File size: 5,694 Bytes
c35c7ce | 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 | from __future__ import annotations
import json
import time
from pathlib import Path
from typing import Any
import torch
from huggingface_hub import snapshot_download
from peft import PeftModel
from PIL import Image
from transformers import (
AutoModelForImageTextToText,
AutoProcessor,
BitsAndBytesConfig,
)
BASE_MODEL_ID = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
MODEL_REPO_ID = "TuWaveGod/Puker_Judge"
MAX_LENGTH = 2048
MAX_IMAGE_LONGEST_EDGE = 1280
BINARY_PROMPT = (
"Judge whether this geometrically assembled playing card has coherent rank, "
"suit, border, portrait, symbols, and continuous artwork. A whole-card "
"180-degree rotation is valid. Answer VALID or INVALID only."
)
def rank_prompt(candidate_count: int) -> str:
if not 2 <= candidate_count <= 4:
raise ValueError("Rank inference requires 2 to 4 candidates.")
labels = ", ".join(str(index) for index in range(1, candidate_count + 1))
return (
"All displayed candidates are geometrically valid reconstructions made "
"from the same playing-card pieces. Select the candidate whose rank, suit, "
"outer border, portrait, symbols, and line artwork form one coherent "
"original playing card. A whole-card 180-degree rotation is equivalent. "
f"The available labels are {labels}. Answer with one label only."
)
def resize_for_model(image: Image.Image) -> Image.Image:
image = image.convert("RGB")
longest = max(image.size)
if longest <= MAX_IMAGE_LONGEST_EDGE:
return image
scale = MAX_IMAGE_LONGEST_EDGE / longest
return image.resize(
(
max(1, int(round(image.width * scale))),
max(1, int(round(image.height * scale))),
),
Image.Resampling.LANCZOS,
)
def load_adapter(
adapter_name: str,
*,
repo_id: str = MODEL_REPO_ID,
base_model_id: str = BASE_MODEL_ID,
int4: bool = False,
) -> tuple[Any, Any, torch.device, dict[str, float]]:
if adapter_name not in {"binary_adapter", "rank_adapter"}:
raise ValueError(f"Unknown adapter: {adapter_name}")
if not torch.cuda.is_available():
raise RuntimeError("A CUDA GPU is required by these example scripts.")
local_repo = Path(repo_id).expanduser()
if local_repo.is_dir():
snapshot_path = local_repo.resolve()
download_seconds = 0.0
else:
download_started = time.perf_counter()
snapshot_path = Path(
snapshot_download(
repo_id=repo_id,
allow_patterns=[
f"{adapter_name}/*",
"processor/*",
],
)
)
download_seconds = time.perf_counter() - download_started
processor = AutoProcessor.from_pretrained(snapshot_path / "processor")
load_kwargs: dict[str, Any] = {
"torch_dtype": torch.bfloat16,
"attn_implementation": "sdpa",
}
if int4:
load_kwargs.update(
{
"quantization_config": BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
),
"device_map": {"": 0},
}
)
load_started = time.perf_counter()
base_model = AutoModelForImageTextToText.from_pretrained(
base_model_id,
**load_kwargs,
)
if not int4:
base_model = base_model.to("cuda:0")
model = PeftModel.from_pretrained(
base_model,
snapshot_path / adapter_name,
).eval()
torch.cuda.synchronize()
load_seconds = time.perf_counter() - load_started
return (
model,
processor,
torch.device("cuda:0"),
{
"snapshot_download_seconds": download_seconds,
"model_load_seconds": load_seconds,
},
)
def encode_image_prompt(
processor: Any,
image: Image.Image,
prompt: str,
device: torch.device,
) -> dict[str, Any]:
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": prompt},
],
}
]
text = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=False,
)
inputs = processor(
text=text,
images=resize_for_model(image),
return_tensors="pt",
truncation=True,
max_length=MAX_LENGTH,
)
moved: dict[str, Any] = {}
for key, value in inputs.items():
if not isinstance(value, torch.Tensor):
moved[key] = value
elif key == "pixel_values":
moved[key] = value.to(device=device, dtype=torch.bfloat16)
else:
moved[key] = value.to(device=device)
return moved
def generate_answer(
model: Any,
processor: Any,
inputs: dict[str, Any],
*,
max_new_tokens: int = 4,
) -> tuple[str, float]:
input_length = int(inputs["input_ids"].shape[1])
torch.cuda.synchronize()
started = time.perf_counter()
with torch.inference_mode():
output_ids = model.generate(
**inputs,
do_sample=False,
max_new_tokens=max_new_tokens,
)
torch.cuda.synchronize()
elapsed = time.perf_counter() - started
answer = processor.decode(
output_ids[0, input_length:],
skip_special_tokens=True,
).strip()
return answer, elapsed
def print_json(payload: dict[str, Any]) -> None:
print(json.dumps(payload, ensure_ascii=False, indent=2))
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