mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-07-20 17:49:18 +00:00
211 lines
8.7 KiB
Python
211 lines
8.7 KiB
Python
#!/usr/bin/env python3
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import argparse
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import json
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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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import datasets
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import logging
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import matplotlib.pyplot as plt
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import numpy as np
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import requests
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from tqdm.contrib.concurrent import thread_map
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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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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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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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n_failures: int = 0
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while True:
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try:
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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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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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return {"process": process, "address": address, "fout": fout}
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def get_prompt_length(data: dict) -> int:
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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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response = session.post(
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f"{server_address}/tokenize",
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json={"content": prompt, "add_special": 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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tokens: list[str] = json.loads(response.text)["tokens"]
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return len(tokens)
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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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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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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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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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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_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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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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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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finally:
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if server is not None:
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server["process"].terminate()
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server["process"].wait()
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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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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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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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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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token_t = np.array(token_t, dtype=np.float64)
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token_t -= t0
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token_t_last = np.max(token_t)
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logger.info("")
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logger.info(f"Benchmark duration: {token_t_last:.2f} s")
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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"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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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.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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bin_max = np.ceil(token_t_last) + 1
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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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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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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("--n_predict", type=int, default=2048, help="Max. number of tokens to predict per prompt")
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args = parser.parse_args()
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benchmark(**vars(args))
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