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