为什么我的地址都改了，还会出现这样的错误：Traceback (most recent call last): File "/root/autodl-tmp/model/XrayGLM/VisualGLM-6B-main/finetune_visualglm.py", line 9, in <module> from sat.model.finetune.lora2 import LoraMixin ModuleNotFoundError: No module named 'sat.model.finetune.lora2' [2024-12-25 00:31:28,139] [INFO] [launch.py:319:sigkill_handler] Killing subprocess 1338

[zxdpro]

import os import torch import argparse

from sat import mpu, get_args, get_tokenizer from sat.training.deepspeed_training import training_main from model import VisualGLMModel from sat.model.finetune import PTuningV2Mixin from sat.model.finetune.lora2 import LoraMixin

class FineTuneVisualGLMModel(VisualGLMModel): def init(self, args, transformer=None, **kw_args): super().init(args, transformer=transformer, **kw_args) if args.use_ptuning: self.add_mixin("ptuning", PTuningV2Mixin(args.num_layers, args.hidden_size // args.num_attention_heads, args.num_attention_heads, args.pre_seq_len)) if args.use_lora: self.add_mixin("lora", LoraMixin(args.num_layers, args.lora_rank, layer_range=args.layer_range), reinit=True) # self.get_mixin("eva").model.glm_proj = replace_linear_with_lora(self.get_mixin("eva").model.glm_proj, LoraLinear, args.lora_rank) elif args.use_qlora: self.add_mixin("lora", LoraMixin(args.num_layers, args.lora_rank, layer_range=args.layer_range, qlora=True), reinit=True) self.args = args

@classmethod
def add_model_specific_args(cls, parser):
    group = parser.add_argument_group('VisualGLM-finetune', 'VisualGLM finetune Configurations')
    group.add_argument('--pre_seq_len', type=int, default=8)
    group.add_argument('--lora_rank', type=int, default=10)
    group.add_argument('--use_ptuning', action="store_true")
    group.add_argument('--use_lora', action="store_true")
    group.add_argument('--use_qlora', action="store_true")
    group.add_argument('--layer_range', nargs='+', type=int, default=None)
    return super().add_model_specific_args(parser)

def disable_untrainable_params(self):
    enable = []
    if self.args.use_ptuning:
        enable.extend(['ptuning'])
    if self.args.use_lora or self.args.use_qlora:
        enable.extend(['matrix_A', 'matrix_B'])
    for n, p in self.named_parameters():
        flag = False
        for e in enable:
            if e.lower() in n.lower():
                flag = True
                break
        if not flag:
            p.requires_grad_(False)
        else:
            print(n)

def get_batch(data_iterator, args, timers): # Items and their type. keys = ['input_ids', 'labels'] datatype = torch.int64

# Broadcast data.
timers('data loader').start()
if data_iterator is not None:
    data = next(data_iterator)
else:
    data = None
timers('data loader').stop()
data_b = mpu.broadcast_data(keys, data, datatype)
data_i = mpu.broadcast_data(['image'], data, torch.float32)
# Unpack.
tokens = data_b['input_ids'].long()
labels = data_b['labels'].long()
img = data_i['image']
if args.fp16:
    img = img.half()

return tokens, labels, img, data['pre_image']

from torch.nn import CrossEntropyLoss

def forward_step(data_iterator, model, args, timers): """Forward step."""

# Get the batch.
timers('batch generator').start()
tokens, labels, image, pre_image = get_batch(
    data_iterator, args, timers)
timers('batch generator').stop()

logits = model(input_ids=tokens, image=image, pre_image=pre_image)[0]
dtype = logits.dtype
lm_logits = logits.to(torch.float32)

# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss(ignore_index=-100)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

lm_logits = lm_logits.to(dtype)
loss = loss.to(dtype)
return loss, {'loss': loss}

from model.blip2 import BlipImageEvalProcessor from torch.utils.data import Dataset import json from PIL import Image

class FewShotDataset(Dataset): def init(self, path, processor, tokenizer, args): max_seq_length = args.max_source_length + args.max_target_length with open(path, 'r', encoding='utf-8') as f: data = json.load(f) self.images = [] self.input_ids = [] self.labels = [] for item in data: image = processor(Image.open(item['img']).convert('RGB')) input0 = tokenizer.encode("", add_special_tokens=False) input1 = [tokenizer.pad_token_id] * args.image_length input2 = tokenizer.encode("问："+item['prompt']+"\n答：", add_special_tokens=False) a_ids = sum([input0, input1, input2], []) b_ids = tokenizer.encode(text=item['label'], add_special_tokens=False) if len(a_ids) > args.max_source_length - 1: a_ids = a_ids[: args.max_source_length - 1] if len(b_ids) > args.max_target_length - 2: b_ids = b_ids[: args.max_target_length - 2] pre_image = len(input0) input_ids = tokenizer.build_inputs_with_special_tokens(a_ids, b_ids)

        context_length = input_ids.index(tokenizer.bos_token_id)
        mask_position = context_length - 1
        labels = [-100] * context_length + input_ids[mask_position+1:]
        
        pad_len = max_seq_length - len(input_ids)
        input_ids = input_ids + [tokenizer.pad_token_id] * pad_len
        labels = labels + [tokenizer.pad_token_id] * pad_len
        if args.ignore_pad_token_for_loss:
            labels = [(l if l != tokenizer.pad_token_id else -100) for l in labels]
        self.images.append(image)
        self.input_ids.append(input_ids)
        self.labels.append(labels)
    self.pre_image = pre_image

def __len__(self):
    return len(self.images)

def __getitem__(self, idx):
    return {
        "image": self.images[idx],
        "input_ids": self.input_ids[idx],
        "labels": self.labels[idx],
        "pre_image": self.pre_image
    }

def create_dataset_function(path, args): tokenizer = get_tokenizer(args) image_processor = BlipImageEvalProcessor(224)

dataset = FewShotDataset(path, image_processor, tokenizer, args)
return dataset

if name == 'main': py_parser = argparse.ArgumentParser(add_help=False) py_parser.add_argument('--max_source_length', type=int) py_parser.add_argument('--max_target_length', type=int) py_parser.add_argument('--ignore_pad_token_for_loss', type=bool, default=True) # py_parser.add_argument('--old_checkpoint', action="store_true") py_parser.add_argument('--source_prefix', type=str, default="") py_parser = FineTuneVisualGLMModel.add_model_specific_args(py_parser) known, args_list = py_parser.parse_known_args() args = get_args(args_list) args = argparse.Namespace(**vars(args), **vars(known)) args.device = 'cpu'

model_type = '/root/autodl-tmp/model/XrayGLM/VisualGLM-6B-main/visualglm-6b'
model, args = FineTuneVisualGLMModel.from_pretrained(model_type, args)
if torch.cuda.is_available():
    model = model.to('cuda')
    args.device = 'cuda'
tokenizer = get_tokenizer(args)
label_pad_token_id = -100 if args.ignore_pad_token_for_loss else tokenizer.pad_token_id
def data_collator(examples):
    for example in examples:
        example['input_ids'] = torch.tensor(example['input_ids'], dtype=torch.long)
        example['labels'] = torch.tensor(example['labels'], dtype=torch.long)
    ret = {
        'input_ids': torch.stack([example['input_ids'] for example in examples]),
        'labels': torch.stack([example['labels'] for example in examples]),
        'image': torch.stack([example['image'] for example in examples]),
        'pre_image': example['pre_image']
    }
    return ret
training_main(args, model_cls=model, forward_step_function=forward_step, create_dataset_function=create_dataset_function, collate_fn=data_collator)