Delete merge_lora_weights.py

merge_lora_weights.py

@@ -1,150 +0,0 @@
-import argparse
-import glob
-import os
-import sys
-
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-import transformers
-from peft import LoraConfig, get_peft_model
-from transformers import AutoTokenizer
-
-from model.VISA_multiseg import VrshqForCausalLM
-# from utils.utils import DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN
-
-"""
-python merge_lora_weights_and_save_hf_model.py \
-    --version   /mnt/nlp-ali/usr/yancilin/clyan-data-2/video-llm/Chat-UniVi/Chat-UniVi \
-    --weight    /mnt/public03/dataset/ovis/rgvos/visa7b/ckpt_model/pytorch_model15000.bin \
-    --save_path /mnt/public03/dataset/ovis/rgvos/visa7b/ckpt_model/hf_model
-"""
-
-DEFAULT_IM_START_TOKEN = "<im_start>"
-DEFAULT_IM_END_TOKEN = "<im_end>"
-
-
-def parse_args(args):
-    parser = argparse.ArgumentParser(description="merge lora weights and save model with hf format")
-    parser.add_argument("--version", default="chat_univi", type=str)         # path to chatunivi
-    parser.add_argument("--weight", default="/18515601223/segment-anything-2/runs/VISA-SAM2-MULTISEG-0.1/pytorch_model.bin", type=str)       # path to your checkpoints
-    parser.add_argument("--save_path", default="/18515601223/segment-anything-2/save_weights_multiseg_0.1_bf16", type=str)     
-    parser.add_argument("--precision", default="bf16", type=str, choices=["fp32", "bf16", "fp16"], help="precision for inference")
-    parser.add_argument("--out_dim", default=256, type=int)
-    parser.add_argument("--image_size", default=1024, type=int, help="image size")
-    parser.add_argument("--model_max_length", default=2048, type=int)
-    parser.add_argument("--vision_tower", default="openai/clip-vit-large-patch14", type=str)
-    parser.add_argument("--lora_r", default=8, type=int)
-    parser.add_argument("--lora_alpha", default=16, type=int)
-    parser.add_argument("--lora_dropout", default=0.05, type=float)
-    parser.add_argument("--lora_target_modules", default="q_proj,v_proj", type=str)
-    parser.add_argument("--local_rank", default=0, type=int, help="node rank")
-    parser.add_argument("--train_mask_decoder", action="store_true", default=True)
-    parser.add_argument("--use_mm_start_end", action="store_true", default=False)
-    parser.add_argument("--conv_type", default="llava_v1", type=str, choices=["llava_v1", "llava_llama_2"])
-    parser.add_argument("--alpha", default=0.1, type=float)
-    return parser.parse_args(args)
-
-
-def main(args):
-    args = parse_args(args)
-
-    # Create model
-    tokenizer = transformers.AutoTokenizer.from_pretrained(
-        pretrained_model_name_or_path=args.version,
-        cache_dir=None,
-        model_max_length=args.model_max_length,
-        padding_side="right",
-        use_fast=False,
-    )
-    tokenizer.pad_token = tokenizer.unk_token
-    num_added_tokens = tokenizer.add_tokens("[SEG]")
-    args.seg_token_idx = tokenizer("[SEG]", add_special_tokens=False).input_ids[-1]
-
-    num_added_tokens = tokenizer.add_tokens("[TAK]")
-    args.track_token_idx = tokenizer("[TAK]", add_special_tokens=False).input_ids[-1]
-
-    if args.use_mm_start_end:
-        tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
-
-    model_args = {
-        "train_mask_decoder": args.train_mask_decoder,
-        "out_dim": args.out_dim,
-        "seg_token_idx": args.seg_token_idx,
-        "vision_tower": args.vision_tower,
-        "track_token_idx": args.track_token_idx,
-        "seg_token_num": 1,
-        "alpha": args.alpha,
-    }
-
-    torch_dtype = torch.float32
-    if args.precision == "bf16":
-        torch_dtype = torch.bfloat16
-    elif args.precision == "fp16":
-        torch_dtype = torch.half
-    model = VrshqForCausalLM.from_pretrained(pretrained_model_name_or_path=args.version, torch_dtype=torch_dtype,
-                                            **model_args)
-    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
-
-    # 加载clip预训练模型
-    model.get_model().initialize_vision_modules(model.get_model().config)
-    vision_tower = model.get_model().get_vision_tower()
-    vision_tower.to(dtype=torch_dtype)
-    model.get_model().initialize_lisa_modules(model.get_model().config)
-
-    lora_r = args.lora_r
-    if lora_r > 0:
-
-        def find_linear_layers(model, lora_target_modules):
-            cls = torch.nn.Linear
-            lora_module_names = set()
-            for name, module in model.named_modules():
-                if (
-                        isinstance(module, cls)
-                        and all(
-                    [x not in name for x in ["visual_model", "vision_tower", "mm_projector", "text_hidden_fcs"]])
-                        and any([x in name for x in lora_target_modules])
-                ):
-                    lora_module_names.add(name)
-            return sorted(list(lora_module_names))
-
-        lora_alpha = args.lora_alpha
-        lora_dropout = args.lora_dropout
-        lora_target_modules = find_linear_layers(model, args.lora_target_modules.split(","), )
-        lora_config = LoraConfig(
-            r=lora_r,
-            lora_alpha=lora_alpha,
-            target_modules=lora_target_modules,
-            lora_dropout=lora_dropout,
-            bias="none",
-            task_type="CAUSAL_LM",
-        )
-        model = get_peft_model(model, lora_config)
-        model.print_trainable_parameters()
-
-    model.resize_token_embeddings(len(tokenizer))
-
-    # for key in model.state_dict().keys():
-    #     print(key)
-
-    state_dict = torch.load(args.weight, map_location="cpu")
-    model.load_state_dict(state_dict, strict=True)
-
-    model = model.merge_and_unload()
-    state_dict = {}
-    for k, v in model.state_dict().items():  # 过滤掉clip vision encoder中的参数
-        if "vision_tower" not in k:
-            state_dict[k] = v
-        if "clip_model" not in k:
-            state_dict[k] = v
-        else:
-            pass
-    model.save_pretrained(args.save_path, state_dict=state_dict)
-    tokenizer.save_pretrained(args.save_path)
-
-
-if __name__ == "__main__":
-    main(sys.argv[1:])