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 lora_mixin import LoraMixin
class FineTuneVisualGLMModel(VisualGLMModel):
def __init__(self, args, transformer=None, parallel_output=True, **kw_args):
super().__init__(args, transformer=transformer, parallel_output=parallel_output, **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:
# If you use lora on other "normal" Transformer, just use it with head_first=False (by default)
self.add_mixin("lora", LoraMixin(args.num_layers, args.lora_rank, head_first=True, num_attention_heads=args.num_attention_heads, hidden_size_per_attention_head=args.hidden_size // args.num_attention_heads, 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, head_first=True, num_attention_heads=args.num_attention_heads, hidden_size_per_attention_head=args.hidden_size // args.num_attention_heads, 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("<img>", add_special_tokens=False)
input1 = [tokenizer.pad_token_id] * args.image_length
input2 = tokenizer.encode("</img>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 = 'visualglm-6b'
model, args = FineTuneVisualGLMModel.from_pretrained(model_type, args)
if torch.cuda.is_available():
model = model.to('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)