mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-07-18 16:47:42 +00:00
scripts: synthetic prompt mode for server-bench.py (#14695)
This commit is contained in:
Johannes Gäßler
GitHub
187
scripts/server-bench.py
Normal file → Executable file
187
scripts/server-bench.py
Normal file → Executable file
@ -2,9 +2,11 @@
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import argparse
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import json
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import os
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import random
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import subprocess
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from time import sleep, time
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from typing import Optional
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from typing import Optional, Union
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import datasets
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import logging
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@ -18,31 +20,39 @@ logging.basicConfig(level=logging.INFO, format='%(message)s')
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logger = logging.getLogger("server-bench")
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def get_prompts(n_prompts: int) -> list[str]:
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logger.info("Loading MMLU dataset...")
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ret = datasets.load_dataset("cais/mmlu", "all")["test"]["question"] # type: ignore
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def get_prompts_text(dataset_name: str, n_prompts: int) -> Optional[list[str]]:
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ret = []
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if dataset_name.lower() == "mmlu":
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logger.info("Loading MMLU dataset...")
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ret = datasets.load_dataset("cais/mmlu", "all")["test"]["question"] # type: ignore
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else:
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return None
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if n_prompts >= 0:
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ret = ret[:n_prompts]
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return ret
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def get_server(path_server: str, path_model: str, path_log: Optional[str], port: int, n_gpu_layers: int, parallel: int, ctx_size: int) -> dict:
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logger.info("Starting the llama.cpp server...")
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address = f"http://localhost:{port}"
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def get_prompt_lengths_rng(n_prompts: int, prompt_length_min: int, prompt_length_max: int) -> list[int]:
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assert n_prompts >= 0
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ret: list[int] = []
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for i in range(n_prompts):
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random.seed(13 * i + 0)
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ret.append(random.randint(prompt_length_min, prompt_length_max))
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return ret
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popen_args: list[str] = [
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path_server,
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"--flash-attn",
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"--n-gpu-layers", str(n_gpu_layers),
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"--parallel", str(parallel),
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"--ctx-size", str(parallel * ctx_size),
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"--model", path_model,
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"--port", str(port),
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"--swa-full", # FIXME performance bad otherwise
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# "--attn-streams",
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]
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fout = open("bench.log", "w") if path_log is not None else subprocess.DEVNULL
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process = subprocess.Popen(popen_args, stdout=fout, stderr=subprocess.STDOUT)
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def get_prompts_rng(prompt_lengths: list[int]) -> list[list[int]]:
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return [[random.randint(100, 10000) for _ in range(pl)] for pl in prompt_lengths]
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def get_server(path_server: str, path_log: Optional[str]) -> dict:
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logger.info("Starting the llama.cpp server...")
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hostname: str = os.environ.get("LLAMA_ARG_HOST", "127.0.0.1")
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port: str = os.environ.get("LLAMA_ARG_PORT", "8080")
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address: str = f"http://{hostname}:{port}"
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fout = open(path_log, "w") if path_log is not None else subprocess.DEVNULL
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process = subprocess.Popen([path_server], stdout=fout, stderr=subprocess.STDOUT)
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n_failures: int = 0
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while True:
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@ -50,14 +60,14 @@ def get_server(path_server: str, path_model: str, path_log: Optional[str], port:
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sleep(1.0)
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exit_code = process.poll()
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if exit_code is not None:
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raise RuntimeError(f"llama.cpp server for {path_model} exited unexpectedly with exit code {exit_code}")
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raise RuntimeError(f"llama.cpp server exited unexpectedly with exit code {exit_code}, see {path_log}")
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response = requests.get(f"{address}/health")
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if response.status_code == 200:
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break
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except requests.ConnectionError:
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n_failures += 1
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if n_failures >= 10:
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raise RuntimeError(f"llama.cpp server for {path_model} is not healthy after 10 seconds")
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raise RuntimeError("llama.cpp server is not healthy after 10 seconds")
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return {"process": process, "address": address, "fout": fout}
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@ -87,58 +97,97 @@ def send_prompt(data: dict) -> tuple[float, list[float]]:
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session = data["session"]
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server_address: str = data["server_address"]
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response = session.post(
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f"{server_address}/apply-template",
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json={"messages": [{"role": "user", "content": data["prompt"], "stream": True}]}
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)
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if response.status_code != 200:
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raise RuntimeError(f"Server returned status code {response.status_code}: {response.text}")
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prompt: str = json.loads(response.text)["prompt"]
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t_submit = time()
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if data["synthetic_prompt"]:
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json_data: dict = {
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"prompt": data["prompt"], "ignore_eos": True, "cache_prompt": False,
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"seed": data["seed"], "n_predict": data["n_predict"], "stream": True}
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response = session.post(f"{server_address}/completion", json=json_data, stream=True)
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else:
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response = session.post(
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f"{server_address}/apply-template",
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json={"messages": [{"role": "user", "content": data["prompt"], "stream": True}]}
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)
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if response.status_code != 200:
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raise RuntimeError(f"Server returned status code {response.status_code}: {response.text}")
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prompt: str = json.loads(response.text)["prompt"]
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json_data: dict = {"prompt": prompt, "seed": data["seed"], "n_predict": data["n_predict"], "stream": True}
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response = session.post(f"{server_address}/completion", json=json_data, stream=True)
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json_data: dict = {"prompt": prompt, "seed": data["seed"], "n_predict": data["n_predict"], "stream": True}
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response = session.post(f"{server_address}/completion", json=json_data, stream=True)
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last_valid_line: str = ""
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token_arrival_times: list[float] = []
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for line in response.iter_lines(decode_unicode=True):
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if not line.startswith("data: "):
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for line in response.iter_lines(decode_unicode=False):
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if not line.startswith(b"data: "):
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continue
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last_valid_line = line
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token_arrival_times.append(time())
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token_arrival_times = token_arrival_times[:-1]
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if response.status_code != 200:
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raise RuntimeError(f"Server returned status code {response.status_code}: {response.text}")
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timings: dict = json.loads(last_valid_line[6:])["timings"]
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return (timings["prompt_ms"], token_arrival_times)
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return (t_submit, token_arrival_times)
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def benchmark(path_server: str, path_model: str, path_log: Optional[str], port: int, n_gpu_layers: int, parallel: int, ctx_size: int, n_prompts: int, n_predict: int):
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num_workers: int = parallel + 1
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prompts: list[str] = get_prompts(n_prompts)
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def benchmark(path_server: str, path_log: Optional[str], prompt_source: str, n_prompts: int, n_predict: int, n_predict_min: int):
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if os.environ.get("LLAMA_ARG_N_PARALLEL") is None:
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logger.info("LLAMA_ARG_N_PARALLEL not explicitly set, using 32")
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os.environ["LLAMA_ARG_N_PARALLEL"] = "32"
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if os.environ.get("LLAMA_ARG_N_GPU_LAYERS") is None:
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logger.info("LLAMA_ARG_N_GPU_LAYERS not explicitly set, using 999")
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os.environ["LLAMA_ARG_N_GPU_LAYERS"] = "999"
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if os.environ.get("LLAMA_ARG_FLASH_ATTN") is None:
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logger.info("LLAMA_ARG_FLASH_ATTN not explicitly set, using 'true'")
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os.environ["LLAMA_ARG_FLASH_ATTN"] = "true"
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parallel: int = int(os.environ.get("LLAMA_ARG_N_PARALLEL", 1))
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prompts: Union[None, list[str], list[list[int]]] = get_prompts_text(prompt_source, n_prompts)
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synthetic_prompts: bool = prompts is None
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prompt_n = []
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if synthetic_prompts:
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prompt_source_split: list[str] = prompt_source.split("-")
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assert len(prompt_source_split) == 3
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assert prompt_source_split[0].lower() == "rng"
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prompt_length_min: int = int(prompt_source_split[1])
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prompt_length_max: int = int(prompt_source_split[2])
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logger.info("Generating random prompts...")
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prompt_n = get_prompt_lengths_rng(n_prompts, prompt_length_min, prompt_length_max)
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prompts = get_prompts_rng(prompt_n)
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else:
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n_predict_min = n_predict
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if os.environ.get("LLAMA_ARG_CTX_SIZE") is None:
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context_per_slot: int = int(1.05 * (n_predict + (np.max(prompt_n) if synthetic_prompts else 2048)))
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context_total: int = context_per_slot * parallel
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os.environ["LLAMA_ARG_CTX_SIZE"] = str(context_total)
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logger.info(f"LLAMA_ARG_CTX_SIZE not explicitly set, using {context_total} ({context_per_slot} per slot).")
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server: Optional[dict] = None
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session = None
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try:
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server = get_server(path_server, path_model, path_log, port, n_gpu_layers, parallel, ctx_size)
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server = get_server(path_server, path_log)
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server_address: str = server["address"]
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adapter = requests.adapters.HTTPAdapter(pool_connections=num_workers, pool_maxsize=num_workers) # type: ignore
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adapter = requests.adapters.HTTPAdapter(pool_connections=parallel, pool_maxsize=parallel) # type: ignore
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session = requests.Session()
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session.mount("http://", adapter)
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session.mount("https://", adapter)
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data: list[dict] = []
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for i, p in enumerate(prompts):
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data.append({"session": session, "server_address": server_address, "prompt": p, "n_predict": n_predict, "seed": i})
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logger.info("Getting the prompt lengths...")
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prompt_n = [get_prompt_length(d) for d in data]
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for i, p in enumerate(prompts):
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random.seed(13 * i + 1)
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data.append({
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"session": session, "server_address": server_address, "prompt": p, "synthetic_prompt": synthetic_prompts,
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"n_predict": random.randint(n_predict_min, n_predict), "seed": 13 * i + 2})
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if not synthetic_prompts:
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logger.info("Getting the prompt lengths...")
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prompt_n = [get_prompt_length(d) for d in data]
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logger.info("Starting the benchmark...\n")
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t0 = time()
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results: list[tuple[int, list[float]]] = thread_map(send_prompt, data, max_workers=num_workers, chunksize=1)
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results: list[tuple[float, list[float]]] = thread_map(send_prompt, data, max_workers=parallel, chunksize=1)
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finally:
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if server is not None:
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server["process"].terminate()
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@ -146,17 +195,18 @@ def benchmark(path_server: str, path_model: str, path_log: Optional[str], port:
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if session is not None:
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session.close()
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prompt_ms = []
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prompt_t = []
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token_t = []
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depth_sum: int = 0
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for pn, (pms, tat) in zip(prompt_n, results):
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prompt_ms.append(pms)
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for pn, (t_submit, tat) in zip(prompt_n, results):
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prompt_t.append(tat[0] - t_submit)
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token_t += tat
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n_tokens: int = len(tat)
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depth_sum += n_tokens * pn
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depth_sum += n_tokens * (n_tokens + 1) // 2
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assert len(token_t) > 0
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prompt_n = np.array(prompt_n, dtype=np.int64)
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prompt_ms = np.array(prompt_ms, dtype=np.float64)
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prompt_t = np.array(prompt_t, dtype=np.float64)
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token_t = np.array(token_t, dtype=np.float64)
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token_t -= t0
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@ -167,18 +217,21 @@ def benchmark(path_server: str, path_model: str, path_log: Optional[str], port:
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logger.info(f"Request throughput: {n_prompts / token_t_last:.2f} requests/s = {n_prompts / (token_t_last/60):.2f} requests/min")
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logger.info(f"Total prompt length: {np.sum(prompt_n)} tokens")
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logger.info(f"Average prompt length: {np.mean(prompt_n):.2f} tokens")
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logger.info(f"Average prompt latency: {np.mean(prompt_ms):.2f} ms")
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logger.info(f"Average prompt speed: {np.sum(prompt_n) / (1e-3 * np.sum(prompt_ms)):.2f} tokens/s")
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logger.info(f"Average prompt latency: {1e3 * np.mean(prompt_t):.2f} ms")
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logger.info(f"Average prompt speed: {np.sum(prompt_n) / np.sum(prompt_t):.2f} tokens/s")
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logger.info(f"Total generated tokens: {token_t.shape[0]}")
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logger.info(f"Average generation depth: {depth_sum / token_t.shape[0]:.2f} tokens")
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logger.info(f"Average total generation speed: {token_t.shape[0] / token_t_last:.2f} tokens/s")
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logger.info(f"Average generation speed per slot: {token_t.shape[0] / (parallel * token_t_last):.2f} tokens/s / slot")
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logger.info("")
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logger.info(
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"The above numbers are the speeds as observed by the Python script and may differ from the performance reported by the server, "
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"particularly when the server is fast vs. the network or Python script (e.g. when serving a very small model).")
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plt.figure()
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plt.scatter(prompt_n, prompt_ms, s=10.0, marker=".", alpha=0.25)
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plt.xlim(0, 1.05 * np.max(prompt_n))
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plt.ylim(0, 1.05 * np.max(prompt_ms))
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plt.title(path_model)
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plt.scatter(prompt_n, 1e3 * prompt_t, s=10.0, marker=".", alpha=0.25)
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plt.xlim(0, 1.05e0 * np.max(prompt_n))
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plt.ylim(0, 1.05e3 * np.max(prompt_t))
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plt.xlabel("Prompt length [tokens]")
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plt.ylabel("Time to first token [ms]")
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plt.savefig("prompt_time.png", dpi=240)
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@ -187,7 +240,6 @@ def benchmark(path_server: str, path_model: str, path_log: Optional[str], port:
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plt.figure()
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plt.hist(token_t, np.arange(0, bin_max))
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plt.xlim(0, bin_max + 1)
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plt.title(path_model)
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plt.xlabel("Time [s]")
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plt.ylabel("Num. tokens generated per second")
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plt.savefig("gen_rate.png", dpi=240)
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@ -196,15 +248,18 @@ def benchmark(path_server: str, path_model: str, path_log: Optional[str], port:
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Tool for benchmarking the throughput of the llama.cpp HTTP server. "
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"Results are printed to console and visualized as plots (saved to current working directory).")
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"Results are printed to console and visualized as plots (saved to current working directory). "
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"To pass arguments such as the model path to the server, set the corresponding environment variables (see llama-server --help).")
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parser.add_argument("--path_server", type=str, default="llama-server", help="Path to the llama.cpp server binary")
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parser.add_argument("--path_model", type=str, required=True, help="Path to the model to use for the benchmark")
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parser.add_argument("--path_log", type=str, default=None, help="Path to the model to use for the benchmark")
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parser.add_argument("--port", type=int, default=18725, help="Port to use for the server during the benchmark")
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parser.add_argument("--n_gpu_layers", type=int, default=999, help="Number of GPU layers for the server")
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parser.add_argument("--parallel", type=int, default=16, help="Number of slots for the server")
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parser.add_argument("--ctx_size", type=int, default=4096, help="Server context size per slot")
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parser.add_argument("--n_prompts", type=int, default=1000, help="Number of prompts to evaluate")
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parser.add_argument("--path_log", type=str, default="server-bench.log", help="Path to the model to use for the benchmark")
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parser.add_argument(
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"--prompt_source", type=str, default="rng-1024-2048",
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help="How to get the prompts for the benchmark, either 'mmlu' for MMLU questions or "
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"rng-MIN-MAX for synthetic prompts with random lengths in the interval [MIN, MAX]")
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parser.add_argument("--n_prompts", type=int, default=100, help="Number of prompts to evaluate")
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parser.add_argument("--n_predict", type=int, default=2048, help="Max. number of tokens to predict per prompt")
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parser.add_argument(
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"--n_predict_min", type=int, default=1024,
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help="Min. number of tokens to predict per prompt (supported for synthetic prompts only)")
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args = parser.parse_args()
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benchmark(**vars(args))
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@ -7,7 +7,7 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
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**Features:**
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* LLM inference of F16 and quantized models on GPU and CPU
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* [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes
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* Reranking endoint (https://github.com/ggml-org/llama.cpp/pull/9510)
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* Reranking endpoint (https://github.com/ggml-org/llama.cpp/pull/9510)
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* Parallel decoding with multi-user support
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* Continuous batching
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* Multimodal ([documentation](../../docs/multimodal.md)) / with OpenAI-compatible API support
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