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91c1e9e 06b25e4 91c1e9e 06b25e4 91c1e9e 06b25e4 91c1e9e 06b25e4 91c1e9e | 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 | """Shared PRIMA model construction for inference and benchmark evaluation."""
import torch
import transformers
from pathlib import Path
from peft import LoraConfig, get_peft_model
from model.PRIMA import PRIMAForCausalLM
from tools.checkpoint import load_checkpoint
from tools.config import validate_paths
DEFAULT_HUB_MODEL = 'PLAN-Lab/PRIMA'
MODEL_DEFAULTS = {
'merged_ckpt_path': '', 'hub_revision': 'main',
'version': '/path/to/base_model', 'ckpt_path': '/path/to/prima_weights',
'q_former_model': '/path/to/qformer_weights', 'pretrained_q_former_path': '',
'qformer_bert_path': '/path/to/bert_model', 'eva_weights': '/path/to/eva_weights',
'pretrain_mm_mlp_adapter': '', 'precision': 'bf16',
'image_size': 1024, 'vit_image_size': 224, 'model_max_length': 3400,
'max_num_images': 5, 'num_query_tokens': 32, 'lora_r': 8,
'lora_alpha': 16, 'lora_dropout': 0.05,
'lora_target_modules': 'q_proj,v_proj', 'conv_type': 'llava_v1',
'local_rank': 0,
}
INTERNAL_DEFAULTS = {'use_mm_start_end': True, 'img_emb_len': None}
def add_model_arguments(parser, *, merged_checkpoint=''):
for key, value in MODEL_DEFAULTS.items():
parser.add_argument('--' + key, default=value, type=type(value))
parser.set_defaults(merged_ckpt_path=merged_checkpoint, **INTERNAL_DEFAULTS)
def normalize_model_args(args):
for key, value in {**MODEL_DEFAULTS, **INTERNAL_DEFAULTS}.items():
if not hasattr(args, key):
setattr(args, key, value)
if args.precision != 'bf16':
raise ValueError('The release currently supports the validated bf16 execution path.')
if not args.pretrained_q_former_path or args.pretrained_q_former_path.startswith('/path/to/'):
args.pretrained_q_former_path = args.q_former_model
return args
def setup_tokenizer_and_special_tokens(args):
"""Load tokenizer and add special tokens."""
source = resolve_merged_checkpoint(args) if args.merged_ckpt_path else args.version
tokenizer = transformers.AutoTokenizer.from_pretrained(
source,
model_max_length=args.model_max_length,
padding_side="left",
use_fast=False,
)
tokenizer.pad_token = tokenizer.unk_token
args.seg_token_idx = tokenizer.convert_tokens_to_ids("[SEG]")
if args.seg_token_idx == tokenizer.unk_token_id:
raise ValueError("The base tokenizer must contain the [SEG] special token.")
args.img_emb_len = args.num_query_tokens
max_images = args.model_max_length // args.img_emb_len
max_images = min(max_images, args.max_num_images)
args.seg_image_tokens = []
for i in range(max_images):
args.seg_image_tokens.append(
tokenizer(f"[SEG] (IMAGE{i + 1}", add_special_tokens=False).input_ids
)
args.seg_image_tokens = [pattern[pattern.index(args.seg_token_idx):] for pattern in args.seg_image_tokens]
return tokenizer
def setup_qformer_tokenizer(args):
source = (Path(resolve_merged_checkpoint(args)) / 'qformer_tokenizer'
if args.merged_ckpt_path else args.qformer_bert_path)
qformer_tokenizer = transformers.BertTokenizer.from_pretrained(
source, truncation_side="left"
)
qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"})
return qformer_tokenizer
def initialize_model(args, tokenizer):
"""Initialize the PRIMA model."""
model_args = {
k: getattr(args, k)
for k in [
"seg_token_idx",
"seg_image_tokens",
"max_num_images",
"pretrain_mm_mlp_adapter",
]
}
model_args["img_emb_len"] = args.img_emb_len
for name in ('ce_loss_weight', 'dice_loss_weight', 'bce_loss_weight', 'train_mask_decoder'):
if hasattr(args, name):
model_args[name] = getattr(args, name)
source = resolve_merged_checkpoint(args) if args.merged_ckpt_path else args.version
model = PRIMAForCausalLM.from_pretrained(
source, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, **model_args
)
# Configure model tokens
model.config.eos_token_id = tokenizer.eos_token_id
model.config.bos_token_id = tokenizer.bos_token_id
model.config.pad_token_id = tokenizer.pad_token_id
if args.merged_ckpt_path:
if not getattr(model.config, 'self_contained', False):
raise ValueError('merged_ckpt_path must point to a complete PRIMA checkpoint.')
else:
model.config.eva_weights = args.eva_weights
model.config.qformer_bert_path = args.qformer_bert_path
return model
def prepare_model_for_inference(model, args, qformer_tokenizer):
if args.merged_ckpt_path:
if args.ckpt_path and not args.ckpt_path.startswith('/path/to/'):
return get_peft_model(model, setup_lora_config(model, args))
return model
# Initialize vision tower
model.get_model().initialize_qformer(model.get_model().config, pretrained_q_former_path=args.pretrained_q_former_path, vit_image_size=args.vit_image_size, precision=args.precision, q_former_model=args.q_former_model, num_query_tokens=args.num_query_tokens, qformer_tokenizer=qformer_tokenizer, pretrain_mm_mlp_adapter=args.pretrain_mm_mlp_adapter)
model.get_model().Qformer.to(dtype=torch.bfloat16, device=args.local_rank)
model.get_model().mm_projector.to(dtype=torch.bfloat16, device=args.local_rank)
model.get_model().visual_encoder.to(dtype=torch.bfloat16, device=args.local_rank)
model.get_model().ln_vision.to(dtype=torch.bfloat16, device=args.local_rank)
model.get_model().query_tokens.to(dtype=torch.bfloat16, device=args.local_rank)
if args.lora_r > 0:
lora_config = setup_lora_config(model, args)
model = get_peft_model(model, lora_config)
return model
def setup_lora_config(model, args):
"""Configure LoRA settings for the model."""
def find_proj_layers(model, target_modules):
"""Identify projection layers in the model for LoRA adaptation."""
linear_cls = torch.nn.Linear
lora_module_names = set()
for name, module in model.named_modules():
if (
isinstance(module, linear_cls)
and all(
x not in name
for x in [
"grounding_encoder",
"vision_tower",
"mm_projector",
"text_hidden_fcs",
"visual_encoder",
"ln_vision",
"Qformer",
"query_tokens",
]
)
and any(x in name for x in target_modules)
):
lora_module_names.add(name)
return sorted(list(lora_module_names))
# Extracting LoRA target modules
lora_target_modules = args.lora_target_modules.split(",")
lora_module_names = find_proj_layers(model, lora_target_modules)
# Configuring LoRA
lora_config = LoraConfig(
r=args.lora_r,
lora_alpha=args.lora_alpha,
target_modules=lora_module_names,
lora_dropout=args.lora_dropout,
bias="none",
task_type="CAUSAL_LM",
)
return lora_config
def load_model_from_checkpoint(model, args):
overlay = args.ckpt_path and not args.ckpt_path.startswith('/path/to/')
report = load_checkpoint(model, args.ckpt_path) if overlay or not args.merged_ckpt_path else {}
model.to(device=torch.device(f'cuda:{args.local_rank}'), dtype=torch.bfloat16)
return report
def resolve_merged_checkpoint(args):
"""Resolve a local complete model or a pinned Hub snapshot once per command."""
if not getattr(args, '_merged_snapshot', None):
source = Path(args.merged_ckpt_path).expanduser()
if source.is_dir():
args._merged_snapshot = str(source.resolve())
else:
from huggingface_hub import snapshot_download
args._merged_snapshot = snapshot_download(
args.merged_ckpt_path, revision=args.hub_revision)
return args._merged_snapshot
def load_bundle(args):
normalize_model_args(args)
validate_paths(args)
tokenizer = setup_tokenizer_and_special_tokens(args)
qformer_tokenizer = setup_qformer_tokenizer(args)
model = initialize_model(args, tokenizer)
model = prepare_model_for_inference(model, args, qformer_tokenizer)
load_model_from_checkpoint(model, args)
return model.eval(), tokenizer, qformer_tokenizer
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