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6bcddd0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | import json
import os
import re
import time
from typing import Any
import modal
APP_NAME = "virtual-characters-gemma-benchmark"
MODEL_ID = os.environ.get("VC_BENCH_MODEL", "google/gemma-4-12B-it")
GPU = os.environ.get("VC_BENCH_GPU", "L40S")
MODEL_DIR = "/root/.cache/huggingface"
HF_SECRET_NAME = os.environ.get("VC_HF_SECRET_NAME", "hf-token")
image = (
modal.Image.from_registry("nvidia/cuda:12.9.0-devel-ubuntu22.04", add_python="3.12")
.entrypoint([])
.uv_pip_install(
"accelerate>=1.8.0",
"huggingface-hub>=0.36.0",
"librosa>=0.10.2",
"pillow>=11.0.0",
"safetensors>=0.5.0",
"torch>=2.7.0",
"torchvision>=0.22.0",
"transformers>=4.57.0",
)
.env({"HF_HUB_CACHE": MODEL_DIR, "HF_XET_HIGH_PERFORMANCE": "1"})
)
hf_cache = modal.Volume.from_name("vc-hf-cache", create_if_missing=True)
app = modal.App(APP_NAME, image=image)
def _strip_empty_thought_prefix(text: str) -> str:
return re.sub(r"^\s*(?:<\|channel\>)?thought\s*(?:<channel\|>)?", "", text, count=1).strip()
@app.function(
gpu=GPU,
timeout=60 * 45,
scaledown_window=60,
secrets=[modal.Secret.from_name(HF_SECRET_NAME)],
volumes={MODEL_DIR: hf_cache},
)
def run_benchmark(prompt: str, max_new_tokens: int = 64, enable_thinking: bool = False) -> dict[str, Any]:
import torch
from transformers import AutoModelForMultimodalLM, AutoProcessor
result: dict[str, Any] = {
"model_id": MODEL_ID,
"gpu": GPU,
"max_new_tokens": max_new_tokens,
"enable_thinking": enable_thinking,
}
remote_started = time.perf_counter()
t0 = time.perf_counter()
processor = AutoProcessor.from_pretrained(MODEL_ID)
result["processor_load_s"] = round(time.perf_counter() - t0, 3)
t0 = time.perf_counter()
model = AutoModelForMultimodalLM.from_pretrained(MODEL_ID, dtype="auto", device_map="auto")
model.eval()
if torch.cuda.is_available():
torch.cuda.synchronize()
torch.cuda.reset_peak_memory_stats()
result["model_load_s"] = round(time.perf_counter() - t0, 3)
messages = [
{"role": "system", "content": "你是一个中文虚拟角色对话模型。回答要自然、简短、有角色感。"},
{"role": "user", "content": prompt},
]
t0 = time.perf_counter()
try:
inputs = processor.apply_chat_template(
messages,
tokenize=True,
return_dict=True,
return_tensors="pt",
add_generation_prompt=True,
enable_thinking=enable_thinking,
).to(model.device)
except TypeError:
inputs = processor.apply_chat_template(
messages,
tokenize=True,
return_dict=True,
return_tensors="pt",
add_generation_prompt=True,
).to(model.device)
input_len = int(inputs["input_ids"].shape[-1])
result["prompt_tokens"] = input_len
result["prepare_input_s"] = round(time.perf_counter() - t0, 3)
generate_kwargs = {
**inputs,
"max_new_tokens": max_new_tokens,
"do_sample": False,
"pad_token_id": getattr(processor.tokenizer, "eos_token_id", None),
}
if torch.cuda.is_available():
torch.cuda.synchronize()
t0 = time.perf_counter()
outputs = model.generate(**generate_kwargs)
if torch.cuda.is_available():
torch.cuda.synchronize()
generation_s = time.perf_counter() - t0
new_token_ids = outputs[0][input_len:]
output_tokens = int(new_token_ids.shape[-1])
decoded = processor.decode(new_token_ids, skip_special_tokens=True)
preview = _strip_empty_thought_prefix(decoded)
result.update(
{
"output_tokens": output_tokens,
"generation_s": round(generation_s, 3),
"tokens_per_s": round(output_tokens / generation_s, 3) if generation_s > 0 else None,
"remote_function_s": round(time.perf_counter() - remote_started, 3),
"response_preview": preview[:300],
}
)
if torch.cuda.is_available():
result["cuda_name"] = torch.cuda.get_device_name(0)
result["cuda_peak_memory_gb"] = round(torch.cuda.max_memory_allocated() / 1024**3, 3)
return result
@app.local_entrypoint()
def main(
prompt: str = "我今天有点累，请你用虚拟角色的语气安慰我，并给我一个很短的建议。",
max_new_tokens: int = 64,
enable_thinking: bool = False,
):
started = time.perf_counter()
result = run_benchmark.remote(prompt=prompt, max_new_tokens=max_new_tokens, enable_thinking=enable_thinking)
result["client_wall_s"] = round(time.perf_counter() - started, 3)
print(json.dumps(result, ensure_ascii=False, indent=2))
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