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import argparse
import logging
import os
from typing import Optional
import torch
from PIL import Image
import requests
from io import BytesIO
import yaml
from transformers import GenerationConfig, AutoProcessor
# Allow running without installation when working inside the repo
import sys
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from larm.memory_generator.memgen_model import LatentMemoryModel
def _load_image(image_path_or_url: str) -> Image.Image:
"""Load image from local path or URL as RGB PIL.Image."""
if image_path_or_url.startswith("http"):
resp = requests.get(image_path_or_url, timeout=30)
resp.raise_for_status()
return Image.open(BytesIO(resp.content)).convert("RGB")
if not os.path.exists(image_path_or_url):
raise FileNotFoundError(f"Image not found: {image_path_or_url}")
return Image.open(image_path_or_url).convert("RGB")
def build_inputs(processor, messages, image: Optional[Image.Image] = None):
"""Build model inputs (input_ids, attention_mask, pixel_values, image_grid_thw)."""
if image is not None:
# Apply chat template first so that processor knows where to insert image tokens
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
enc = processor(text=[text], images=[image], return_tensors="pt", padding=False)
else:
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
enc = processor(text=[text], return_tensors="pt", padding=False)
input_ids = enc["input_ids"]
attention_mask = enc["attention_mask"]
pixel_values = enc.get("pixel_values")
image_grid_thw = enc.get("image_grid_thw")
if pixel_values is not None:
pixel_values = pixel_values.to(torch.bfloat16)
return input_ids, attention_mask, pixel_values, image_grid_thw
def load_model_from_cfg(cfg_path: str, device: torch.device):
"""Load LatentMemoryModel from a YAML config (same structure as training)."""
with open(cfg_path, "r", encoding="utf-8") as f:
cfg = yaml.safe_load(f)
model_cfg = cfg["model"] if "model" in cfg else cfg # support wrapped config
model = LatentMemoryModel.from_config(model_cfg).to(device)
model.eval()
return model
def main():
parser = argparse.ArgumentParser(description="LatentMemoryModel inference script")
parser.add_argument("--cfg", required=True, help="Path to YAML config used to instantiate the model")
parser.add_argument("--image", help="Optional image path or URL")
parser.add_argument("--text", required=True, help="User prompt text")
parser.add_argument("--max_new_tokens", type=int, default=256)
parser.add_argument("--temperature", type=float, default=1)
parser.add_argument("--do_sample", action="store_true", help="Enable sampling")
parser.add_argument("--options", nargs="*", help="Override model config via KEY VALUE pairs, e.g. --options model.max_prompt_aug_num 0")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO, format="[%(levelname)s] %(message)s")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logging.info(f"Using device: {device}")
# 1. Load model & processor
# Handle overrides
overrides = args.options or []
if len(overrides) % 2 != 0:
raise ValueError("--options should contain KEY VALUE pairs")
if overrides:
import copy, yaml
with open(args.cfg, "r", encoding="utf-8") as f:
base_cfg = yaml.safe_load(f)
cfg = copy.deepcopy(base_cfg)
def set_nested(cfg_dict, key_path, value):
keys = key_path.split('.')
cur = cfg_dict
for k in keys[:-1]:
if k not in cur or not isinstance(cur[k], dict):
cur[k] = {}
cur = cur[k]
# try to cast value to int/float/bool
if value.lower() == 'null':
val_cast = None
else:
for cast in (int, float):
try:
val_cast = cast(value)
break
except ValueError:
val_cast = value
if value.lower() in ("true", "false"):
val_cast = value.lower() == "true"
cur[keys[-1]] = val_cast
for k, v in zip(overrides[::2], overrides[1::2]):
set_nested(cfg, k, v)
# write to tmp then load
import tempfile, os
with tempfile.NamedTemporaryFile("w", delete=False, suffix=".yaml") as tmp:
yaml.safe_dump(cfg, tmp)
tmp_path = tmp.name
model = load_model_from_cfg(tmp_path, device)
os.remove(tmp_path)
else:
model = load_model_from_cfg(args.cfg, device)
processor = model.processor # AutoProcessor loaded inside the model
# 2. Build messages list
messages = []
if args.image:
image = _load_image(args.image)
messages.append({
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": args.text},
],
})
else:
messages.append({
"role": "user",
"content": args.text,
})
# 3. Tokenize / encode
input_ids, attention_mask, pixel_values, image_grid_thw = build_inputs(processor, messages, image if args.image else None)
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
if pixel_values is not None:
pixel_values = pixel_values.to(device)
if image_grid_thw is not None:
image_grid_thw = image_grid_thw.to(device)
# 4. Build generation config
gen_cfg = GenerationConfig(
max_new_tokens=args.max_new_tokens,
temperature=args.temperature,
do_sample=args.do_sample,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
)
# 5. Generate
with torch.no_grad():
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
generation_config=gen_cfg,
pixel_values=pixel_values,
image_grid_thw=image_grid_thw,
)
if isinstance(outputs, tuple): # when return_augmentation_mask=True
outputs = outputs[0]
full_ids = outputs[0].detach().cpu()
prompt_len = input_ids.size(1)
# 5.a Only the assistant completion (clean)
gen_only = full_ids[prompt_len:]
gen_only_valid = [tid for tid in gen_only.tolist() if tid >= 0]
clean_text = processor.tokenizer.decode(gen_only_valid, skip_special_tokens=True)
print("\n===== ASSISTANT (clean) =====\n")
print(clean_text)
# 5.b Only after the last <|image_pad|> token
image_pad_id = processor.tokenizer.convert_tokens_to_ids("<|image_pad|>")
ids_list = full_ids.tolist()
try:
last_pad_idx = len(ids_list) - 1 - ids_list[::-1].index(image_pad_id)
start_after_skip = last_pad_idx + 1
except ValueError:
start_after_skip = 0
sliced_after_skip = ids_list[start_after_skip:]
# Also remove any residual <|image_pad|> that might appear later
filtered_after_skip = [tid for tid in sliced_after_skip if tid != image_pad_id]
raw_text_with_markers = processor.tokenizer.decode(filtered_after_skip, skip_special_tokens=False)
print("\n===== RAW (after <|image_pad|>, with special tokens) =====\n")
print(raw_text_with_markers)
# # 5.c Token-by-token dump (ids + tokens)
# dump_limit = full_ids.numel()
# print("\n===== TOKEN DUMP (after <|image_pad|>, skip <|image_pad|>) =====")
# for idx in range(start_after_skip, dump_limit):
# tid = int(full_ids[idx].item())
# if tid == image_pad_id:
# continue
# tok = processor.tokenizer.decode([tid], skip_special_tokens=False)
# print(f"[{idx:04d}] id={tid:<8} token={tok}")
if __name__ == "__main__":
main()
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