File size: 6,245 Bytes
fb35018 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | import json
import os
import time
import threading
from dataclasses import dataclass, asdict
import psutil
import requests
import torch
from PIL import Image
from transformers import AutoModelForImageTextToText, AutoProcessor
MODEL_ID = "Dharunkumar9/SmolVLM-256M-Instruct-Agri"
OUT_JSON = os.path.join(os.path.dirname(__file__), "benchmark_results.json")
SAMPLE_IMAGE_URL = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png"
@dataclass
class CaseResult:
name: str
input_tokens: int
generated_tokens: int
latency_s: float
tokens_per_s: float
peak_rss_mb: float
output_preview: str
class MemoryMonitor:
def __init__(self, process: psutil.Process, interval_s: float = 0.01):
self.process = process
self.interval_s = interval_s
self._running = False
self._thread = None
self.max_rss = 0
def _run(self):
while self._running:
rss = self.process.memory_info().rss
if rss > self.max_rss:
self.max_rss = rss
time.sleep(self.interval_s)
def __enter__(self):
self._running = True
self.max_rss = self.process.memory_info().rss
self._thread = threading.Thread(target=self._run, daemon=True)
self._thread.start()
return self
def __exit__(self, exc_type, exc, tb):
self._running = False
if self._thread is not None:
self._thread.join(timeout=1)
def pick_device():
if torch.backends.mps.is_available():
return "mps", torch.float16
if torch.cuda.is_available():
return "cuda", torch.bfloat16
return "cpu", torch.float32
def make_prompt(processor, text: str, with_image: bool):
if with_image:
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": text},
],
}
]
else:
messages = [{"role": "user", "content": [{"type": "text", "text": text}]}]
return processor.apply_chat_template(messages, add_generation_prompt=True)
def prepare_inputs(processor, prompt: str, image: Image.Image | None, device: str):
kwargs = {"text": prompt, "return_tensors": "pt"}
if image is not None:
kwargs["images"] = [image]
inputs = processor(**kwargs)
return {k: (v.to(device) if torch.is_tensor(v) else v) for k, v in inputs.items()}
def run_case(model, processor, device: str, case_name: str, text: str, image: Image.Image | None, max_new_tokens: int = 64):
prompt = make_prompt(processor, text, with_image=image is not None)
inputs = prepare_inputs(processor, prompt, image, device)
input_tokens = int(inputs["input_ids"].shape[1])
proc = psutil.Process(os.getpid())
with MemoryMonitor(proc) as mon:
t0 = time.perf_counter()
with torch.inference_mode():
out = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=False,
use_cache=True,
)
t1 = time.perf_counter()
latency = t1 - t0
generated_tokens = int(out.shape[1] - input_tokens)
tps = float(generated_tokens / latency) if latency > 0 else 0.0
decoded = processor.batch_decode(out[:, input_tokens:], skip_special_tokens=True)
preview = (decoded[0] if decoded else "").strip().replace("\n", " ")[:220]
return CaseResult(
name=case_name,
input_tokens=input_tokens,
generated_tokens=generated_tokens,
latency_s=round(latency, 3),
tokens_per_s=round(tps, 3),
peak_rss_mb=round(mon.max_rss / (1024 * 1024), 2),
output_preview=preview,
)
def main():
process = psutil.Process(os.getpid())
rss_start_mb = process.memory_info().rss / (1024 * 1024)
device, dtype = pick_device()
print(f"Device: {device}, dtype: {dtype}")
t0 = time.perf_counter()
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForImageTextToText.from_pretrained(
MODEL_ID,
torch_dtype=dtype,
low_cpu_mem_usage=True,
attn_implementation="eager",
).to(device)
model.eval()
t1 = time.perf_counter()
load_time_s = t1 - t0
rss_after_load_mb = process.memory_info().rss / (1024 * 1024)
print("Downloading sample image...")
img_bytes = requests.get(SAMPLE_IMAGE_URL, timeout=30).content
image = Image.open(__import__("io").BytesIO(img_bytes)).convert("RGB")
# Warm-up
_ = run_case(
model,
processor,
device,
"warmup",
"Describe this image briefly.",
image,
max_new_tokens=16,
)
cases = [
("text_only_short", "You are an agri assistant. Give 3 tips for identifying early leaf blight.", None, 64),
(
"image_short",
"What do you see in this image? Mention crop/plant clues if visible.",
image,
64,
),
(
"image_long",
"Analyze this image for agriculture relevance. Return: 1) likely object/plant, 2) possible health indicators, 3) recommended next observation steps, 4) confidence from 0-1.",
image,
96,
),
]
results = []
for name, text, img, max_new_tokens in cases:
print(f"Running case: {name}")
results.append(asdict(run_case(model, processor, device, name, text, img, max_new_tokens=max_new_tokens)))
payload = {
"model_id": MODEL_ID,
"device": device,
"dtype": str(dtype),
"load_time_s": round(load_time_s, 3),
"rss_start_mb": round(rss_start_mb, 2),
"rss_after_load_mb": round(rss_after_load_mb, 2),
"model_num_parameters": int(model.num_parameters()),
"transformers_version": __import__("transformers").__version__,
"torch_version": torch.__version__,
"cases": results,
}
with open(OUT_JSON, "w", encoding="utf-8") as f:
json.dump(payload, f, indent=2)
print(f"Saved results to {OUT_JSON}")
print(json.dumps(payload, indent=2))
if __name__ == "__main__":
main()
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