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peft_lora.py
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peft_lora.py
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import argparse
import gc
import json
import os
import random
import threading
import yaml
from PIL import Image
import psutil
import torch
from accelerate import Accelerator, DeepSpeedPlugin
from accelerate.utils import HfDeepSpeedConfig
from torch.utils.data import Dataset, DataLoader, random_split
from tqdm import tqdm
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
get_linear_schedule_with_warmup
)
from torch.utils.tensorboard import SummaryWriter
from peft import get_peft_model, LoraConfig, TaskType
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class ConversationDataset(Dataset):
def __init__(self,
root_dir,
tokenizer,
model,
torch_type,
device='cuda',
input_length=1024,
output_length=1024
):
self.root_dir = root_dir
self.tokenizer = tokenizer
self.model = model
self.image_dir = os.path.join(root_dir, 'images')
self.label_dir = os.path.join(root_dir,
'labels_en') # can be change to labels_en or labels_zh in SFT-311K dataset
self.filenames = os.listdir(self.image_dir)
self.input_length = input_length
self.output_length = output_length
self.device = device
self.torch_type = torch_type
self.padding_len = 2306
self.max_length = self.input_length + self.output_length + self.padding_len
def __len__(self):
return len(self.filenames)
@staticmethod
def custom_collate_fn(batch):
batched_data = {}
for key in batch[0].keys():
if isinstance(batch[0][key], list):
batched_data[key] = [batch_item[key] for batch_item in batch]
elif isinstance(batch[0][key], torch.Tensor):
batched_data[key] = torch.stack([item[key] for item in batch])
else:
raise ValueError("Unsupported datatype in custom collate_fn")
return batched_data
def __getitem__(self, idx):
img_name = os.path.join(self.image_dir, self.filenames[idx])
label_name = os.path.join(self.label_dir, self.filenames[idx].replace('.jpg', '.json'))
image = Image.open(img_name).convert('RGB')
with open(label_name, 'r') as f:
label_data = json.load(f)
num_rounds = len(label_data["conversations"]) // 2
sampled_round_id = random.randint(0, num_rounds - 1)
history = [(label_data["conversations"][(sampled_round_id - 1) * 2]["content"],
label_data["conversations"][(sampled_round_id - 1) * 2 + 1]["content"])] if (
sampled_round_id > 0 and random.random() > 0.5) else None
query = label_data["conversations"][sampled_round_id * 2]["content"]
response = label_data["conversations"][sampled_round_id * 2 + 1]["content"]
input_data = self.model.build_conversation_input_ids(
tokenizer=self.tokenizer,
query=query,
history=history,
images=[image],
answer=response
)
def pad_to_len(unpadded_tensor, pad_to_length, pad_value=0):
if len(unpadded_tensor) >= pad_to_length:
return unpadded_tensor[:pad_to_length]
return torch.cat(
(unpadded_tensor,
torch.full([pad_to_length - len(unpadded_tensor)],
fill_value=pad_value,
dtype=unpadded_tensor.dtype,
device=unpadded_tensor.device)), dim=0)
input_data['input_ids'] = pad_to_len(
input_data['input_ids'],
self.max_length,
pad_value=128002,
)
input_data['attention_mask'] = pad_to_len(
input_data['attention_mask'],
self.max_length,
pad_value=0
)
input_data['token_type_ids'] = pad_to_len(
input_data['token_type_ids'],
self.max_length,
pad_value=0
)
input_data['labels'] = pad_to_len(
input_data['labels'],
self.max_length,
pad_value=-100
)
for data_key in input_data:
if data_key in ['images']:
input_data[data_key] = [data.to(self.device).to(self.torch_type) for data in
input_data[data_key]]
else:
input_data[data_key] = input_data[data_key].to(self.device)
return input_data
def b2mb(x):
return int(x / 2 ** 20)
class TorchTracemalloc:
def __enter__(self):
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
self.begin = torch.cuda.memory_allocated()
self.process = psutil.Process()
self.cpu_begin = self.cpu_mem_used()
self.peak_monitoring = True
peak_monitor_thread = threading.Thread(target=self.peak_monitor_func)
peak_monitor_thread.daemon = True
peak_monitor_thread.start()
return self
def cpu_mem_used(self):
return self.process.memory_info().rss
def peak_monitor_func(self):
self.cpu_peak = -1
while True:
self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak)
if not self.peak_monitoring:
break
def __exit__(self, *exc):
self.peak_monitoring = False
gc.collect()
torch.cuda.empty_cache()
self.end = torch.cuda.memory_allocated()
self.peak = torch.cuda.max_memory_allocated()
self.used = b2mb(self.end - self.begin)
self.peaked = b2mb(self.peak - self.begin)
self.cpu_end = self.cpu_mem_used()
self.cpu_used = b2mb(self.cpu_end - self.cpu_begin)
self.cpu_peaked = b2mb(self.cpu_peak - self.cpu_begin)
def main():
parser = argparse.ArgumentParser(description="Finetune a CogVLM model with LoRA")
parser.add_argument("--lr", type=float, default=1e-7, help="Learning rate")
parser.add_argument("--num_epochs", type=int, default=5, help="Number of epochs")
parser.add_argument("--batch_size", type=int, default=2, help="Batch size")
parser.add_argument("--torch_type", type=str, default="torch.bfloat16", help="Torch type")
parser.add_argument("--save_step", type=int, default=100, help="Steps between checkpoints")
parser.add_argument("--train_dataset_rate", type=float, default=0.8,
help="Proportion of dataset to use for training")
parser.add_argument("--local_rank", type=int, default=-1, help="Local rank for distributed training")
parser.add_argument("--lora_rank", type=int, default=8, help="Rank parameter for LoRA")
parser.add_argument("--lora_alpha", type=int, default=32, help="Alpha parameter for LoRA")
parser.add_argument("--lora_target", type=str, default=["lm_head"],
help="Finetune Target for LoRA")
parser.add_argument("--lora_dropout", type=float, default=0.1, help="Dropout rate for LoRA")
parser.add_argument("--warmup_steps", type=int, default=0,
help="Number of warmup steps for learning rate scheduler")
parser.add_argument("--max_input_len", type=int, default=1024, help="Maximum input length")
parser.add_argument("--max_output_len", type=int, default=1024, help="Maximum output length")
parser.add_argument("--model_path", type=str,
default="THUDM/cogvlm2-llama3-chat-19B",
help="Path to the pretrained model")
parser.add_argument("--dataset_path", type=str,
default="CogVLM-SFT-311K/llava_instruction_multi_conversations_formate",
help="Path to the conversation dataset")
parser.add_argument("--save_path", type=str, default="output",
help="Path to save the finetuned model, must be a exit directory")
parser.add_argument("--ds_config", type=str, default="ds_config.yaml",
help="DeepSpeed configuration file path")
args = parser.parse_args()
args.torch_type = eval(args.torch_type)
with open(args.ds_config) as f:
ds_config = yaml.safe_load(f)
hf_ds_config = HfDeepSpeedConfig(ds_config)
ds_plugin = DeepSpeedPlugin(hf_ds_config=hf_ds_config)
accelerator = Accelerator(deepspeed_plugin=ds_plugin)
zero_stage = ds_plugin.hf_ds_config.config['zero_optimization']['stage']
is_ds_zero_3 = zero_stage == 3
tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(args.model_path, torch_dtype=args.torch_type, trust_remote_code=True)
if len(tokenizer) != model.get_input_embeddings().weight.size(0):
model.resize_token_embeddings(len(tokenizer))
dataset = ConversationDataset(
root_dir=args.dataset_path,
tokenizer=tokenizer,
model=model,
torch_type=args.torch_type,
input_length=args.max_input_len,
output_length=args.max_output_len
)
train_size = int(args.train_dataset_rate * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=dataset.custom_collate_fn,
)
eval_dataloader = DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=dataset.custom_collate_fn,
)
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=False,
r=args.lora_rank,
target_modules=args.lora_target,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
bias="none",
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr)
lr_scheduler = get_linear_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=(len(train_dataloader) * args.num_epochs),
)
model, train_dataloader, eval_dataloader, optimizer, lr_scheduler = accelerator.prepare(
model, train_dataloader, eval_dataloader, optimizer, lr_scheduler
)
logger.info("Preparation done. Starting training...")
writer = SummaryWriter(log_dir=args.save_path) # TensorBoard writer
for epoch in range(args.num_epochs):
with TorchTracemalloc() as tracemalloc:
model.train()
total_loss = 0
for step, batch in enumerate(tqdm(train_dataloader)):
outputs = model(
input_ids=batch['input_ids'],
token_type_ids=batch['token_type_ids'],
attention_mask=batch['attention_mask'],
images=batch['images'],
labels=batch['labels']
)
loss = outputs.loss
total_loss += loss.detach().float()
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
if (step + 1) % args.save_step == 0:
print(f"Epoch {epoch}, Step {step + 1}, Loss {loss.item()}")
checkpoint_path = os.path.join(args.save_path, f'checkpoint_epoch_{epoch}_step_{step + 1}')
model.save_pretrained(
save_directory=checkpoint_path,
safe_serialization=True,
save_embedding_layers=True,
)
writer.add_scalar('Train/Loss', loss.item(), epoch * len(train_dataloader) + step)
accelerator.print(f"GPU Memory before entering the train : {b2mb(tracemalloc.begin)}")
accelerator.print(f"GPU Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
accelerator.print(f"GPU Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
accelerator.print(
f"GPU Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
train_epoch_loss = total_loss / len(train_dataloader)
train_ppl = torch.exp(train_epoch_loss)
accelerator.print(f"{epoch=}: {train_ppl=} {train_epoch_loss=}")
writer.add_scalar('Train/Perplexity', train_ppl, epoch)
writer.add_scalar('Train/Epoch_Loss', train_epoch_loss, epoch)
model.eval()
eval_preds = []
with TorchTracemalloc() as tracemalloc:
for _, batch in enumerate(tqdm(eval_dataloader)):
batch = {k: v for k, v in batch.items() if k != "labels"}
with torch.no_grad():
outputs = accelerator.unwrap_model(model).generate(
**batch, synced_gpus=is_ds_zero_3, max_new_tokens=args.max_output_len
)
outputs = accelerator.pad_across_processes(outputs, dim=1, pad_index=tokenizer.pad_token_id)
preds = accelerator.gather_for_metrics(outputs)
preds = preds[:, args.max_input_len + args.max_output_len:].detach().cpu().numpy()
eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True, padding_size="left"))
accelerator.print(f"GPU Memory before entering the eval : {b2mb(tracemalloc.begin)}")
accelerator.print(f"GPU Memory consumed at the end of the eval (end-begin): {tracemalloc.used}")
accelerator.print(f"GPU Peak Memory consumed during the eval (max-begin): {tracemalloc.peaked}")
accelerator.print(
f"GPU Total Peak Memory consumed during the eval (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the eval : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the eval (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the eval (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
f"CPU Total Peak Memory consumed during the eval (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
writer.add_scalar('Eval/Perplexity', torch.exp(train_epoch_loss), epoch)
writer.add_scalar('Eval/Epoch_Loss', train_epoch_loss, epoch)
checkpoint_path = os.path.join(args.save_path, 'final_model')
model.save_pretrained(
save_directory=checkpoint_path,
safe_serialization=True,
save_embedding_layers=True
)
accelerator.wait_for_everyone()
writer.close()
if __name__ == "__main__":
main()