lmcache--lmcache
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158 行
5.3 KiB
Markdown
158 行
5.3 KiB
Markdown
# OpenAI Chat‑Completion TTFT Benchmark
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Measure **time‑to‑first‑token (TTFT)** — and optional cache‑hit latency — from **any
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server that speaks the OpenAI `/v1` API** (vLLM, llama.cpp with `--api`,
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OpenAI‑proxy, etc.).
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> **Why run it?**
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> • Compare *cold* latency vs. *cache‑hit* latency.
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> • Verify whether a KV‑cache (VRAM, SSD, LMCache, …) actually helps.
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> • Collect JSONL you can plot
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---
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## 1 · Prerequisites
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| Requirement | Notes |
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|-------------|-------|
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| **Running endpoint** | Must expose the OpenAI REST interface (default URL `http://localhost:8000/v1`). |
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For more information on how to serve an endpoint using vllm and LMCache,
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---
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## 2 · Command‑line flags
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| Flag / shorthand | Default | Meaning |
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|------------------|---------|---------|
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| `--api_base` | `http://localhost:8000/v1` | URL of the OpenAI‑style endpoint. |
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| `--api_key` | `EMPTY` | Any string (ignored by most local servers). |
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| `--model` | *first model from* `/models` | Explicit model ID. |
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| `-C`, `--context_file` | *see table below* | Document inserted before the prompt. |
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| `--max_ctx_tokens` | **131 072** | Upper bound *after* truncation. |
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| `--prompt` | `"Summarize this text"` | Prompt appended after the document. |
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| `--num_following`| **1** | Extra TTFT‑measured requests after the baseline. |
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| `-F`, `--flush_cache` | off | Flush GPU KV‑cache **once** after run 1. |
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| `--out` | `benchmark.jsonl` | JSONL log (cleared at start). |
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### Behaviour of `--context_file`
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| Invocation | Document used |
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|------------|---------------|
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| *(flag omitted)* | Synthetic ASCII filler based on max ctx length input|
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| `--context_file` *(no path)* | Bundled `ffmpeg.txt` (one dir up) |
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| `--context_file /path/doc.txt` | Exact file you specify |
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> **Legacy shorthand** – you may also run
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> `python openai_chat_completion_client.py <PORT>`
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> and every other option remains default.
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---
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## 3 · Quick start
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Cold + warm measurement (two requests total):
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```bash
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python openai_chat_completion_client.py --num_following 1
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```
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Example console output
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```
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=== Run 1: baseline TTFT ===
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TTFT_1 = 0.429s
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(no KV‑cache flush requested)
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=== Run 2: TTFT continued ===
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TTFT_2 = 0.081s
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```
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`benchmark.jsonl`
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```json
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{"run_index":1,"context_tokens":120938,"ttft_seconds":0.429}
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{"run_index":2,"context_tokens":120938,"ttft_seconds":0.081}
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```
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---
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## 4 · Advanced use
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### 4.1 · Benchmark after cache eviction
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```bash
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python openai_chat_completion_client.py \
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-C war_and_peace.txt \
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--num_following 3 \
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--flush_cache \
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--prompt "Give me a concise outline." \
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--out warpeace_flush.jsonl
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```
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* Run 1 – cold
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* Cache flushed
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* Run 2 – cold again (miss)
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* Runs 3‑4 – warm (hits)
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### 4.2 · Stress maximum context
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```bash
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python openai_chat_completion_client.py \
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--max_ctx_tokens 131072 \
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--num_following 1 -F
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```
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Generates a k‑char filler, truncates to fit
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`≤ max_ctx ` tokens (keeps a **2 048‑token safety margin**), then
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measures cold vs. warm TTFT.
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---
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## 5 · Output schema
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Each JSONL line contains:
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| Key | Type | Description |
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|-----|------|-------------|
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| `run_index` | int | 1 = baseline, 2… = follow‑ups |
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| `context_tokens` | int | Tokens after truncation |
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| `ttft_seconds` | float | Wall‑clock seconds to **first** streamed token |
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Concatenate multiple logs with `cat` and plot as you like.
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---
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## 6 · Implementation notes
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* **Safety margin** – `SAFETY_MARGIN = 2048` tokens so the request never
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overruns model context even on tokenizer quirks.
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* **Spinner** – Red arrows animate while waiting for token #1, stop instantly
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on arrival for visual TTFT confirmation.
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* **Tokenizer fallback** – If the matching tokenizer can’t load, the script
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degrades to the heuristic “≈ 4 chars = 1 token”.
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* **Cache‑flush routine** – Sends ten *1‑token* completions built on a
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100 k‑char filler doc to evict KV blocks from VRAM.
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## 7 · Batch driver script (`bench_ttft_sweep.sh`)
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This is an example basic bash script you might use to do a sweep across different context lengths, combining results to one file for easy comparison of caching methods.
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### What the script does
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| Step | Detail |
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|------|--------|
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| **1. Configure variables** | `BENCH` points to the Python benchmark, `MASTER_OUT` is the cumulative log, and `CONTEXT_SIZES` lists the target document lengths (in **tokens**). |
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| **2. Per‑size run** | For each length the script launches the benchmark with:<br>• custom `--max_ctx_tokens` (see above)<br>• one cache‑hit follow‑up (`--num_following 1`)<br>• an explicit **70 B** Llama 3 checkpoint via `--model` |
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| **3. Log collation** | Each invocation writes its own JSONL (`ttft_<N>.jsonl`). Those lines are immediately concatenated into **`all_ttft_results.jsonl`**, producing a tidy file like: <br>`{"run_index":1,"context_tokens":32000,"ttft_seconds":0.45}` |
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| **4. Done banner** | After the loop finishes you get a green check‑mark and the path to the merged log. |
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#### Customising
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* **Change the model** — edit `--model …` to point at any endpoint‑visible name.
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* **Different sizes** — just tweak the `CONTEXT_SIZES` array.
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* **More follow‑ups** — bump `--num_following` if you want deeper cache‑hit sampling.
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