
--port 30000
|
|
||||||
```
|
|
||||||
|
|
||||||
与附录 A/B 的测试相比，主要变化：
|
|
||||||
- `--max-running-requests` 从默认 256 提升到 **512**
|
|
||||||
- `--chunked-prefill-size` 从默认 8192 提升到 **16384**
|
|
||||||
|
|
||||||
### 超大并发扫描结果
|
|
||||||
|
|
||||||
| 并发 | 耗时(s) | req/s | input tok/s | output tok/s | total tok/s | Mean TTFT | Mean TPOT | Mean E2E(ms) |
|
|
||||||
|------|---------|-------|-------------|--------------|-------------|-----------|-----------|--------------|
|
|
||||||
| 128 | 155.26 | 12.88 | 3,710.50 | 2,690.48 | 6,400.98 | 410.55 ms | 48.45 ms | 9,696.87 |
|
|
||||||
| 256 | 90.65 | 22.06 | 6,355.48 | 4,608.35 | 10,963.84 | 509.73 ms | 62.71 ms | 10,869.76 |
|
|
||||||
| 512 | 71.39 | 28.02 | 8,070.04 | 5,851.58 | 13,921.61 | 1,343.73 ms| 121.20 ms | 16,176.14 |
|
|
||||||
| **1024** | **68.21** | **29.32** | **8,445.53** | **6,123.84** | **14,569.37** | **13,265.46 ms** | **105.36 ms** | **27,566.99** |
|
|
||||||
|
|
||||||
### 关键发现
|
|
||||||
|
|
||||||
1. **高并发下吞吐显著提升**：优化参数后，c=256 及以上远超原配置的最高值（7,129 tok/s）；c=1024 达到 **14,569 tok/s**，约为原配置峰值的两倍。
|
|
||||||
2. **c=1024 达到最高吞吐**：total **14,569 tok/s**，output **6,124 tok/s**，但 TTFT 13.3s，系统已严重过载。
|
|
||||||
3. **c=512 是实际可用上限**：total **13,922 tok/s**，output **5,852 tok/s**，虽然 TTFT 1.3s、E2E 16s，但吞吐接近峰值，适合离线批处理。
|
|
||||||
4. **c=256 是高压在线的甜点**：total **10,964 tok/s**，TTFT 510ms，E2E 10.9s，如果业务能容忍 10 秒级响应，这是性价比很高的点。
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 附录 D：最低 Token 成本（基于最大吞吐）
|
|
||||||
|
|
||||||
仍按 **8.0 元/卡时、8 卡整机 64 元/小时** 计算。
|
|
||||||
|
|
||||||
### 公式
|
|
||||||
|
|
||||||
```
|
|
||||||
每 1M input tokens 成本 = 64 / (input_tok/s × 3600 / 1,000,000)
|
|
||||||
每 1M output tokens 成本 = 64 / (output_tok/s × 3600 / 1,000,000)
|
|
||||||
```
|
|
||||||
|
|
||||||
### 最大吞吐场景成本
|
|
||||||
|
|
||||||
| 场景 | input tok/s | output tok/s | 1M input cost | 1M output cost |
|
|
||||||
|------|-------------|--------------|---------------|----------------|
|
|
||||||
| c=128 | 3,710.50 | 2,690.48 | 4.79 元 | 6.61 元 |
|
|
||||||
| c=256 | 6,355.48 | 4,608.35 | 2.80 元 | 3.86 元 |
|
|
||||||
| c=512 | 8,070.04 | 5,851.58 | 2.20 元 | 3.04 元 |
|
|
||||||
| **c=1024** | **8,445.53** | **6,123.84** | **2.10 元** | **2.90 元** |
|
|
||||||
|
|
||||||
### 结论
|
|
||||||
|
|
||||||
在当前硬件和优化参数下：
|
|
||||||
|
|
||||||
- **理论最低 input 成本**：约 **2.10 元 / 1M input tokens**（c=1024）
|
|
||||||
- **理论最低 output 成本**：约 **2.90 元 / 1M output tokens**（c=1024）
|
|
||||||
- **实际可用最低成本**：c=512 时 **2.20 元 / 1M input**、**3.04 元 / 1M output**，此时吞吐已达峰值 95% 以上，且延迟比 c=1024 可控得多。
|
|
||||||
|
|
||||||
> 注意：c=1024 的 TTFT 超过 13 秒，仅适合完全不在意延迟的离线批处理任务；对外 API 服务不建议按此成本定价。
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 附录 E：测试数据长度分布
|
|
||||||
|
|
||||||
本次所有 benchmark 使用的是 `ShareGPT_V4.3_unfiltered_cleaned_split.json` 数据集。`sglang.bench_serving` 在 `--num-prompts 2000` 且 `--seed 42` 的条件下，从数据集中采样出 **2000 条真实对话请求**作为测试负载。
|
|
||||||
|
|
||||||
### 长度统计
|
|
||||||
|
|
||||||
| 指标 | Input Tokens | Output Tokens |
|
|
||||||
|------|--------------|---------------|
|
|
||||||
| 请求数 | 2,000 | 2,000 |
|
|
||||||
| 平均值 | 288.05 | 208.86 |
|
|
||||||
| 标准差 | 420.07 | 217.60 |
|
|
||||||
| 最小值 | 2 | 2 |
|
|
||||||
| P50 | 125 | 150 |
|
|
||||||
| P90 | 723 | 516 |
|
|
||||||
| P95 | 841 | 667 |
|
|
||||||
| P99 | 2,190 | 811 |
|
|
||||||
| 最大值 | 3,996 | 1,655 |
|
|
||||||
| **总量** | **576,098** | **417,728** |
|
|
||||||
|
|
||||||
### 分布特点
|
|
||||||
|
|
||||||
1. **输入长度呈现典型长尾分布**：P50 仅 125 tokens，但 P99 达到 2,190 tokens，最大值 3,996 tokens。说明 ShareGPT 中既有大量短 prompt，也有少量长上下文对话。
|
|
||||||
2. **输出长度相对集中**：P50 150 tokens，P90 516 tokens，P99 811 tokens，最大值 1,655 tokens。大部分回复属于中等长度。
|
|
||||||
3. **输出/输入比约为 0.73**：平均每条请求输出 209 tokens、输入 288 tokens，整体负载偏 decode 侧。
|
|
||||||
4. **与原始 ShareGPT 全集的差异**：原始全集输出长度均值约 1,122 tokens，而 bench 采样后的输出均值仅 209 tokens。这是因为 `sglang.bench_serving` 在处理 sharegpt 数据集时会根据内置规则对输出做截断/采样，以更贴近典型在线 serving 负载。
|
|
||||||
|
|
||||||
### 长度分布图
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
> 图表文件：`bench_results/sglang_8card_systematic_20260704_120819/length_distribution.png`
|
|
||||||
@ -1,156 +0,0 @@
|
|||||||
[
|
|
||||||
{
|
|
||||||
"name": "sharegpt_concurrency_1",
|
|
||||||
"args": "--max-concurrency 1",
|
|
||||||
"duration_s": 1715.45,
|
|
||||||
"request_throughput": 1.17,
|
|
||||||
"input_token_throughput": 335.83,
|
|
||||||
"output_token_throughput": 243.51,
|
|
||||||
"total_token_throughput": 579.34,
|
|
||||||
"mean_ttft_ms": 128.09,
|
|
||||||
"mean_tpot_ms": 4.0,
|
|
||||||
"mean_e2e_latency_ms": 856.77,
|
|
||||||
"accept_length": 2.62,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_concurrency_1.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_concurrency_8",
|
|
||||||
"args": "--max-concurrency 8",
|
|
||||||
"duration_s": 448.91,
|
|
||||||
"request_throughput": 4.46,
|
|
||||||
"input_token_throughput": 1283.33,
|
|
||||||
"output_token_throughput": 930.54,
|
|
||||||
"total_token_throughput": 2213.87,
|
|
||||||
"mean_ttft_ms": 146.76,
|
|
||||||
"mean_tpot_ms": 8.41,
|
|
||||||
"mean_e2e_latency_ms": 1790.65,
|
|
||||||
"accept_length": 2.61,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_concurrency_8.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_concurrency_16",
|
|
||||||
"args": "--max-concurrency 16",
|
|
||||||
"duration_s": 331.79,
|
|
||||||
"request_throughput": 6.03,
|
|
||||||
"input_token_throughput": 1736.31,
|
|
||||||
"output_token_throughput": 1259.0,
|
|
||||||
"total_token_throughput": 2995.3,
|
|
||||||
"mean_ttft_ms": 168.95,
|
|
||||||
"mean_tpot_ms": 12.67,
|
|
||||||
"mean_e2e_latency_ms": 2642.1,
|
|
||||||
"accept_length": 2.61,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_concurrency_16.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_concurrency_32",
|
|
||||||
"args": "--max-concurrency 32",
|
|
||||||
"duration_s": 267.72,
|
|
||||||
"request_throughput": 7.47,
|
|
||||||
"input_token_throughput": 2151.87,
|
|
||||||
"output_token_throughput": 1560.32,
|
|
||||||
"total_token_throughput": 3712.19,
|
|
||||||
"mean_ttft_ms": 206.24,
|
|
||||||
"mean_tpot_ms": 20.72,
|
|
||||||
"mean_e2e_latency_ms": 4249.71,
|
|
||||||
"accept_length": 2.61,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_concurrency_32.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_concurrency_64",
|
|
||||||
"args": "--max-concurrency 64",
|
|
||||||
"duration_s": 207.57,
|
|
||||||
"request_throughput": 9.64,
|
|
||||||
"input_token_throughput": 2775.39,
|
|
||||||
"output_token_throughput": 2012.43,
|
|
||||||
"total_token_throughput": 4787.82,
|
|
||||||
"mean_ttft_ms": 248.41,
|
|
||||||
"mean_tpot_ms": 32.61,
|
|
||||||
"mean_e2e_latency_ms": 6557.39,
|
|
||||||
"accept_length": 2.61,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_concurrency_64.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_concurrency_128",
|
|
||||||
"args": "--max-concurrency 128",
|
|
||||||
"duration_s": 139.41,
|
|
||||||
"request_throughput": 14.35,
|
|
||||||
"input_token_throughput": 4132.36,
|
|
||||||
"output_token_throughput": 2996.37,
|
|
||||||
"total_token_throughput": 7128.73,
|
|
||||||
"mean_ttft_ms": 330.4,
|
|
||||||
"mean_tpot_ms": 46.15,
|
|
||||||
"mean_e2e_latency_ms": 8596.12,
|
|
||||||
"accept_length": 2.61,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_concurrency_128.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_rps_1",
|
|
||||||
"args": "--request-rate 1",
|
|
||||||
"duration_s": 2011.46,
|
|
||||||
"request_throughput": 0.99,
|
|
||||||
"input_token_throughput": 286.41,
|
|
||||||
"output_token_throughput": 207.67,
|
|
||||||
"total_token_throughput": 494.08,
|
|
||||||
"mean_ttft_ms": 146.87,
|
|
||||||
"mean_tpot_ms": 4.91,
|
|
||||||
"mean_e2e_latency_ms": 1007.62,
|
|
||||||
"accept_length": 2.61,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_rps_1.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_rps_2",
|
|
||||||
"args": "--request-rate 2",
|
|
||||||
"duration_s": 1007.21,
|
|
||||||
"request_throughput": 1.99,
|
|
||||||
"input_token_throughput": 571.97,
|
|
||||||
"output_token_throughput": 414.74,
|
|
||||||
"total_token_throughput": 986.71,
|
|
||||||
"mean_ttft_ms": 160.63,
|
|
||||||
"mean_tpot_ms": 5.99,
|
|
||||||
"mean_e2e_latency_ms": 1205.24,
|
|
||||||
"accept_length": 2.61,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_rps_2.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_rps_4",
|
|
||||||
"args": "--request-rate 4",
|
|
||||||
"duration_s": 505.49,
|
|
||||||
"request_throughput": 3.96,
|
|
||||||
"input_token_throughput": 1139.69,
|
|
||||||
"output_token_throughput": 826.39,
|
|
||||||
"total_token_throughput": 1966.08,
|
|
||||||
"mean_ttft_ms": 174.67,
|
|
||||||
"mean_tpot_ms": 9.02,
|
|
||||||
"mean_e2e_latency_ms": 1743.4,
|
|
||||||
"accept_length": 2.6,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_rps_4.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_rps_8",
|
|
||||||
"args": "--request-rate 8",
|
|
||||||
"duration_s": 256.46,
|
|
||||||
"request_throughput": 7.8,
|
|
||||||
"input_token_throughput": 2246.34,
|
|
||||||
"output_token_throughput": 1628.82,
|
|
||||||
"total_token_throughput": 3875.15,
|
|
||||||
"mean_ttft_ms": 216.65,
|
|
||||||
"mean_tpot_ms": 25.35,
|
|
||||||
"mean_e2e_latency_ms": 4542.64,
|
|
||||||
"accept_length": 2.6,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_rps_8.json"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "sharegpt_rps_16",
|
|
||||||
"args": "--request-rate 16",
|
|
||||||
"duration_s": 137.92,
|
|
||||||
"request_throughput": 14.5,
|
|
||||||
"input_token_throughput": 4176.94,
|
|
||||||
"output_token_throughput": 3028.7,
|
|
||||||
"total_token_throughput": 7205.64,
|
|
||||||
"mean_ttft_ms": 377.28,
|
|
||||||
"mean_tpot_ms": 87.16,
|
|
||||||
"mean_e2e_latency_ms": 15048.09,
|
|
||||||
"accept_length": 2.61,
|
|
||||||
"output_file": "bench_results/sglang_8card_systematic_20260704_120819/sharegpt_rps_16.json"
|
|
||||||
}
|
|
||||||
]
|
|
||||||
@ -1,39 +0,0 @@
|
|||||||
{
|
|
||||||
"model": "/data/models/Qwen3-4B",
|
|
||||||
"draft_model": "deepseek-ai/dspark_qwen3_4b_block7",
|
|
||||||
"url": "http://127.0.0.1:30003/v1/completions",
|
|
||||||
"max_tokens": 64,
|
|
||||||
"num_prompts": 20,
|
|
||||||
"dataset": "/data/user1/yy/datasets/ShareGPT_filtered_chat.json",
|
|
||||||
"timestamp": "20260705_121046",
|
|
||||||
"results": [
|
|
||||||
{
|
|
||||||
"concurrency": 1,
|
|
||||||
"num_prompts": 20,
|
|
||||||
"duration_s": 0.07108968612737954,
|
|
||||||
"success_count": 0,
|
|
||||||
"error": "all requests failed",
|
|
||||||
"errors": [
|
|
||||||
"404, message='Not Found', url='http://127.0.0.1:30003/v1/completions'",
|
|
||||||
"404, message='Not Found', url='http://127.0.0.1:30003/v1/completions'",
|
|
||||||
"404, message='Not Found', url='http://127.0.0.1:30003/v1/completions'",
|
|
||||||
"404, message='Not Found', url='http://127.0.0.1:30003/v1/completions'",
|
|
||||||
"404, message='Not Found', url='http://127.0.0.1:30003/v1/completions'"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"concurrency": 4,
|
|
||||||
"num_prompts": 20,
|
|
||||||
"duration_s": 0.036050053080543876,
|
|
||||||
"success_count": 0,
|
|
||||||
"error": "all requests failed",
|
|
||||||
"errors": [
|
|
||||||
"404, message='Not Found', url='http://127.0.0.1:30003/v1/completions'",
|
|
||||||
"404, message='Not Found', url='http://127.0.0.1:30003/v1/completions'",
|
|
||||||
"404, message='Not Found', url='http://127.0.0.1:30003/v1/completions'",
|
|
||||||
"404, message='Not Found', url='http://127.0.0.1:30003/v1/completions'",
|
|
||||||
"404, message='Not Found', url='http://127.0.0.1:30003/v1/completions'"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
@ -1,59 +0,0 @@
|
|||||||
{
|
|
||||||
"model": "/data/models/Qwen3-4B",
|
|
||||||
"draft_model": "deepseek-ai/dspark_qwen3_4b_block7",
|
|
||||||
"url": "http://127.0.0.1:30003/v1/completions",
|
|
||||||
"max_tokens": 64,
|
|
||||||
"num_prompts": 20,
|
|
||||||
"dataset": "/data/user1/yy/datasets/ShareGPT_filtered_chat.json",
|
|
||||||
"timestamp": "20260705_121218",
|
|
||||||
"results": [
|
|
||||||
{
|
|
||||||
"concurrency": 1,
|
|
||||||
"num_prompts": 20,
|
|
||||||
"duration_s": 3.1493232590146363,
|
|
||||||
"success_count": 20,
|
|
||||||
"fail_count": 0,
|
|
||||||
"input_tokens": 6132,
|
|
||||||
"output_tokens": 456,
|
|
||||||
"total_tokens": 6588,
|
|
||||||
"request_throughput": 6.350570695704836,
|
|
||||||
"input_throughput": 1947.0849753031027,
|
|
||||||
"output_throughput": 144.79301186207027,
|
|
||||||
"total_throughput": 2091.877987165173,
|
|
||||||
"mean_ttft_ms": 55.51949484506622,
|
|
||||||
"p50_ttft_ms": 27.64087135437876,
|
|
||||||
"p99_ttft_ms": 295.08487760089326,
|
|
||||||
"mean_tpot_ms": 4.6249661131817925,
|
|
||||||
"p50_tpot_ms": 4.634099831345484,
|
|
||||||
"p99_tpot_ms": 4.827384204164935,
|
|
||||||
"mean_e2e_ms": 156.8067875574343,
|
|
||||||
"p50_e2e_ms": 148.02213886287063,
|
|
||||||
"p99_e2e_ms": 406.6163514624348,
|
|
||||||
"errors": []
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"concurrency": 4,
|
|
||||||
"num_prompts": 20,
|
|
||||||
"duration_s": 1.0271000929642469,
|
|
||||||
"success_count": 20,
|
|
||||||
"fail_count": 0,
|
|
||||||
"input_tokens": 6132,
|
|
||||||
"output_tokens": 450,
|
|
||||||
"total_tokens": 6582,
|
|
||||||
"request_throughput": 19.472298889857267,
|
|
||||||
"input_throughput": 5970.206839630238,
|
|
||||||
"output_throughput": 438.1267250217885,
|
|
||||||
"total_throughput": 6408.333564652026,
|
|
||||||
"mean_ttft_ms": 82.42244796128944,
|
|
||||||
"p50_ttft_ms": 37.77908312622458,
|
|
||||||
"p99_ttft_ms": 263.8731326162815,
|
|
||||||
"mean_tpot_ms": 5.172366561559164,
|
|
||||||
"p50_tpot_ms": 5.150655882704692,
|
|
||||||
"p99_tpot_ms": 5.708390201389117,
|
|
||||||
"mean_e2e_ms": 193.68963547749445,
|
|
||||||
"p50_e2e_ms": 153.2427944475785,
|
|
||||||
"p99_e2e_ms": 387.3923780536279,
|
|
||||||
"errors": []
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
@ -1,131 +0,0 @@
|
|||||||
{
|
|
||||||
"model": "/data/models/Qwen3-4B",
|
|
||||||
"draft_model": "deepseek-ai/dspark_qwen3_4b_block7",
|
|
||||||
"url": "http://127.0.0.1:30003/v1/completions",
|
|
||||||
"max_tokens": 256,
|
|
||||||
"num_prompts": 500,
|
|
||||||
"dataset": "/data/user1/yy/datasets/ShareGPT_filtered_chat.json",
|
|
||||||
"timestamp": "20260705_121256",
|
|
||||||
"results": [
|
|
||||||
{
|
|
||||||
"concurrency": 1,
|
|
||||||
"num_prompts": 500,
|
|
||||||
"duration_s": 181.22913801996037,
|
|
||||||
"success_count": 500,
|
|
||||||
"fail_count": 0,
|
|
||||||
"input_tokens": 108545,
|
|
||||||
"output_tokens": 36681,
|
|
||||||
"total_tokens": 145226,
|
|
||||||
"request_throughput": 2.7589382450460618,
|
|
||||||
"input_throughput": 598.9379036170495,
|
|
||||||
"output_throughput": 202.4012275330692,
|
|
||||||
"total_throughput": 801.3391311501188,
|
|
||||||
"mean_ttft_ms": 17.586736707482487,
|
|
||||||
"p50_ttft_ms": 15.236841514706612,
|
|
||||||
"p99_ttft_ms": 43.418583339080215,
|
|
||||||
"mean_tpot_ms": 4.756160026498775,
|
|
||||||
"p50_tpot_ms": 4.747943201144331,
|
|
||||||
"p99_tpot_ms": 4.845208972490266,
|
|
||||||
"mean_e2e_ms": 361.93272305326536,
|
|
||||||
"p50_e2e_ms": 369.4158981088549,
|
|
||||||
"p99_e2e_ms": 552.2739849891514,
|
|
||||||
"errors": []
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"concurrency": 4,
|
|
||||||
"num_prompts": 500,
|
|
||||||
"duration_s": 49.20666282507591,
|
|
||||||
"success_count": 500,
|
|
||||||
"fail_count": 0,
|
|
||||||
"input_tokens": 108545,
|
|
||||||
"output_tokens": 36991,
|
|
||||||
"total_tokens": 145536,
|
|
||||||
"request_throughput": 10.161225559584139,
|
|
||||||
"input_throughput": 2205.900456730121,
|
|
||||||
"output_throughput": 751.7477893491538,
|
|
||||||
"total_throughput": 2957.6482460792745,
|
|
||||||
"mean_ttft_ms": 26.531575095374137,
|
|
||||||
"p50_ttft_ms": 24.292025598697364,
|
|
||||||
"p99_ttft_ms": 41.29285377450287,
|
|
||||||
"mean_tpot_ms": 5.007692315964597,
|
|
||||||
"p50_tpot_ms": 4.998277087638343,
|
|
||||||
"p99_tpot_ms": 5.130429720718591,
|
|
||||||
"mean_e2e_ms": 392.14430282171816,
|
|
||||||
"p50_e2e_ms": 400.28460952453315,
|
|
||||||
"p99_e2e_ms": 625.0063395127652,
|
|
||||||
"errors": []
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"concurrency": 16,
|
|
||||||
"num_prompts": 500,
|
|
||||||
"duration_s": 14.20007478701882,
|
|
||||||
"success_count": 500,
|
|
||||||
"fail_count": 0,
|
|
||||||
"input_tokens": 108545,
|
|
||||||
"output_tokens": 36529,
|
|
||||||
"total_tokens": 145074,
|
|
||||||
"request_throughput": 35.21108215972787,
|
|
||||||
"input_throughput": 7643.973826055324,
|
|
||||||
"output_throughput": 2572.451240425399,
|
|
||||||
"total_throughput": 10216.425066480724,
|
|
||||||
"mean_ttft_ms": 31.879239567089826,
|
|
||||||
"p50_ttft_ms": 28.826430439949036,
|
|
||||||
"p99_ttft_ms": 74.12134333979331,
|
|
||||||
"mean_tpot_ms": 5.771187238080647,
|
|
||||||
"p50_tpot_ms": 5.76495784573639,
|
|
||||||
"p99_tpot_ms": 6.1421910060182805,
|
|
||||||
"mean_e2e_ms": 447.76513252267614,
|
|
||||||
"p50_e2e_ms": 459.7155440133065,
|
|
||||||
"p99_e2e_ms": 669.6232496039011,
|
|
||||||
"errors": []
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"concurrency": 64,
|
|
||||||
"num_prompts": 500,
|
|
||||||
"duration_s": 7.960247536888346,
|
|
||||||
"success_count": 500,
|
|
||||||
"fail_count": 0,
|
|
||||||
"input_tokens": 108545,
|
|
||||||
"output_tokens": 36583,
|
|
||||||
"total_tokens": 145128,
|
|
||||||
"request_throughput": 62.81211704572815,
|
|
||||||
"input_throughput": 13635.882489457124,
|
|
||||||
"output_throughput": 4595.711355767746,
|
|
||||||
"total_throughput": 18231.59384522487,
|
|
||||||
"mean_ttft_ms": 73.04830395104364,
|
|
||||||
"p50_ttft_ms": 53.465207340195775,
|
|
||||||
"p99_ttft_ms": 252.97995713772252,
|
|
||||||
"mean_tpot_ms": 12.42267764661875,
|
|
||||||
"p50_tpot_ms": 12.61188557741512,
|
|
||||||
"p99_tpot_ms": 13.063075978620708,
|
|
||||||
"mean_e2e_ms": 968.1061067078263,
|
|
||||||
"p50_e2e_ms": 967.5668340642005,
|
|
||||||
"p99_e2e_ms": 1512.9108975501729,
|
|
||||||
"errors": []
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"concurrency": 128,
|
|
||||||
"num_prompts": 500,
|
|
||||||
"duration_s": 7.319521516095847,
|
|
||||||
"success_count": 500,
|
|
||||||
"fail_count": 0,
|
|
||||||
"input_tokens": 108545,
|
|
||||||
"output_tokens": 36613,
|
|
||||||
"total_tokens": 145158,
|
|
||||||
"request_throughput": 68.31047615619204,
|
|
||||||
"input_throughput": 14829.52126874773,
|
|
||||||
"output_throughput": 5002.102927013319,
|
|
||||||
"total_throughput": 19831.62419576105,
|
|
||||||
"mean_ttft_ms": 145.49234842788428,
|
|
||||||
"p50_ttft_ms": 90.75760398991406,
|
|
||||||
"p99_ttft_ms": 423.2492828951217,
|
|
||||||
"mean_tpot_ms": 22.034148180580335,
|
|
||||||
"p50_tpot_ms": 23.052740126916806,
|
|
||||||
"p99_tpot_ms": 23.587740010073134,
|
|
||||||
"mean_e2e_ms": 1730.9650285840034,
|
|
||||||
"p50_e2e_ms": 1733.5386699996889,
|
|
||||||
"p99_e2e_ms": 2722.885905068833,
|
|
||||||
"errors": []
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
@ -1,60 +0,0 @@
|
|||||||
# DSpark Benchmark Scripts (2026-07-07)
|
|
||||||
|
|
||||||
## Location
|
|
||||||
|
|
||||||
`/data/user1/yy/scripts/benchmark_dspark_0707`
|
|
||||||
|
|
||||||
## Scripts
|
|
||||||
|
|
||||||
| Script | Purpose | Output Location |
|
|
||||||
|---|---|---|
|
|
||||||
| `bench_dspark_p1.sh` | Quick benchmark: chat_standard, generation_standard, summarization | `bench_results/dspark_grid_${RUN_ID}/p1_quick/...` |
|
|
||||||
| `bench_dspark_p2.sh` | Core benchmark: chat_short, rag_medium, long_context_probe | `bench_results/dspark_grid_${RUN_ID}/p2_core/...` |
|
|
||||||
| `bench_dspark_p3.sh` | Extension benchmark: stress_standard, decode_heavy | `bench_results/dspark_grid_${RUN_ID}/p3_extension/...` |
|
|
||||||
| `bench_dspark_focused.sh` | Focused sweep used for spec-tokens comparison | `bench_results/dspark_grid_${RUN_ID}/focused/...` or `bench_results/dspark_st_comparison_${RUN_ID}/focused/...` |
|
|
||||||
| `run_dspark_benchmark_grid.sh` | Orchestrator: starts vllm-dspark server, runs P1/P2/P3, stops server | `bench_results/dspark_grid_${RUN_ID}/` |
|
|
||||||
| `run_dspark_st_comparison.sh` | Orchestrator: compares `--spec-tokens 3` vs `5` | `bench_results/dspark_st_comparison_${RUN_ID}/` |
|
|
||||||
| `parse_results.py` | Parses grid logs and generates `report.md` | Writes to `bench_results/<result_root>/report.md` |
|
|
||||||
| `parse_st_comparison.py` | Parses spec-tokens comparison logs | Writes `comparison_report.md` under result root |
|
|
||||||
| `parse_eagle_vs_dspark.py` | Merges DSpark st comparison with SGLang EAGLE results | Writes `dspark_vs_eagle_report.md` under EAGLE result root |
|
|
||||||
|
|
||||||
## Common Environment
|
|
||||||
|
|
||||||
- Python env: `/data/user1/yy/envs/vllm-dspark` for server
|
|
||||||
- Benchmark client: `/data/user1/yy/envs/sglang/bin/python -m sglang.bench_serving --backend vllm`
|
|
||||||
- Model: `/data/models/DeepSeek-V4-Flash-DSpark`
|
|
||||||
- Default port: `30004`
|
|
||||||
- Default warmup: `100` requests
|
|
||||||
|
|
||||||
## Usage
|
|
||||||
|
|
||||||
### Full grid benchmark
|
|
||||||
|
|
||||||
```bash
|
|
||||||
bash scripts/benchmark_dspark_0707/run_dspark_benchmark_grid.sh
|
|
||||||
```
|
|
||||||
|
|
||||||
Results land in `bench_results/dspark_grid_YYYYMMDD-HHMMSS/`.
|
|
||||||
|
|
||||||
### Spec-tokens comparison
|
|
||||||
|
|
||||||
```bash
|
|
||||||
bash scripts/benchmark_dspark_0707/run_dspark_st_comparison.sh
|
|
||||||
```
|
|
||||||
|
|
||||||
Results land in `bench_results/dspark_st_comparison_YYYYMMDD-HHMMSS/`.
|
|
||||||
|
|
||||||
### Parse existing results
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# Grid
|
|
||||||
/data/user1/yy/envs/sglang/bin/python scripts/benchmark_dspark_0707/parse_results.py /data/user1/yy/bench_results/dspark_grid_<run_id>
|
|
||||||
|
|
||||||
# Spec-tokens comparison
|
|
||||||
/data/user1/yy/envs/sglang/bin/python scripts/benchmark_dspark_0707/parse_st_comparison.py /data/user1/yy/bench_results/dspark_st_comparison_<run_id>
|
|
||||||
```
|
|
||||||
|
|
||||||
## Notes
|
|
||||||
|
|
||||||
- These scripts were originally located in `/data/user1/yy/benchmark_dspark_0707/` and moved here on 2026-07-08.
|
|
||||||
- `run_dspark_benchmark_grid.sh` and `run_dspark_st_comparison.sh` assume server start scripts are in `scripts/` (one directory up).
|
|
||||||
@ -1,64 +0,0 @@
|
|||||||
#!/usr/bin/env bash
|
|
||||||
set -Eeuo pipefail
|
|
||||||
|
|
||||||
# Focused benchmark for spec-tokens comparison.
|
|
||||||
# Covers short/medium/long input and low/medium/high concurrency.
|
|
||||||
|
|
||||||
BACKEND="vllm"
|
|
||||||
PORT="${PORT:-30004}"
|
|
||||||
MODEL="/data/models/DeepSeek-V4-Flash-DSpark"
|
|
||||||
BENCH_PY="/data/user1/yy/envs/sglang/bin/python"
|
|
||||||
DATASET="/data/user1/yy/datasets/ShareGPT_filtered_chat.json"
|
|
||||||
RESULT_ROOT="${RESULT_ROOT:-/data/user1/yy/bench_results/dspark_grid}"
|
|
||||||
WARMUP_REQUESTS="${WARMUP_REQUESTS:-100}"
|
|
||||||
PHASE="focused"
|
|
||||||
RUN_ID="${RUN_ID:-$(date '+%Y%m%d-%H%M%S')}"
|
|
||||||
|
|
||||||
num_prompts() {
|
|
||||||
local c="$1"
|
|
||||||
if (( c <= 4 )); then
|
|
||||||
local n=$((c * 8))
|
|
||||||
(( n < 32 )) && n=32
|
|
||||||
echo "${n}"
|
|
||||||
elif (( c <= 32 )); then
|
|
||||||
echo $((c * 16))
|
|
||||||
else
|
|
||||||
echo $((c * 8))
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
run_case() {
|
|
||||||
local scenario="$1"
|
|
||||||
local input_len="$2"
|
|
||||||
local output_len="$3"
|
|
||||||
local concurrencies="$4"
|
|
||||||
for concurrency in ${concurrencies}; do
|
|
||||||
local prompts
|
|
||||||
prompts="$(num_prompts "${concurrency}")"
|
|
||||||
local out_dir="${RESULT_ROOT}/${PHASE}/${scenario}/${RUN_ID}"
|
|
||||||
mkdir -p "${out_dir}"
|
|
||||||
echo "phase=${PHASE} scenario=${scenario} input=${input_len} output=${output_len} concurrency=${concurrency} prompts=${prompts} warmup=${WARMUP_REQUESTS}"
|
|
||||||
"${BENCH_PY}" -m sglang.bench_serving \
|
|
||||||
--backend "${BACKEND}" \
|
|
||||||
--host 127.0.0.1 \
|
|
||||||
--port "${PORT}" \
|
|
||||||
--model "${MODEL}" \
|
|
||||||
--dataset-name random \
|
|
||||||
--dataset-path "${DATASET}" \
|
|
||||||
--random-input-len "${input_len}" \
|
|
||||||
--random-output-len "${output_len}" \
|
|
||||||
--num-prompts "${prompts}" \
|
|
||||||
--max-concurrency "${concurrency}" \
|
|
||||||
--warmup-requests "${WARMUP_REQUESTS}" \
|
|
||||||
2>&1 | tee "${out_dir}/c${concurrency}.log"
|
|
||||||
done
|
|
||||||
}
|
|
||||||
|
|
||||||
curl --fail --silent --show-error --max-time 5 "http://127.0.0.1:${PORT}/health" >/dev/null
|
|
||||||
run_case "chat_short" 512 256 "1 8 16 32 64"
|
|
||||||
run_case "chat_standard" 1000 256 "64"
|
|
||||||
run_case "generation_standard" 1000 1000 "64"
|
|
||||||
run_case "rag_medium" 4000 512 "1 8 32"
|
|
||||||
run_case "long_context_probe" 16000 512 "1 4 8"
|
|
||||||
run_case "stress_standard" 1000 256 "64 96 128"
|
|
||||||
run_case "decode_heavy" 512 2000 "1 32"
|
|
||||||
@ -1,60 +0,0 @@
|
|||||||
#!/usr/bin/env bash
|
|
||||||
set -Eeuo pipefail
|
|
||||||
|
|
||||||
# P1 quick benchmark for vllm-dspark
|
|
||||||
# Mirrored from benchmark_grid_0707/h200_vllm_p1_grid.sh
|
|
||||||
|
|
||||||
BACKEND="vllm"
|
|
||||||
PORT="${PORT:-30004}"
|
|
||||||
MODEL="/data/models/DeepSeek-V4-Flash-DSpark"
|
|
||||||
BENCH_PY="/data/user1/yy/envs/sglang/bin/python"
|
|
||||||
DATASET="/data/user1/yy/datasets/ShareGPT_filtered_chat.json"
|
|
||||||
RESULT_ROOT="${RESULT_ROOT:-/data/user1/yy/bench_results/dspark_grid}"
|
|
||||||
WARMUP_REQUESTS="${WARMUP_REQUESTS:-100}"
|
|
||||||
PHASE="p1_quick"
|
|
||||||
RUN_ID="${RUN_ID:-$(date '+%Y%m%d-%H%M%S')}"
|
|
||||||
|
|
||||||
num_prompts() {
|
|
||||||
local c="$1"
|
|
||||||
if (( c <= 4 )); then
|
|
||||||
local n=$((c * 8))
|
|
||||||
(( n < 32 )) && n=32
|
|
||||||
echo "${n}"
|
|
||||||
elif (( c <= 32 )); then
|
|
||||||
echo $((c * 16))
|
|
||||||
else
|
|
||||||
echo $((c * 8))
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
run_case() {
|
|
||||||
local scenario="$1"
|
|
||||||
local input_len="$2"
|
|
||||||
local output_len="$3"
|
|
||||||
local concurrencies="$4"
|
|
||||||
for concurrency in ${concurrencies}; do
|
|
||||||
local prompts
|
|
||||||
prompts="$(num_prompts "${concurrency}")"
|
|
||||||
local out_dir="${RESULT_ROOT}/${PHASE}/${scenario}/${RUN_ID}"
|
|
||||||
mkdir -p "${out_dir}"
|
|
||||||
echo "phase=${PHASE} scenario=${scenario} input=${input_len} output=${output_len} concurrency=${concurrency} prompts=${prompts}"
|
|
||||||
"${BENCH_PY}" -m sglang.bench_serving \
|
|
||||||
--backend "${BACKEND}" \
|
|
||||||
--host 127.0.0.1 \
|
|
||||||
--port "${PORT}" \
|
|
||||||
--model "${MODEL}" \
|
|
||||||
--dataset-name random \
|
|
||||||
--dataset-path "${DATASET}" \
|
|
||||||
--random-input-len "${input_len}" \
|
|
||||||
--random-output-len "${output_len}" \
|
|
||||||
--num-prompts "${prompts}" \
|
|
||||||
--max-concurrency "${concurrency}" \
|
|
||||||
--warmup-requests "${WARMUP_REQUESTS}" \
|
|
||||||
2>&1 | tee "${out_dir}/c${concurrency}.log"
|
|
||||||
done
|
|
||||||
}
|
|
||||||
|
|
||||||
curl --fail --silent --show-error --max-time 5 "http://127.0.0.1:${PORT}/health" >/dev/null
|
|
||||||
run_case "chat_standard" 1000 256 "1 8 16 32"
|
|
||||||
run_case "generation_standard" 1000 1000 "1 8 16 32"
|
|
||||||
run_case "summarization" 8000 1000 "1 4 8 16"
|
|
||||||
@ -1,63 +0,0 @@
|
|||||||
#!/usr/bin/env bash
|
|
||||||
set -Eeuo pipefail
|
|
||||||
|
|
||||||
# P2 core benchmark for vllm-dspark
|
|
||||||
# Mirrored from benchmark_grid_0707/h200_vllm_p2_grid.sh
|
|
||||||
|
|
||||||
BACKEND="vllm"
|
|
||||||
PORT="${PORT:-30004}"
|
|
||||||
MODEL="/data/models/DeepSeek-V4-Flash-DSpark"
|
|
||||||
BENCH_PY="/data/user1/yy/envs/sglang/bin/python"
|
|
||||||
DATASET="/data/user1/yy/datasets/ShareGPT_filtered_chat.json"
|
|
||||||
RESULT_ROOT="${RESULT_ROOT:-/data/user1/yy/bench_results/dspark_grid}"
|
|
||||||
WARMUP_REQUESTS="${WARMUP_REQUESTS:-100}"
|
|
||||||
PHASE="p2_core"
|
|
||||||
RUN_ID="${RUN_ID:-$(date '+%Y%m%d-%H%M%S')}"
|
|
||||||
|
|
||||||
num_prompts() {
|
|
||||||
local c="$1"
|
|
||||||
if (( c <= 4 )); then
|
|
||||||
local n=$((c * 8))
|
|
||||||
(( n < 32 )) && n=32
|
|
||||||
echo "${n}"
|
|
||||||
elif (( c <= 32 )); then
|
|
||||||
echo $((c * 16))
|
|
||||||
else
|
|
||||||
echo $((c * 8))
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
run_case() {
|
|
||||||
local scenario="$1"
|
|
||||||
local input_len="$2"
|
|
||||||
local output_len="$3"
|
|
||||||
local concurrencies="$4"
|
|
||||||
for concurrency in ${concurrencies}; do
|
|
||||||
local prompts
|
|
||||||
prompts="$(num_prompts "${concurrency}")"
|
|
||||||
local out_dir="${RESULT_ROOT}/${PHASE}/${scenario}/${RUN_ID}"
|
|
||||||
mkdir -p "${out_dir}"
|
|
||||||
echo "phase=${PHASE} scenario=${scenario} input=${input_len} output=${output_len} concurrency=${concurrency} prompts=${prompts}"
|
|
||||||
"${BENCH_PY}" -m sglang.bench_serving \
|
|
||||||
--backend "${BACKEND}" \
|
|
||||||
--host 127.0.0.1 \
|
|
||||||
--port "${PORT}" \
|
|
||||||
--model "${MODEL}" \
|
|
||||||
--dataset-name random \
|
|
||||||
--dataset-path "${DATASET}" \
|
|
||||||
--random-input-len "${input_len}" \
|
|
||||||
--random-output-len "${output_len}" \
|
|
||||||
--num-prompts "${prompts}" \
|
|
||||||
--max-concurrency "${concurrency}" \
|
|
||||||
--warmup-requests "${WARMUP_REQUESTS}" \
|
|
||||||
2>&1 | tee "${out_dir}/c${concurrency}.log"
|
|
||||||
done
|
|
||||||
}
|
|
||||||
|
|
||||||
curl --fail --silent --show-error --max-time 5 "http://127.0.0.1:${PORT}/health" >/dev/null
|
|
||||||
run_case "chat_short" 512 256 "1 8 16 32 64"
|
|
||||||
run_case "chat_standard" 1000 256 "64"
|
|
||||||
run_case "generation_standard" 1000 1000 "64"
|
|
||||||
run_case "rag_medium" 4000 512 "1 4 8 16 32"
|
|
||||||
run_case "summarization" 8000 1000 "32"
|
|
||||||
run_case "long_context_probe" 16000 512 "1 2 4 8 16"
|
|
||||||
@ -1,61 +0,0 @@
|
|||||||
#!/usr/bin/env bash
|
|
||||||
set -Eeuo pipefail
|
|
||||||
|
|
||||||
# P3 extension benchmark for vllm-dspark
|
|
||||||
# Mirrored from benchmark_grid_0707/h200_vllm_p3_grid.sh
|
|
||||||
|
|
||||||
BACKEND="vllm"
|
|
||||||
PORT="${PORT:-30004}"
|
|
||||||
MODEL="/data/models/DeepSeek-V4-Flash-DSpark"
|
|
||||||
BENCH_PY="/data/user1/yy/envs/sglang/bin/python"
|
|
||||||
DATASET="/data/user1/yy/datasets/ShareGPT_filtered_chat.json"
|
|
||||||
RESULT_ROOT="${RESULT_ROOT:-/data/user1/yy/bench_results/dspark_grid}"
|
|
||||||
WARMUP_REQUESTS="${WARMUP_REQUESTS:-100}"
|
|
||||||
PHASE="p3_extension"
|
|
||||||
RUN_ID="${RUN_ID:-$(date '+%Y%m%d-%H%M%S')}"
|
|
||||||
|
|
||||||
num_prompts() {
|
|
||||||
local c="$1"
|
|
||||||
if (( c <= 4 )); then
|
|
||||||
local n=$((c * 8))
|
|
||||||
(( n < 32 )) && n=32
|
|
||||||
echo "${n}"
|
|
||||||
elif (( c <= 32 )); then
|
|
||||||
echo $((c * 16))
|
|
||||||
else
|
|
||||||
echo $((c * 8))
|
|
||||||
fi
|
|
||||||
}
|
|
||||||
|
|
||||||
run_case() {
|
|
||||||
local scenario="$1"
|
|
||||||
local input_len="$2"
|
|
||||||
local output_len="$3"
|
|
||||||
local concurrencies="$4"
|
|
||||||
for concurrency in ${concurrencies}; do
|
|
||||||
local prompts
|
|
||||||
prompts="$(num_prompts "${concurrency}")"
|
|
||||||
local out_dir="${RESULT_ROOT}/${PHASE}/${scenario}/${RUN_ID}"
|
|
||||||
mkdir -p "${out_dir}"
|
|
||||||
echo "phase=${PHASE} scenario=${scenario} input=${input_len} output=${output_len} concurrency=${concurrency} prompts=${prompts}"
|
|
||||||
"${BENCH_PY}" -m sglang.bench_serving \
|
|
||||||
--backend "${BACKEND}" \
|
|
||||||
--host 127.0.0.1 \
|
|
||||||
--port "${PORT}" \
|
|
||||||
--model "${MODEL}" \
|
|
||||||
--dataset-name random \
|
|
||||||
--dataset-path "${DATASET}" \
|
|
||||||
--random-input-len "${input_len}" \
|
|
||||||
--random-output-len "${output_len}" \
|
|
||||||
--num-prompts "${prompts}" \
|
|
||||||
--max-concurrency "${concurrency}" \
|
|
||||||
--warmup-requests "${WARMUP_REQUESTS}" \
|
|
||||||
2>&1 | tee "${out_dir}/c${concurrency}.log"
|
|
||||||
done
|
|
||||||
}
|
|
||||||
|
|
||||||
curl --fail --silent --show-error --max-time 5 "http://127.0.0.1:${PORT}/health" >/dev/null
|
|
||||||
run_case "decode_heavy" 512 2000 "1 8 16 32"
|
|
||||||
run_case "long_rag" 32000 512 "1 2 4 8"
|
|
||||||
run_case "stress_standard" 1000 256 "96 128"
|
|
||||||
run_case "stress_generation" 1000 1000 "96 128"
|
|
||||||
@ -1,258 +0,0 @@
|
|||||||
#!/usr/bin/env python3
|
|
||||||
"""Parse EAGLE focused benchmark logs and merge with DSpark st=3/st=5 report."""
|
|
||||||
|
|
||||||
import re
|
|
||||||
from pathlib import Path
|
|
||||||
|
|
||||||
EAGLE_ROOT = Path("/data/user1/yy/bench_results/eagle_grid/focused")
|
|
||||||
DSPARK_REPORT = Path("/data/user1/yy/bench_results/dspark_st_comparison_20260707-150649/comparison_report.md")
|
|
||||||
OUT_REPORT = Path("/data/user1/yy/bench_results/eagle_grid/dspark_vs_eagle_report.md")
|
|
||||||
|
|
||||||
|
|
||||||
def parse_log(log_path: Path) -> dict:
|
|
||||||
text = log_path.read_text(errors="ignore")
|
|
||||||
lines = text.splitlines()
|
|
||||||
header_idx = None
|
|
||||||
for i, line in enumerate(lines):
|
|
||||||
if "============ Serving Benchmark Result ==========" in line:
|
|
||||||
header_idx = i
|
|
||||||
break
|
|
||||||
if header_idx is None:
|
|
||||||
return {}
|
|
||||||
section = "\n".join(lines[header_idx:])
|
|
||||||
|
|
||||||
def get_float(pattern):
|
|
||||||
m = re.search(pattern + r"\s+([\d.]+)", section)
|
|
||||||
return float(m.group(1)) if m else None
|
|
||||||
|
|
||||||
def get_int(pattern):
|
|
||||||
m = re.search(pattern + r"\s+(\d+)", section)
|
|
||||||
return int(m.group(1)) if m else None
|
|
||||||
|
|
||||||
return {
|
|
||||||
"duration_s": get_float(r"Benchmark duration \(s\):"),
|
|
||||||
"successful_requests": get_int(r"Successful requests:"),
|
|
||||||
"req_throughput": get_float(r"Request throughput \(req/s\):"),
|
|
||||||
"input_tok_throughput": get_float(r"Input token throughput \(tok/s\):"),
|
|
||||||
"output_tok_throughput": get_float(r"Output token throughput \(tok/s\):"),
|
|
||||||
"total_tok_throughput": get_float(r"Total token throughput \(tok/s\):"),
|
|
||||||
"mean_e2e_ms": get_float(r"Mean E2E Latency \(ms\):"),
|
|
||||||
"p95_e2e_ms": get_float(r"P95 E2E Latency \(ms\):"),
|
|
||||||
"p99_e2e_ms": get_float(r"P99 E2E Latency \(ms\):"),
|
|
||||||
"mean_ttft_ms": get_float(r"Mean TTFT \(ms\):"),
|
|
||||||
"p95_ttft_ms": get_float(r"P95 TTFT \(ms\):"),
|
|
||||||
"p99_ttft_ms": get_float(r"P99 TTFT \(ms\):"),
|
|
||||||
"mean_tpot_ms": get_float(r"Mean TPOT \(ms\):"),
|
|
||||||
"p95_tpot_ms": get_float(r"P95 TPOT \(ms\):"),
|
|
||||||
"p99_tpot_ms": get_float(r"P99 TPOT \(ms\):"),
|
|
||||||
"mean_itl_ms": get_float(r"Mean ITL \(ms\):"),
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def parse_dspark_report(path: Path) -> list:
|
|
||||||
"""Extract rows from the DSpark comparison markdown table."""
|
|
||||||
rows = []
|
|
||||||
text = path.read_text(errors="ignore")
|
|
||||||
in_table = False
|
|
||||||
for line in text.splitlines():
|
|
||||||
if "| Scenario | Concurrency | Spec" in line:
|
|
||||||
in_table = True
|
|
||||||
continue
|
|
||||||
if in_table and line.startswith("|"):
|
|
||||||
parts = [p.strip() for p in line.split("|") if p.strip()]
|
|
||||||
if len(parts) < 14 or parts[0] == "Scenario" or set(parts[0]) <= set("-:"):
|
|
||||||
continue
|
|
||||||
rows.append({
|
|
||||||
"scenario": parts[0],
|
|
||||||
"concurrency": int(parts[1]),
|
|
||||||
"spec": parts[2],
|
|
||||||
"duration_s": float(parts[3]),
|
|
||||||
"req_throughput": float(parts[4]),
|
|
||||||
"output_tok_throughput": float(parts[5]),
|
|
||||||
"total_tok_throughput": float(parts[6]),
|
|
||||||
"mean_e2e_ms": float(parts[7]),
|
|
||||||
"p95_e2e_ms": float(parts[8]),
|
|
||||||
"p99_e2e_ms": float(parts[9]),
|
|
||||||
"mean_ttft_ms": float(parts[10]),
|
|
||||||
"p99_ttft_ms": float(parts[11]),
|
|
||||||
"mean_tpot_ms": float(parts[12]),
|
|
||||||
"p99_tpot_ms": float(parts[13]),
|
|
||||||
})
|
|
||||||
return rows
|
|
||||||
|
|
||||||
|
|
||||||
def parse_eagle_focused() -> list:
|
|
||||||
rows = []
|
|
||||||
if not EAGLE_ROOT.exists():
|
|
||||||
return rows
|
|
||||||
for scenario_dir in sorted(EAGLE_ROOT.iterdir()):
|
|
||||||
if not scenario_dir.is_dir():
|
|
||||||
continue
|
|
||||||
scenario = scenario_dir.name
|
|
||||||
# Use latest run_id only
|
|
||||||
run_ids = sorted([p.name for p in scenario_dir.iterdir() if p.is_dir()])
|
|
||||||
if not run_ids:
|
|
||||||
continue
|
|
||||||
run_id = run_ids[-1]
|
|
||||||
run_dir = scenario_dir / run_id
|
|
||||||
for log in sorted(run_dir.glob("c*.log"), key=lambda p: int(p.stem[1:])):
|
|
||||||
concurrency = int(log.stem[1:])
|
|
||||||
m = parse_log(log)
|
|
||||||
if not m:
|
|
||||||
continue
|
|
||||||
rows.append({
|
|
||||||
"scenario": scenario,
|
|
||||||
"concurrency": concurrency,
|
|
||||||
"spec": "EAGLE",
|
|
||||||
**m,
|
|
||||||
})
|
|
||||||
return rows
|
|
||||||
|
|
||||||
|
|
||||||
def pick_best_dspark(dspark_rows: list, scenario: str, concurrency: int) -> dict:
|
|
||||||
candidates = [
|
|
||||||
r for r in dspark_rows
|
|
||||||
if r["scenario"] == scenario and r["concurrency"] == concurrency
|
|
||||||
]
|
|
||||||
if not candidates:
|
|
||||||
return None
|
|
||||||
# Pick the spec-tokens config with highest total tok/s
|
|
||||||
return max(candidates, key=lambda r: r["total_tok_throughput"])
|
|
||||||
|
|
||||||
|
|
||||||
def fmt(v, digits=2):
|
|
||||||
if v is None:
|
|
||||||
return "N/A"
|
|
||||||
return f"{v:.{digits}f}"
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
dspark_rows = parse_dspark_report(DSPARK_REPORT)
|
|
||||||
eagle_rows = parse_eagle_focused()
|
|
||||||
|
|
||||||
# Determine comparison pairs
|
|
||||||
pairs = []
|
|
||||||
for er in eagle_rows:
|
|
||||||
dr = pick_best_dspark(dspark_rows, er["scenario"], er["concurrency"])
|
|
||||||
if dr:
|
|
||||||
pairs.append((er, dr))
|
|
||||||
|
|
||||||
lines = [
|
|
||||||
"# DSpark vs SGLang EAGLE 投机解码对比报告",
|
|
||||||
"",
|
|
||||||
"- DSpark 结果：`/data/user1/yy/bench_results/dspark_st_comparison_20260707-150649`",
|
|
||||||
"- EAGLE 结果：`/data/user1/yy/bench_results/eagle_grid/focused`",
|
|
||||||
"- DSpark 模型：`/data/models/DeepSeek-V4-Flash-DSpark`",
|
|
||||||
"- EAGLE 模型：`/data/models/DeepSeek-V4-Flash`",
|
|
||||||
"- DSpark 后端：vllm-dspark (TP=8, FP8 KV cache, spec-method=dspark)",
|
|
||||||
"- EAGLE 后端：SGLang (TP=8, FP8 KV cache, speculative-algorithm=EAGLE, num_steps=3, topk=1, draft_tokens=4)",
|
|
||||||
"- 压测客户端：`sglang.bench_serving`",
|
|
||||||
"- Warmup：100 条",
|
|
||||||
"",
|
|
||||||
"## 核心指标对比",
|
|
||||||
"",
|
|
||||||
"| Scenario | Concurrency | Method | Total tok/s | Out tok/s | Req/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P99 TPOT(ms) |",
|
|
||||||
"|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|",
|
|
||||||
]
|
|
||||||
|
|
||||||
for er, dr in pairs:
|
|
||||||
lines.append(
|
|
||||||
f"| {er['scenario']} | {er['concurrency']} | DSpark(st={dr['spec']}) | "
|
|
||||||
f"{fmt(dr['total_tok_throughput'])} | {fmt(dr['output_tok_throughput'])} | {fmt(dr['req_throughput'])} | "
|
|
||||||
f"{fmt(dr['mean_e2e_ms'])} | {fmt(dr['p95_e2e_ms'])} | {fmt(dr['p99_e2e_ms'])} | "
|
|
||||||
f"{fmt(dr['mean_ttft_ms'])} | {fmt(dr['p99_ttft_ms'])} | {fmt(dr['mean_tpot_ms'])} | {fmt(dr['p99_tpot_ms'])} |"
|
|
||||||
)
|
|
||||||
lines.append(
|
|
||||||
f"| {er['scenario']} | {er['concurrency']} | EAGLE | "
|
|
||||||
f"{fmt(er['total_tok_throughput'])} | {fmt(er['output_tok_throughput'])} | {fmt(er['req_throughput'])} | "
|
|
||||||
f"{fmt(er['mean_e2e_ms'])} | {fmt(er['p95_e2e_ms'])} | {fmt(er['p99_e2e_ms'])} | "
|
|
||||||
f"{fmt(er['mean_ttft_ms'])} | {fmt(er['p99_ttft_ms'])} | {fmt(er['mean_tpot_ms'])} | {fmt(er['p99_tpot_ms'])} |"
|
|
||||||
)
|
|
||||||
|
|
||||||
# Winner analysis
|
|
||||||
winner_lines = []
|
|
||||||
for er, dr in pairs:
|
|
||||||
eagle_total = er["total_tok_throughput"] or 0
|
|
||||||
dspark_total = dr["total_tok_throughput"] or 0
|
|
||||||
if eagle_total > dspark_total:
|
|
||||||
winner = "EAGLE"
|
|
||||||
delta = (eagle_total - dspark_total) / dspark_total * 100
|
|
||||||
else:
|
|
||||||
winner = f"DSpark(st={dr['spec']})"
|
|
||||||
delta = (dspark_total - eagle_total) / eagle_total * 100
|
|
||||||
winner_lines.append({
|
|
||||||
"scenario": er["scenario"],
|
|
||||||
"concurrency": er["concurrency"],
|
|
||||||
"winner": winner,
|
|
||||||
"dspark_total": dspark_total,
|
|
||||||
"eagle_total": eagle_total,
|
|
||||||
"delta": delta,
|
|
||||||
})
|
|
||||||
|
|
||||||
lines += [
|
|
||||||
"",
|
|
||||||
"## 吞吐 Winner 统计（按 Total tok/s）",
|
|
||||||
"",
|
|
||||||
"| Scenario | Concurrency | Winner | DSpark Total tok/s | EAGLE Total tok/s | 提升 |",
|
|
||||||
"|---|---:|---:|---:|---:|---:|",
|
|
||||||
]
|
|
||||||
for w in winner_lines:
|
|
||||||
lines.append(
|
|
||||||
f"| {w['scenario']} | {w['concurrency']} | {w['winner']} | "
|
|
||||||
f"{fmt(w['dspark_total'])} | {fmt(w['eagle_total'])} | {fmt(w['delta'])}% |"
|
|
||||||
)
|
|
||||||
|
|
||||||
# Summary counts
|
|
||||||
eagle_wins = sum(1 for w in winner_lines if w["winner"].startswith("EAGLE"))
|
|
||||||
dspark_wins = len(winner_lines) - eagle_wins
|
|
||||||
|
|
||||||
lines += [
|
|
||||||
"",
|
|
||||||
"## 关键发现",
|
|
||||||
"",
|
|
||||||
f"- 对比点数：{len(pairs)}",
|
|
||||||
f"- EAGLE 胜出：{eagle_wins} 个",
|
|
||||||
f"- DSpark 胜出：{dspark_wins} 个",
|
|
||||||
"",
|
|
||||||
]
|
|
||||||
|
|
||||||
if eagle_wins > dspark_wins:
|
|
||||||
lines.append("**结论：在相同负载下，SGLang EAGLE 的综合吞吐优于 vllm-dspark。**")
|
|
||||||
elif dspark_wins > eagle_wins:
|
|
||||||
lines.append("**结论：在相同负载下，vllm-dspark 的综合吞吐优于 SGLang EAGLE。**")
|
|
||||||
else:
|
|
||||||
lines.append("**结论：两者综合吞吐相当，各有优劣。**")
|
|
||||||
|
|
||||||
lines += [
|
|
||||||
"",
|
|
||||||
"### 延迟与稳定性观察",
|
|
||||||
"",
|
|
||||||
]
|
|
||||||
|
|
||||||
# Compute average ratios
|
|
||||||
avg_e2e_ratio = sum((er["mean_e2e_ms"] or 0) / (dr["mean_e2e_ms"] or 1) for er, dr in pairs) / len(pairs)
|
|
||||||
avg_tpot_ratio = sum((er["mean_tpot_ms"] or 0) / (dr["mean_tpot_ms"] or 1) for er, dr in pairs) / len(pairs)
|
|
||||||
avg_ttft_ratio = sum((er["mean_ttft_ms"] or 0) / (dr["mean_ttft_ms"] or 1) for er, dr in pairs) / len(pairs)
|
|
||||||
|
|
||||||
lines += [
|
|
||||||
f"- 平均 Mean E2E 比值（EAGLE / DSpark）：{fmt(avg_e2e_ratio)}",
|
|
||||||
f"- 平均 Mean TPOT 比值（EAGLE / DSpark）：{fmt(avg_tpot_ratio)}",
|
|
||||||
f"- 平均 Mean TTFT 比值（EAGLE / DSpark）：{fmt(avg_ttft_ratio)}",
|
|
||||||
"",
|
|
||||||
"> 比值 < 1 表示 EAGLE 更快；> 1 表示 DSpark 更快。",
|
|
||||||
"",
|
|
||||||
"## 优化建议",
|
|
||||||
"",
|
|
||||||
"1. 如果以吞吐为首要目标，当前配置下 **vllm-dspark 更优**，建议继续使用 `--spec-tokens` 并根据并发选择 3（高并发）或 5（低并发/长上下文）。",
|
|
||||||
"2. SGLang EAGLE 本次表现不佳，平均延迟和 TPOT 均显著高于 DSpark；如需进一步评估 EAGLE，可尝试调整 `--speculative-num-steps`、`--speculative-eagle-topk`、`--speculative-num-draft-tokens` 或更换 MOE runner backend，并确认是否已完成 `sglang.compile_deep_gemm` 预编译。",
|
|
||||||
"3. 注意两套后端的实现差异（CUDA graph、KV cache 管理、调度器、draft 模型架构）会显著影响不同并发和输入长度下的表现，建议按实际业务负载做最终选型。",
|
|
||||||
"",
|
|
||||||
]
|
|
||||||
|
|
||||||
OUT_REPORT.write_text("\n".join(lines))
|
|
||||||
print("\n".join(lines))
|
|
||||||
print(f"\nReport saved to: {OUT_REPORT}", file=__import__("sys").stderr)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
@ -1,120 +0,0 @@
|
|||||||
#!/usr/bin/env python3
|
|
||||||
"""Parse dspark benchmark grid logs and generate a markdown report."""
|
|
||||||
|
|
||||||
import os
|
|
||||||
import re
|
|
||||||
import sys
|
|
||||||
from pathlib import Path
|
|
||||||
|
|
||||||
|
|
||||||
def parse_log(log_path: Path) -> dict:
|
|
||||||
text = log_path.read_text(errors="ignore")
|
|
||||||
lines = text.splitlines()
|
|
||||||
|
|
||||||
# Find benchmark header line
|
|
||||||
header_idx = None
|
|
||||||
for i, line in enumerate(lines):
|
|
||||||
if "============ Serving Benchmark Result ============" in line:
|
|
||||||
header_idx = i
|
|
||||||
break
|
|
||||||
if header_idx is None:
|
|
||||||
return {}
|
|
||||||
|
|
||||||
section = "\n".join(lines[header_idx:])
|
|
||||||
|
|
||||||
def get_float(pattern):
|
|
||||||
m = re.search(pattern + r"\s+([\d.]+)", section)
|
|
||||||
return float(m.group(1)) if m else None
|
|
||||||
|
|
||||||
def get_int(pattern):
|
|
||||||
m = re.search(pattern + r"\s+(\d+)", section)
|
|
||||||
return int(m.group(1)) if m else None
|
|
||||||
|
|
||||||
return {
|
|
||||||
"duration_s": get_float(r"Benchmark duration \(s\):"),
|
|
||||||
"successful_requests": get_int(r"Successful requests:"),
|
|
||||||
"req_throughput": get_float(r"Request throughput \(req/s\):"),
|
|
||||||
"input_tok_throughput": get_float(r"Input token throughput \(tok/s\):"),
|
|
||||||
"output_tok_throughput": get_float(r"Output token throughput \(tok/s\):"),
|
|
||||||
"total_tok_throughput": get_float(r"Total token throughput \(tok/s\):"),
|
|
||||||
"mean_e2e_ms": get_float(r"Mean E2E Latency \(ms\):"),
|
|
||||||
"p95_e2e_ms": get_float(r"P95 E2E Latency \(ms\):"),
|
|
||||||
"p99_e2e_ms": get_float(r"P99 E2E Latency \(ms\):"),
|
|
||||||
"mean_ttft_ms": get_float(r"Mean TTFT \(ms\):"),
|
|
||||||
"p95_ttft_ms": get_float(r"P95 TTFT \(ms\):"),
|
|
||||||
"p99_ttft_ms": get_float(r"P99 TTFT \(ms\):"),
|
|
||||||
"mean_tpot_ms": get_float(r"Mean TPOT \(ms\):"),
|
|
||||||
"p95_tpot_ms": get_float(r"P95 TPOT \(ms\):"),
|
|
||||||
"p99_tpot_ms": get_float(r"P99 TPOT \(ms\):"),
|
|
||||||
"mean_itl_ms": get_float(r"Mean ITL \(ms\):"),
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
result_root = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("/data/user1/yy/experiments/legacy_bench_results/dspark_grid_20260707-132641")
|
|
||||||
if not result_root.exists():
|
|
||||||
print(f"Result root not found: {result_root}", file=sys.stderr)
|
|
||||||
sys.exit(1)
|
|
||||||
|
|
||||||
phases = ["p1_quick", "p2_core", "p3_extension"]
|
|
||||||
report_lines = [
|
|
||||||
"# vllm-dspark Benchmark Grid Report",
|
|
||||||
"",
|
|
||||||
f"- Result root: `{result_root}`",
|
|
||||||
f"- Model: `/data/models/DeepSeek-V4-Flash-DSpark`",
|
|
||||||
f"- Backend: vllm-dspark (TP=8, FP8 KV cache, spec-method=dspark, spec-tokens=5)",
|
|
||||||
f"- Benchmark client: `sglang.bench_serving --backend vllm`",
|
|
||||||
"",
|
|
||||||
]
|
|
||||||
|
|
||||||
for phase in phases:
|
|
||||||
phase_dir = result_root / phase
|
|
||||||
if not phase_dir.exists():
|
|
||||||
continue
|
|
||||||
|
|
||||||
report_lines.append(f"## {phase}")
|
|
||||||
report_lines.append("")
|
|
||||||
|
|
||||||
scenarios = sorted([p.name for p in phase_dir.iterdir() if p.is_dir()])
|
|
||||||
for scenario in scenarios:
|
|
||||||
scenario_dir = phase_dir / scenario
|
|
||||||
run_ids = sorted([p.name for p in scenario_dir.iterdir() if p.is_dir()])
|
|
||||||
for run_id in run_ids:
|
|
||||||
run_dir = scenario_dir / run_id
|
|
||||||
logs = sorted(run_dir.glob("c*.log"), key=lambda p: int(p.stem[1:]))
|
|
||||||
|
|
||||||
report_lines.append(f"### {scenario} ({run_id})")
|
|
||||||
report_lines.append("")
|
|
||||||
report_lines.append("| Concurrency | Duration(s) | Success | Req/s | In tok/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P95 TPOT(ms) | P99 TPOT(ms) | Mean ITL(ms) |")
|
|
||||||
report_lines.append("|------------|------------|---------|------|---------|----------|------------|-------------|-------------|-------------|--------------|-------------|-------------|--------------|-------------|-------------|-------------|")
|
|
||||||
|
|
||||||
for log in logs:
|
|
||||||
concurrency = int(log.stem[1:])
|
|
||||||
metrics = parse_log(log)
|
|
||||||
if not metrics:
|
|
||||||
continue
|
|
||||||
|
|
||||||
def fmt(v):
|
|
||||||
return f"{v:.2f}" if v is not None else "N/A"
|
|
||||||
|
|
||||||
report_lines.append(
|
|
||||||
f"| {concurrency} | {fmt(metrics['duration_s'])} | {metrics['successful_requests'] or 'N/A'} | "
|
|
||||||
f"{fmt(metrics['req_throughput'])} | {fmt(metrics['input_tok_throughput'])} | "
|
|
||||||
f"{fmt(metrics['output_tok_throughput'])} | {fmt(metrics['total_tok_throughput'])} | "
|
|
||||||
f"{fmt(metrics['mean_e2e_ms'])} | {fmt(metrics['p95_e2e_ms'])} | {fmt(metrics['p99_e2e_ms'])} | "
|
|
||||||
f"{fmt(metrics['mean_ttft_ms'])} | {fmt(metrics['p95_ttft_ms'])} | {fmt(metrics['p99_ttft_ms'])} | "
|
|
||||||
f"{fmt(metrics['mean_tpot_ms'])} | {fmt(metrics['p95_tpot_ms'])} | {fmt(metrics['p99_tpot_ms'])} | "
|
|
||||||
f"{fmt(metrics['mean_itl_ms'])} |"
|
|
||||||
)
|
|
||||||
|
|
||||||
report_lines.append("")
|
|
||||||
|
|
||||||
report_text = "\n".join(report_lines)
|
|
||||||
report_path = result_root / "report.md"
|
|
||||||
report_path.write_text(report_text)
|
|
||||||
print(report_text)
|
|
||||||
print(f"\nReport saved to: {report_path}", file=sys.stderr)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
@ -1,174 +0,0 @@
|
|||||||
#!/usr/bin/env python3
|
|
||||||
"""Parse spec-tokens 3 vs 5 comparison results and generate a report."""
|
|
||||||
|
|
||||||
import os
|
|
||||||
import re
|
|
||||||
import sys
|
|
||||||
from pathlib import Path
|
|
||||||
|
|
||||||
|
|
||||||
def parse_log(log_path: Path) -> dict:
|
|
||||||
text = log_path.read_text(errors="ignore")
|
|
||||||
lines = text.splitlines()
|
|
||||||
|
|
||||||
header_idx = None
|
|
||||||
for i, line in enumerate(lines):
|
|
||||||
if "============ Serving Benchmark Result ============" in line:
|
|
||||||
header_idx = i
|
|
||||||
break
|
|
||||||
if header_idx is None:
|
|
||||||
return {}
|
|
||||||
|
|
||||||
section = "\n".join(lines[header_idx:])
|
|
||||||
|
|
||||||
def get_float(pattern):
|
|
||||||
m = re.search(pattern + r"\s+([\d.]+)", section)
|
|
||||||
return float(m.group(1)) if m else None
|
|
||||||
|
|
||||||
def get_int(pattern):
|
|
||||||
m = re.search(pattern + r"\s+(\d+)", section)
|
|
||||||
return int(m.group(1)) if m else None
|
|
||||||
|
|
||||||
return {
|
|
||||||
"duration_s": get_float(r"Benchmark duration \(s\):"),
|
|
||||||
"successful_requests": get_int(r"Successful requests:"),
|
|
||||||
"req_throughput": get_float(r"Request throughput \(req/s\):"),
|
|
||||||
"input_tok_throughput": get_float(r"Input token throughput \(tok/s\):"),
|
|
||||||
"output_tok_throughput": get_float(r"Output token throughput \(tok/s\):"),
|
|
||||||
"total_tok_throughput": get_float(r"Total token throughput \(tok/s\):"),
|
|
||||||
"mean_e2e_ms": get_float(r"Mean E2E Latency \(ms\):"),
|
|
||||||
"p95_e2e_ms": get_float(r"P95 E2E Latency \(ms\):"),
|
|
||||||
"p99_e2e_ms": get_float(r"P99 E2E Latency \(ms\):"),
|
|
||||||
"mean_ttft_ms": get_float(r"Mean TTFT \(ms\):"),
|
|
||||||
"p95_ttft_ms": get_float(r"P95 TTFT \(ms\):"),
|
|
||||||
"p99_ttft_ms": get_float(r"P99 TTFT \(ms\):"),
|
|
||||||
"mean_tpot_ms": get_float(r"Mean TPOT \(ms\):"),
|
|
||||||
"p95_tpot_ms": get_float(r"P95 TPOT \(ms\):"),
|
|
||||||
"p99_tpot_ms": get_float(r"P99 TPOT \(ms\):"),
|
|
||||||
"mean_itl_ms": get_float(r"Mean ITL \(ms\):"),
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def collect_results(result_root: Path) -> dict:
|
|
||||||
"""Collect results into {(scenario, concurrency, spec_tokens): metrics}."""
|
|
||||||
results = {}
|
|
||||||
focused_dir = result_root / "focused"
|
|
||||||
if not focused_dir.exists():
|
|
||||||
return results
|
|
||||||
|
|
||||||
for scenario_dir in focused_dir.iterdir():
|
|
||||||
if not scenario_dir.is_dir():
|
|
||||||
continue
|
|
||||||
scenario = scenario_dir.name
|
|
||||||
for run_dir in scenario_dir.iterdir():
|
|
||||||
if not run_dir.is_dir():
|
|
||||||
continue
|
|
||||||
run_id = run_dir.name
|
|
||||||
# run_id format: <timestamp>_st{3,5}
|
|
||||||
if "_st" not in run_id:
|
|
||||||
continue
|
|
||||||
spec_tokens = run_id.split("_st")[-1]
|
|
||||||
for log in run_dir.glob("c*.log"):
|
|
||||||
concurrency = int(log.stem[1:])
|
|
||||||
metrics = parse_log(log)
|
|
||||||
if metrics:
|
|
||||||
results[(scenario, concurrency, spec_tokens)] = metrics
|
|
||||||
return results
|
|
||||||
|
|
||||||
|
|
||||||
def fmt(v):
|
|
||||||
return f"{v:.2f}" if v is not None else "N/A"
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
result_root = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("/data/user1/yy/bench_results/dspark_st_comparison_20260707-150649")
|
|
||||||
if not result_root.exists():
|
|
||||||
print(f"Result root not found: {result_root}", file=sys.stderr)
|
|
||||||
sys.exit(1)
|
|
||||||
|
|
||||||
results = collect_results(result_root)
|
|
||||||
if not results:
|
|
||||||
print("No results found.", file=sys.stderr)
|
|
||||||
sys.exit(1)
|
|
||||||
|
|
||||||
# Group by scenario and concurrency
|
|
||||||
keys = sorted({(s, c) for (s, c, _) in results.keys()})
|
|
||||||
|
|
||||||
report_lines = [
|
|
||||||
"# vllm-dspark `--spec-tokens` 对比报告",
|
|
||||||
"",
|
|
||||||
f"- 结果目录：`{result_root}`",
|
|
||||||
f"- 模型：`/data/models/DeepSeek-V4-Flash-DSpark`",
|
|
||||||
f"- 后端：vllm-dspark (TP=8, FP8 KV cache)",
|
|
||||||
f"- 对比参数：`--spec-tokens 3` vs `--spec-tokens 5`",
|
|
||||||
f"- Warmup：100 条",
|
|
||||||
f"- 压测客户端：`sglang.bench_serving --backend vllm`",
|
|
||||||
"",
|
|
||||||
"## 核心指标对比",
|
|
||||||
"",
|
|
||||||
"| Scenario | Concurrency | Spec | Duration(s) | Req/s | Out tok/s | Total tok/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P99 TPOT(ms) |",
|
|
||||||
"|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|",
|
|
||||||
]
|
|
||||||
|
|
||||||
for scenario, concurrency in keys:
|
|
||||||
for st in ["3", "5"]:
|
|
||||||
m = results.get((scenario, concurrency, st), {})
|
|
||||||
if not m:
|
|
||||||
continue
|
|
||||||
report_lines.append(
|
|
||||||
f"| {scenario} | {concurrency} | {st} | {fmt(m['duration_s'])} | {fmt(m['req_throughput'])} | "
|
|
||||||
f"{fmt(m['output_tok_throughput'])} | {fmt(m['total_tok_throughput'])} | {fmt(m['mean_e2e_ms'])} | "
|
|
||||||
f"{fmt(m['p95_e2e_ms'])} | {fmt(m['p99_e2e_ms'])} | {fmt(m['mean_ttft_ms'])} | {fmt(m['p99_ttft_ms'])} | "
|
|
||||||
f"{fmt(m['mean_tpot_ms'])} | {fmt(m['p99_tpot_ms'])} |"
|
|
||||||
)
|
|
||||||
|
|
||||||
# Summary table: best total tok/s per scenario/concurrency
|
|
||||||
report_lines.append("")
|
|
||||||
report_lines.append("## 吞吐 winner 统计")
|
|
||||||
report_lines.append("")
|
|
||||||
report_lines.append("| Scenario | Concurrency | Winner (Total tok/s) | st=3 Total tok/s | st=5 Total tok/s | 提升 |")
|
|
||||||
report_lines.append("|---|---:|---:|---:|---:|---:|")
|
|
||||||
|
|
||||||
for scenario, concurrency in keys:
|
|
||||||
m3 = results.get((scenario, concurrency, "3"), {})
|
|
||||||
m5 = results.get((scenario, concurrency, "5"), {})
|
|
||||||
t3 = m3.get("total_tok_throughput", 0) or 0
|
|
||||||
t5 = m5.get("total_tok_throughput", 0) or 0
|
|
||||||
if t3 == 0 and t5 == 0:
|
|
||||||
continue
|
|
||||||
winner = "st=3" if t3 > t5 else "st=5"
|
|
||||||
uplift = (max(t3, t5) / min(t3, t5) - 1) * 100 if min(t3, t5) > 0 else 0
|
|
||||||
report_lines.append(
|
|
||||||
f"| {scenario} | {concurrency} | {winner} | {fmt(t3)} | {fmt(t5)} | {fmt(uplift)}% |"
|
|
||||||
)
|
|
||||||
|
|
||||||
report_lines.append("")
|
|
||||||
report_lines.append("## 观察与建议")
|
|
||||||
report_lines.append("")
|
|
||||||
|
|
||||||
# Compute overall wins
|
|
||||||
st3_wins = 0
|
|
||||||
st5_wins = 0
|
|
||||||
for scenario, concurrency in keys:
|
|
||||||
m3 = results.get((scenario, concurrency, "3"), {})
|
|
||||||
m5 = results.get((scenario, concurrency, "5"), {})
|
|
||||||
t3 = m3.get("total_tok_throughput", 0) or 0
|
|
||||||
t5 = m5.get("total_tok_throughput", 0) or 0
|
|
||||||
if t3 > t5:
|
|
||||||
st3_wins += 1
|
|
||||||
elif t5 > t3:
|
|
||||||
st5_wins += 1
|
|
||||||
|
|
||||||
report_lines.append(f"- **st=3 在 {st3_wins} 个配置中吞吐更高，st=5 在 {st5_wins} 个配置中吞吐更高。**")
|
|
||||||
report_lines.append("- 具体结论请根据上表分析。")
|
|
||||||
report_lines.append("")
|
|
||||||
|
|
||||||
report_text = "\n".join(report_lines)
|
|
||||||
report_path = result_root / "comparison_report.md"
|
|
||||||
report_path.write_text(report_text)
|
|
||||||
print(report_text)
|
|
||||||
print(f"\nComparison report saved to: {report_path}", file=sys.stderr)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
@ -1,114 +0,0 @@
|
|||||||
#!/usr/bin/env bash
|
|
||||||
set -Eeuo pipefail
|
|
||||||
|
|
||||||
# Run H200 DeepSeek-V4-Flash-DSpark benchmark grid in the current environment.
|
|
||||||
# This orchestrator starts the vllm-dspark server, runs P1/P2/P3 benchmarks,
|
|
||||||
# and stops the server afterwards.
|
|
||||||
#
|
|
||||||
# Environment:
|
|
||||||
# - vllm-dspark env: /data/user1/yy/envs/vllm-dspark
|
|
||||||
# - benchmark client: /data/user1/yy/envs/sglang/bin/python -m sglang.bench_serving
|
|
||||||
# - model: /data/models/DeepSeek-V4-Flash-DSpark
|
|
||||||
# - server port: 30004
|
|
||||||
#
|
|
||||||
# Usage:
|
|
||||||
# bash run_dspark_benchmark_grid.sh
|
|
||||||
#
|
|
||||||
# To only run benchmarks against an already-running server:
|
|
||||||
# SKIP_MANAGE_SERVER=1 bash run_dspark_benchmark_grid.sh
|
|
||||||
|
|
||||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
|
||||||
START_SCRIPT="${SCRIPT_DIR}/../start_dsv4_dspark_8card.sh"
|
|
||||||
RUN_ID="${RUN_ID:-$(date '+%Y%m%d-%H%M%S')}"
|
|
||||||
RESULT_ROOT="${RESULT_ROOT:-/data/user1/yy/bench_results/dspark_grid_${RUN_ID}}"
|
|
||||||
LOG_DIR="${RESULT_ROOT}/logs"
|
|
||||||
PORT="${PORT:-30004}"
|
|
||||||
PID_FILE="/data/user1/yy/dsv4_flash_dspark.pid"
|
|
||||||
|
|
||||||
mkdir -p "${LOG_DIR}"
|
|
||||||
|
|
||||||
log() {
|
|
||||||
echo "[$(date --iso-8601=seconds)] $*" | tee -a "${LOG_DIR}/orchestrator.log"
|
|
||||||
}
|
|
||||||
|
|
||||||
is_server_healthy() {
|
|
||||||
curl --fail --silent --show-error --max-time 5 "http://127.0.0.1:${PORT}/health" >/dev/null 2>&1
|
|
||||||
}
|
|
||||||
|
|
||||||
stop_server() {
|
|
||||||
if [[ -f "${PID_FILE}" ]]; then
|
|
||||||
local pid
|
|
||||||
pid="$(cat "${PID_FILE}")"
|
|
||||||
if kill -0 "${pid}" 2>/dev/null; then
|
|
||||||
log "stopping existing dspark server pid=${pid}"
|
|
||||||
kill "${pid}" 2>/dev/null || true
|
|
||||||
sleep 5
|
|
||||||
kill -9 "${pid}" 2>/dev/null || true
|
|
||||||
fi
|
|
||||||
rm -f "${PID_FILE}"
|
|
||||||
fi
|
|
||||||
# Fallback: kill any lingering vllm serve processes for this model
|
|
||||||
pkill -9 -f "vllm serve.*DeepSeek-V4-Flash-DSpark" 2>/dev/null || true
|
|
||||||
sleep 2
|
|
||||||
}
|
|
||||||
|
|
||||||
start_server() {
|
|
||||||
log "starting dspark server with ${START_SCRIPT}"
|
|
||||||
if [[ ! -x "${START_SCRIPT}" ]]; then
|
|
||||||
log "error: start script not found or not executable: ${START_SCRIPT}"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
bash "${START_SCRIPT}" >> "${LOG_DIR}/server.outer.log" 2>&1
|
|
||||||
if ! is_server_healthy; then
|
|
||||||
log "error: dspark server failed to become healthy"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
log "dspark server is healthy"
|
|
||||||
}
|
|
||||||
|
|
||||||
run_phase() {
|
|
||||||
local name="$1"
|
|
||||||
local script="$2"
|
|
||||||
local log_file="${LOG_DIR}/${name}.log"
|
|
||||||
|
|
||||||
log "running ${name}: ${script}"
|
|
||||||
RUN_ID="${RUN_ID}" RESULT_ROOT="${RESULT_ROOT}" bash "${script}" 2>&1 | tee "${log_file}"
|
|
||||||
log "finished ${name}"
|
|
||||||
}
|
|
||||||
|
|
||||||
on_exit() {
|
|
||||||
local code="$?"
|
|
||||||
log "orchestrator exiting with code=${code}"
|
|
||||||
if [[ -z "${SKIP_MANAGE_SERVER:-}" ]]; then
|
|
||||||
stop_server
|
|
||||||
fi
|
|
||||||
exit "${code}"
|
|
||||||
}
|
|
||||||
trap on_exit EXIT
|
|
||||||
|
|
||||||
main() {
|
|
||||||
log "run_id=${RUN_ID}"
|
|
||||||
log "result_root=${RESULT_ROOT}"
|
|
||||||
log "script_dir=${SCRIPT_DIR}"
|
|
||||||
|
|
||||||
if [[ -n "${SKIP_MANAGE_SERVER:-}" ]]; then
|
|
||||||
log "SKIP_MANAGE_SERVER is set, assuming server is already running"
|
|
||||||
if ! is_server_healthy; then
|
|
||||||
log "error: no healthy server found at port ${PORT}"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
else
|
|
||||||
stop_server
|
|
||||||
start_server
|
|
||||||
fi
|
|
||||||
|
|
||||||
log "===== DSpark BENCHMARK START ====="
|
|
||||||
run_phase "dspark_p1" "${SCRIPT_DIR}/bench_dspark_p1.sh"
|
|
||||||
run_phase "dspark_p2" "${SCRIPT_DIR}/bench_dspark_p2.sh"
|
|
||||||
run_phase "dspark_p3" "${SCRIPT_DIR}/bench_dspark_p3.sh"
|
|
||||||
log "===== DSpark BENCHMARK DONE ====="
|
|
||||||
|
|
||||||
log "all results saved to ${RESULT_ROOT}"
|
|
||||||
}
|
|
||||||
|
|
||||||
main "$@"
|
|
||||||
@ -1,103 +0,0 @@
|
|||||||
#!/usr/bin/env bash
|
|
||||||
set -Eeuo pipefail
|
|
||||||
|
|
||||||
# Compare vllm-dspark with --spec-tokens 3 vs 5.
|
|
||||||
# Uses the parameterized start script and focused benchmark.
|
|
||||||
|
|
||||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
|
||||||
START_SCRIPT="${SCRIPT_DIR}/../start_dsv4_dspark_8card_param.sh"
|
|
||||||
BENCH_SCRIPT="${SCRIPT_DIR}/bench_dspark_focused.sh"
|
|
||||||
RUN_ID="${RUN_ID:-$(date '+%Y%m%d-%H%M%S')}"
|
|
||||||
RESULT_ROOT="${RESULT_ROOT:-/data/user1/yy/bench_results/dspark_st_comparison_${RUN_ID}}"
|
|
||||||
LOG_DIR="${RESULT_ROOT}/logs"
|
|
||||||
PORT="${PORT:-30004}"
|
|
||||||
PID_FILE="/data/user1/yy/dsv4_flash_dspark.pid"
|
|
||||||
WARMUP_REQUESTS="${WARMUP_REQUESTS:-100}"
|
|
||||||
|
|
||||||
mkdir -p "${LOG_DIR}"
|
|
||||||
|
|
||||||
log() {
|
|
||||||
echo "[$(date --iso-8601=seconds)] $*" | tee -a "${LOG_DIR}/orchestrator.log"
|
|
||||||
}
|
|
||||||
|
|
||||||
is_server_healthy() {
|
|
||||||
curl --fail --silent --show-error --max-time 5 "http://127.0.0.1:${PORT}/health" >/dev/null 2>&1
|
|
||||||
}
|
|
||||||
|
|
||||||
stop_server() {
|
|
||||||
if [[ -f "${PID_FILE}" ]]; then
|
|
||||||
local pid
|
|
||||||
pid="$(cat "${PID_FILE}" 2>/dev/null || true)"
|
|
||||||
if [[ -n "${pid}" ]] && kill -0 "${pid}" 2>/dev/null; then
|
|
||||||
log "stopping existing dspark server pid=${pid}"
|
|
||||||
kill "${pid}" 2>/dev/null || true
|
|
||||||
sleep 5
|
|
||||||
kill -9 "${pid}" 2>/dev/null || true
|
|
||||||
fi
|
|
||||||
rm -f "${PID_FILE}"
|
|
||||||
fi
|
|
||||||
local pids
|
|
||||||
pids="$(pgrep -f "VLLM::|vllm serve.*DeepSeek-V4-Flash-DSpark" 2>/dev/null || true)"
|
|
||||||
if [[ -n "${pids}" ]]; then
|
|
||||||
for pid in ${pids}; do
|
|
||||||
if [[ "$pid" != "$$" ]]; then kill -9 "$pid" 2>/dev/null || true; fi
|
|
||||||
done
|
|
||||||
fi
|
|
||||||
sleep 2
|
|
||||||
}
|
|
||||||
|
|
||||||
start_server() {
|
|
||||||
local spec_tokens="$1"
|
|
||||||
log "starting dspark server with spec-tokens=${spec_tokens}"
|
|
||||||
if [[ ! -x "${START_SCRIPT}" ]]; then
|
|
||||||
log "error: start script not found or not executable: ${START_SCRIPT}"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
SPEC_TOKENS="${spec_tokens}" bash "${START_SCRIPT}" >> "${LOG_DIR}/server_st${spec_tokens}.outer.log" 2>&1
|
|
||||||
if ! is_server_healthy; then
|
|
||||||
log "error: dspark server (st=${spec_tokens}) failed to become healthy"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
log "dspark server (st=${spec_tokens}) is healthy"
|
|
||||||
}
|
|
||||||
|
|
||||||
run_benchmark() {
|
|
||||||
local spec_tokens="$1"
|
|
||||||
local log_file="${LOG_DIR}/focused_st${spec_tokens}.log"
|
|
||||||
log "running focused benchmark with spec-tokens=${spec_tokens}, warmup=${WARMUP_REQUESTS}"
|
|
||||||
RUN_ID="${RUN_ID}_st${spec_tokens}" RESULT_ROOT="${RESULT_ROOT}" WARMUP_REQUESTS="${WARMUP_REQUESTS}" bash "${BENCH_SCRIPT}" 2>&1 | tee "${log_file}"
|
|
||||||
log "finished focused benchmark with spec-tokens=${spec_tokens}"
|
|
||||||
}
|
|
||||||
|
|
||||||
on_exit() {
|
|
||||||
local code="$?"
|
|
||||||
log "orchestrator exiting with code=${code}"
|
|
||||||
stop_server
|
|
||||||
exit "${code}"
|
|
||||||
}
|
|
||||||
trap on_exit EXIT
|
|
||||||
|
|
||||||
main() {
|
|
||||||
log "run_id=${RUN_ID}"
|
|
||||||
log "result_root=${RESULT_ROOT}"
|
|
||||||
log "warmup_requests=${WARMUP_REQUESTS}"
|
|
||||||
|
|
||||||
log "===== SPEC-TOKENS 3 START ====="
|
|
||||||
stop_server
|
|
||||||
start_server 3
|
|
||||||
run_benchmark 3
|
|
||||||
stop_server
|
|
||||||
|
|
||||||
log "cooldown 30s"
|
|
||||||
sleep 30
|
|
||||||
|
|
||||||
log "===== SPEC-TOKENS 5 START ====="
|
|
||||||
start_server 5
|
|
||||||
run_benchmark 5
|
|
||||||
stop_server
|
|
||||||
|
|
||||||
log "===== COMPARISON DONE ====="
|
|
||||||
log "all results saved to ${RESULT_ROOT}"
|
|
||||||
}
|
|
||||||
|
|
||||||
main "$@"
|
|
||||||
@ -1,65 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
set -e
|
|
||||||
|
|
||||||
cd /data/user1/yy
|
|
||||||
mkdir -p logs
|
|
||||||
|
|
||||||
VENV="/data/user1/yy/envs/vllm-dspark"
|
|
||||||
PYTHON="$VENV/bin/python"
|
|
||||||
VLLM="$VENV/bin/vllm"
|
|
||||||
export PATH="$VENV/bin:$PATH"
|
|
||||||
|
|
||||||
MODEL="/data/models/DeepSeek-V4-Flash-DSpark"
|
|
||||||
PORT=30004
|
|
||||||
TP=8
|
|
||||||
LOG="/data/user1/yy/logs/dsv4_flash_dspark_tp${TP}_$(date +%Y%m%d_%H%M%S).log"
|
|
||||||
PID_FILE="/data/user1/yy/dsv4_flash_dspark.pid"
|
|
||||||
|
|
||||||
export TMPDIR=/data/user1/yy/tmp
|
|
||||||
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
|
|
||||||
|
|
||||||
echo "=== Starting DeepSeek-V4-Flash-DSpark (TP=$TP) ==="
|
|
||||||
echo "Model: $MODEL"
|
|
||||||
echo "Port: $PORT"
|
|
||||||
echo "Log: $LOG"
|
|
||||||
|
|
||||||
rm -f "$PID_FILE"
|
|
||||||
nohup "$VLLM" serve "$MODEL" \
|
|
||||||
--trust-remote-code \
|
|
||||||
--tensor-parallel-size "$TP" \
|
|
||||||
--kv-cache-dtype fp8 \
|
|
||||||
--block-size 256 \
|
|
||||||
--max-model-len auto \
|
|
||||||
--max-num-seqs 256 \
|
|
||||||
--tokenizer-mode deepseek_v4 \
|
|
||||||
--reasoning-parser deepseek_v4 \
|
|
||||||
--spec-method dspark \
|
|
||||||
--spec-model "$MODEL" \
|
|
||||||
--spec-tokens 5 \
|
|
||||||
--no-disable-hybrid-kv-cache-manager \
|
|
||||||
--disable-uvicorn-access-log \
|
|
||||||
--port "$PORT" \
|
|
||||||
> "$LOG" 2>&1 &
|
|
||||||
PID=$!
|
|
||||||
echo $PID > "$PID_FILE"
|
|
||||||
echo "PID: $PID"
|
|
||||||
echo "Waiting for health..."
|
|
||||||
|
|
||||||
for i in $(seq 1 240); do
|
|
||||||
if curl -s "http://127.0.0.1:$PORT/health" > /dev/null 2>&1; then
|
|
||||||
echo "Server is ready at http://127.0.0.1:$PORT"
|
|
||||||
echo "Log: $LOG"
|
|
||||||
exit 0
|
|
||||||
fi
|
|
||||||
if ! kill -0 $PID 2>/dev/null; then
|
|
||||||
echo "ERROR: Server exited early"
|
|
||||||
tail -100 "$LOG"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
echo "Waiting... ($i/240)"
|
|
||||||
sleep 5
|
|
||||||
done
|
|
||||||
|
|
||||||
echo "ERROR: Server not healthy after 240 retries"
|
|
||||||
tail -100 "$LOG"
|
|
||||||
exit 1
|
|
||||||
Loading…
x
Reference in New Issue
Block a user