File size: 2,896 Bytes
a8f5df5 | 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 | """Strict, component-only loaders. No language model allocation or remote execution."""
import json,pathlib,torch
from torch import nn
from safetensors.torch import load_file
try:
from transformers.initialization import no_init_weights
except ImportError:
from transformers.modeling_utils import no_init_weights
@no_init_weights()
def build_model(root,dtype=None,attention='sdpa'):
root=pathlib.Path(root);kind=root.name
if (root/'pixel_native.py').exists():
from pixel_io import PixelUMMPixelIO
model=PixelUMMPixelIO();kind='pixelumm_pixel_io'
elif (root/'sensenova').exists():
from pixel_io import SenseNovaPixelIO
model=SenseNovaPixelIO(json.loads((root/'source_config.json').read_text()));kind='sensenova_pixel_io'
elif (root/'component_config.json').exists():
from mingtok.modeling_mingtok import MingTok,MingTokConfig
cfg=json.loads((root/'component_config.json').read_text());model=MingTok(MingTokConfig(**cfg));kind='mingtok'
elif (root/'vision_encoder').exists():
from vision_encoder import UniARVisionConfig,UniARVisionModel
cfg=UniARVisionConfig.from_dict(json.loads((root/'source_config.json').read_text()));cfg._attn_implementation=attention
model=UniARVisionModel(cfg);kind='uniar_bsq'
else:
import llada_tokenizer as native
cfg=json.loads((root/'source_config.json').read_text());vc=native.make_vision_config(cfg);vc._attn_implementation=attention
model=nn.Module();model.model=nn.Module();model.model.visual=native.VisionEncoder(vc);model.model.vqmodel=native.VQVAE(native.make_vq_config(cfg));kind='llada_image_tokenizer'
if dtype is None:dtype=torch.bfloat16 if kind=='uniar_bsq' else torch.float32
return model.to(dtype=dtype).eval()
def tokenizer_view(model,root):
"""Expose upstream PIL->token API around strictly loaded LLaDA modules."""
import llada_tokenizer as native
t=native.ImageTokenizer.__new__(native.ImageTokenizer)
t.device=next(model.parameters()).device;t.dtype=next(model.parameters()).dtype
t.visual=model.model.visual;t.vqmodel=model.model.vqmodel
t.image_processor=native.ImagePreprocessor(pathlib.Path(root)/'preprocessor_config.json')
t.spatial_merge_size=t.visual.spatial_merge_size
return t
def load_model(root,dtype=None,attention='sdpa',device='cpu'):
root=pathlib.Path(root);model=build_model(root,dtype,attention)
state=load_file(str(root/'model.safetensors'),device='cpu')
model.load_state_dict(state,strict=True);del state
return model.to(device).eval()
@no_init_weights()
def load_sigvq(root,dtype=torch.float32,device='cpu'):
"""Load LLaDA's separate token-to-decoder conditioning bridge with strict state checks."""
from sigvq import SigVQ
root=pathlib.Path(root);model=SigVQ(vocab_size=16384,inner_dim=4096).to(dtype=dtype)
state=torch.load(root/'sigvq_embedding.pt',map_location='cpu',weights_only=True)
model.load_state_dict(state,strict=True);del state
return model.to(device).eval()
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