Description
运行脚本就是官方给出的示例脚本
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
from swift.llm import (
get_model_tokenizer, load_dataset, get_template, EncodePreprocessor, get_model_arch,
get_multimodal_target_regex, LazyLLMDataset
)
from swift.utils import get_logger, get_model_parameter_info, plot_images, seed_everything
from swift.tuners import Swift, LoraConfig
from swift.trainers import Seq2SeqTrainer, Seq2SeqTrainingArguments
from functools import partial
logger = get_logger()
seed_everything(42)
Hyperparameters for training
model
model_id_or_path = '/home/jdn/.cache/modelscope/hub/models/deepseek-ai/deepseek-vl2-tiny'
system = None # Using the default system defined in the template.
output_dir = '/home/jdn/deepseek/output'
dataset
dataset = '/home/jdn/train_CT_and_Xray_last_500.json' # dataset_id or dataset_path. Sampling 20000 data points
data_seed = 42
max_length = 2048
split_dataset_ratio = 0.01 # Split validation set
num_proc = 4 # The number of processes for data loading.
lora
lora_rank = 8
lora_alpha = 32
freeze_llm = False
freeze_vit = True
freeze_aligner = True
training_args
training_args = Seq2SeqTrainingArguments(
output_dir=output_dir,
learning_rate=1e-4,
per_device_train_batch_size=1,
per_device_eval_batch_size=1,
gradient_checkpointing=True,
weight_decay=0.1,
lr_scheduler_type='cosine',
warmup_ratio=0.05,
report_to=['tensorboard'],
logging_first_step=True,
save_strategy='steps',
save_steps=50,
eval_strategy='steps',
eval_steps=50,
gradient_accumulation_steps=16,
# To observe the training results more quickly, this is set to 1 here.
# Under normal circumstances, a larger number should be used.
num_train_epochs=1,
metric_for_best_model='loss',
save_total_limit=5,
logging_steps=5,
dataloader_num_workers=4,
data_seed=data_seed,
remove_unused_columns=False,
)
output_dir = os.path.abspath(os.path.expanduser(output_dir))
logger.info(f'output_dir: {output_dir}')
Obtain the model and template
model, processor = get_model_tokenizer(model_id_or_path)
#model.half()#jdn修改
logger.info(f'model_info: {model.model_info}')
template = get_template(model.model_meta.template, processor, default_system=system, max_length=max_length)
template.set_mode('train')
if template.use_model:
template.model = model
Get target_modules and add trainable LoRA modules to the model.
target_modules = get_multimodal_target_regex(model, freeze_llm=freeze_llm, freeze_vit=freeze_vit,
freeze_aligner=freeze_aligner)
lora_config = LoraConfig(task_type='CAUSAL_LM', r=lora_rank, lora_alpha=lora_alpha,
target_modules=target_modules)
model = Swift.prepare_model(model, lora_config)
logger.info(f'lora_config: {lora_config}')
Print model structure and trainable parameters.
logger.info(f'model: {model}')
model_parameter_info = get_model_parameter_info(model)
logger.info(f'model_parameter_info: {model_parameter_info}')
Download and load the dataset, split it into a training set and a validation set,
and encode the text data into tokens.
train_dataset, val_dataset = load_dataset(dataset, split_dataset_ratio=split_dataset_ratio, num_proc=num_proc,
seed=data_seed)
logger.info(f'train_dataset: {train_dataset}')
logger.info(f'val_dataset: {val_dataset}')
logger.info(f'train_dataset[0]: {train_dataset[0]}')
train_dataset = LazyLLMDataset(train_dataset, template.encode, random_state=data_seed)
val_dataset = LazyLLMDataset(val_dataset, template.encode, random_state=data_seed)
data = train_dataset[0]
logger.info(f'encoded_train_dataset[0]: {data}')
template.print_inputs(data)
Get the trainer and start the training.
model.enable_input_require_grads() # Compatible with gradient checkpointing
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
data_collator=template.data_collator,
train_dataset=train_dataset,
eval_dataset=val_dataset,
template=template,
)
trainer.train()
last_model_checkpoint = trainer.state.last_model_checkpoint
logger.info(f'last_model_checkpoint: {last_model_checkpoint}')
Visualize the training loss.
You can also use the TensorBoard visualization interface during training by entering
tensorboard --logdir '{output_dir}/runs' at the command line.
images_dir = os.path.join(output_dir, 'images')
logger.info(f'images_dir: {images_dir}')
plot_images(images_dir, training_args.logging_dir, ['train/loss'], 0.9) # save images
Read and display the image.
The light yellow line represents the actual loss value,
while the yellow line represents the loss value smoothed with a smoothing factor of 0.9.
from IPython.display import display
from PIL import Image
image = Image.open(os.path.join(images_dir, 'train_loss.png'))
display(image)
报错为
请问如何修改代码能讲bf16 切换为float16