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import json
import os
import shutil
import sys
from pathlib import Path
import torch
from PIL import Image
from mmengine.config import Config
from mmengine.fileio import PetrelBackend, get_file_backend
from peft import PeftModel
from transformers import AutoModel, AutoProcessor, GenerationConfig, StoppingCriteria, StoppingCriteriaList
from xtuner.model.utils import guess_load_checkpoint
from xtuner.registry import BUILDER
REPO_ROOT = Path(__file__).resolve().parents[2]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from projects.vectorllm_hf_0407.configuration_vectorllm import VectorLLMConfig
from projects.vectorllm_hf_0407.image_processing_vectorllm import VectorLLMImageProcessor
from projects.vectorllm_hf_0407.modeling_vectorllm import VectorLLMForCausalLM
from projects.vectorllm_hf_0407.processing_vectorllm import VectorLLMProcessor
DEFAULT_PROMPT = "<pixel>\nPlease extract the regular vector contour of the central building in the image, start from the left top corner and in clockwise."
DEFAULT_RAW_PROMPT = (
"<|im_start|>user\n<pixel>\nPlease extract the regular vector contour of the central building in the image, "
"start from the left top corner and in clockwise.<|im_end|>\n<|im_start|>assistant\n"
)
class StopWordStoppingCriteria(StoppingCriteria):
def __init__(self, tokenizer, stop_word):
self.tokenizer = tokenizer
self.stop_word = stop_word
self.length = len(self.stop_word)
def __call__(self, input_ids, *args, **kwargs) -> bool:
cur_text = self.tokenizer.decode(input_ids[0])
cur_text = cur_text.replace("\r", "").replace("\n", "")
return cur_text[-self.length:] == self.stop_word
def get_stop_criteria(tokenizer, stop_words=None):
stop_words = stop_words or []
stop_criteria = StoppingCriteriaList()
for word in stop_words:
stop_criteria.append(StopWordStoppingCriteria(tokenizer, word))
return stop_criteria
def parse_args():
parser = argparse.ArgumentParser(description="Convert xtuner VectorLLM checkpoint to HF format.")
parser.add_argument("config", help="xtuner config path")
parser.add_argument("pth_model", help="xtuner checkpoint path")
parser.add_argument("--save-path", required=True, help="HF export directory")
parser.add_argument("--demo-image", required=True, help="demo image for validation")
return parser.parse_args()
def seed_local_transformers_modules(local_model_dir):
model_dir = Path(local_model_dir).expanduser().resolve()
if not model_dir.is_dir():
return
hf_home = Path(os.environ.get("HF_HOME", "~/.cache/huggingface")).expanduser()
cache_root = hf_home / "modules" / "transformers_modules"
cache_root.mkdir(parents=True, exist_ok=True)
init_file = cache_root / "__init__.py"
if not init_file.exists():
init_file.write_text("", encoding="utf-8")
for py_file in model_dir.glob("*.py"):
target = cache_root / py_file.name
if not target.exists():
shutil.copy2(py_file, target)
def build_xtuner_model(config_path, pth_model):
cfg = Config.fromfile(config_path)
cfg.model.pretrained_pth = None
seed_local_transformers_modules(cfg.model.visual_encoder.pretrained_model_name_or_path)
seed_local_transformers_modules(cfg.model.llm.pretrained_model_name_or_path)
image_processor = BUILDER.build(cfg.image_processor)
model = BUILDER.build(cfg.model)
backend = get_file_backend(pth_model)
if isinstance(backend, PetrelBackend):
from xtuner.utils.fileio import patch_fileio
with patch_fileio():
state_dict = guess_load_checkpoint(pth_model)
else:
state_dict = guess_load_checkpoint(pth_model)
model.load_state_dict(state_dict, strict=False)
model.eval()
model.preparing_for_generation(metainfo={})
return cfg, model, image_processor
def build_hf_config(cfg, model):
vision_config_path = Path(
cfg.visual_encoder_name_or_path
if hasattr(cfg, "visual_encoder_name_or_path")
else cfg.model.visual_encoder.pretrained_model_name_or_path
)
llm_config = model.llm.config.to_dict()
vision_config = json.loads((vision_config_path / "config.json").read_text())
vision_args = vision_config.get("args", {})
if vision_args:
vision_args["dtype"] = "bfloat16"
vision_args["amp_dtype"] = "bfloat16"
vision_config["torch_dtype"] = "bfloat16"
llm_config["torch_dtype"] = "bfloat16"
pixel_token_idx = model.tokenizer("<pixel>", add_special_tokens=False).input_ids[0]
return VectorLLMConfig(
vision_config=vision_config,
llm_config=llm_config,
regression_size=cfg.model.regression_size,
projector_depth=cfg.model.get("projector_depth", 2),
visual_hidden_size=model.projector.model[0].in_features,
pixel_idx=pixel_token_idx,
pre_resize_size=432,
resized_size=cfg.model.regression_size[0],
patch_size=16,
do_normalize=False,
vision_model_name_or_path="",
llm_name_or_path="",
visual_peft_config=None,
vision_torch_dtype="bfloat16",
torch_dtype="bfloat16",
auto_map={
"AutoConfig": "configuration_vectorllm.VectorLLMConfig",
"AutoModel": "modeling_vectorllm.VectorLLMForCausalLM",
"AutoModelForCausalLM": "modeling_vectorllm.VectorLLMForCausalLM",
"AutoImageProcessor": "image_processing_vectorllm.VectorLLMImageProcessor",
"AutoProcessor": "processing_vectorllm.VectorLLMProcessor",
},
)
def maybe_merge_visual_encoder(visual_encoder):
if isinstance(visual_encoder, PeftModel):
return visual_encoder.merge_and_unload()
if hasattr(visual_encoder, "merge_and_unload"):
return visual_encoder.merge_and_unload()
return visual_encoder
def copy_remote_code(save_path):
src_root = REPO_ROOT / "projects" / "vectorllm_hf_0407"
dst_root = Path(save_path)
for src_path in src_root.glob("*.py"):
shutil.copy2(src_path, dst_root / src_path.name)
radio_src = src_root / "radio_bundle"
radio_dst = dst_root / "radio_bundle"
if radio_dst.exists():
shutil.rmtree(radio_dst, ignore_errors=True)
if radio_dst.exists():
raise RuntimeError(f"Failed to clean export directory: {radio_dst}")
shutil.copytree(radio_src, radio_dst)
def bootstrap_local_registry(model_path):
model_path = Path(model_path).expanduser().resolve()
parent = str(model_path.parent)
package_name = model_path.name
if parent not in sys.path:
sys.path.insert(0, parent)
__import__(package_name)
def decode_generated_text(output, model_inputs, tokenizer):
input_ids = model_inputs.get("input_ids")
input_length = input_ids.shape[-1] if input_ids is not None else 0
if hasattr(output, "sequences"):
generated_ids = output.sequences[0][input_length:]
if generated_ids.numel() == 0:
generated_ids = output.sequences[0]
else:
generated_ids = output[0][input_length:]
if generated_ids.numel() == 0:
generated_ids = output[0]
return tokenizer.decode(generated_ids, skip_special_tokens=False).strip()
def validate_export(save_path, demo_image_path, expected_text):
bootstrap_local_registry(save_path)
model = AutoModel.from_pretrained(
save_path,
trust_remote_code=False,
torch_dtype=torch.bfloat16,
)
processor = AutoProcessor.from_pretrained(save_path, trust_remote_code=False)
tokenizer = processor.tokenizer
if torch.cuda.is_available():
model = model.cuda()
model.eval()
image = Image.open(demo_image_path).convert("RGB")
model_inputs = processor(text=[DEFAULT_RAW_PROMPT], images=[image], return_tensors="pt")
model_inputs = {
key: value.to(model.device) if torch.is_tensor(value) else value
for key, value in model_inputs.items()
}
generation_config = GenerationConfig(
max_new_tokens=640,
do_sample=False,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
temperature=0.0,
top_k=1,
)
stop_criteria = get_stop_criteria(tokenizer, ["<|im_end|>", "<|endoftext|>"])
output = model.generate(
**model_inputs,
generation_config=generation_config,
bos_token_id=tokenizer.bos_token_id,
stopping_criteria=stop_criteria,
output_hidden_states=False,
return_dict_in_generate=True,
do_sample=False,
temperature=0.0,
top_k=1,
)
actual_text = decode_generated_text(output, model_inputs, tokenizer)
return {
"expected_text": expected_text,
"actual_text": actual_text,
"match": actual_text == expected_text,
}
def run_xtuner_reference(model, image_processor, demo_image_path):
image = Image.open(demo_image_path).convert("RGB")
resized_image = image.resize((432, 432), resample=Image.BICUBIC)
pixel_values = image_processor.preprocess(resized_image, return_tensors="pt")["pixel_values"][0]
if torch.cuda.is_available():
pixel_values = pixel_values.cuda()
model = model.cuda()
result = model.predict_forward(
pixel_values=pixel_values,
text_prompts="<image>\nPlease extract the regular vector contour of the central building in the image, start from the left top corner and in clockwise.",
)
return result["prediction"]
def main():
args = parse_args()
save_path = Path(args.save_path).expanduser().resolve()
save_path.mkdir(parents=True, exist_ok=True)
vision_backbone_dir = save_path / "vision_backbone"
if vision_backbone_dir.exists():
shutil.rmtree(vision_backbone_dir)
cfg, xtuner_model, xtuner_image_processor = build_xtuner_model(args.config, args.pth_model)
xtuner_reference_text = run_xtuner_reference(xtuner_model, xtuner_image_processor, args.demo_image)
hf_config = build_hf_config(cfg, xtuner_model)
vision_model = maybe_merge_visual_encoder(xtuner_model.visual_encoder)
hf_model = VectorLLMForCausalLM(
config=hf_config,
vision_model=vision_model,
language_model=xtuner_model.llm,
projector=xtuner_model.projector,
pos_embeds=xtuner_model.viusal_pos_embeddings,
)
hf_model = hf_model.to(dtype=torch.bfloat16)
hf_model.eval()
hf_model.generation_config = xtuner_model.llm.generation_config
hf_model.config.torch_dtype = "bfloat16"
image_processor = VectorLLMImageProcessor(
do_resize=True,
do_rescale=True,
do_normalize=False,
do_convert_rgb=True,
pre_resize_size=432,
resized_size=hf_config.resized_size,
patch_size=hf_config.patch_size,
auto_map={
"AutoImageProcessor": "image_processing_vectorllm.VectorLLMImageProcessor",
"AutoProcessor": "processing_vectorllm.VectorLLMProcessor",
},
)
tokenizer = xtuner_model.tokenizer
processor = VectorLLMProcessor(
image_processor=image_processor,
tokenizer=tokenizer,
chat_template=tokenizer.chat_template,
)
demo_image = Image.open(args.demo_image).convert("RGB")
demo_inputs = processor(
text=[DEFAULT_RAW_PROMPT],
images=[demo_image],
return_tensors="pt",
)
if torch.cuda.is_available():
hf_model = hf_model.cuda()
demo_inputs = {
key: value.to(hf_model.device) if torch.is_tensor(value) else value
for key, value in demo_inputs.items()
}
generation_config = GenerationConfig(
max_new_tokens=640,
do_sample=False,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
temperature=0.0,
top_k=1,
)
stop_criteria = get_stop_criteria(tokenizer, ["<|im_end|>", "<|endoftext|>"])
output = hf_model.generate(
**demo_inputs,
generation_config=generation_config,
bos_token_id=tokenizer.bos_token_id,
stopping_criteria=stop_criteria,
output_hidden_states=False,
return_dict_in_generate=True,
do_sample=False,
temperature=0.0,
top_k=1,
)
pre_save_text = decode_generated_text(output, demo_inputs, tokenizer)
hf_model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
image_processor.save_pretrained(save_path)
processor.save_pretrained(save_path)
copy_remote_code(save_path)
validation = validate_export(str(save_path), args.demo_image, xtuner_reference_text)
validation["xtuner_reference_text"] = xtuner_reference_text
validation["pre_save_hf_text"] = pre_save_text
validation["pre_save_match_xtuner"] = pre_save_text == xtuner_reference_text
(save_path / "conversion_report.json").write_text(
json.dumps(validation, ensure_ascii=False, indent=2) + "\n",
encoding="utf-8",
)
print(json.dumps(validation, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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