Witllm/wit/stress.py

83 lines
2.2 KiB
Python

import pytorch_lightning as pl
import torch
from torch.utils.data import DataLoader, Dataset, random_split
from lit_module import LitModule
from logger import TBLogger
from wit.configuration import ModelConfig
pretrain_model_name = None # "qwen/Qwen-1_8B-Chat"
learning_rate = 0.0001
use_tril_attention_mask = None
precision = "32-true" # "precision:bf16-mixed,16-mixed,32-true"
train_batch_size = 4
val_batch_size = 8
num_proc = 8
max_epochs = 1000
strategy = "auto"
resume_from_ckpt_path = None
seed = 42
class StressDataset(Dataset):
def __init__(self, start=1, end=128, size=32768): # 1048576 32768
self.size = size
self.features = []
self.data = torch.randint(start, end, [size, 2048]).long()
def __len__(self):
return self.size
def __getitem__(self, idx):
output = {}
data = self.data[idx]
output["input_ids"] = data
output["labels"] = data.clone()
output["token_type_ids"] = torch.zeros(data.shape)
return output
if __name__ == "__main__":
torch.manual_seed(seed)
config = ModelConfig()
config.vocab_size = 4096
config.hidden_size = 1024 # 128 1024 2048 32
config.num_hidden_layers = 6 # 6 12 24 3
config.num_attention_heads = 8 # 8 8 16
lit_module = LitModule(pretrain_model_name, learning_rate, config, use_tril_attention_mask)
raw_dataset = StressDataset()
train_dataset, val_dataset = random_split(raw_dataset, [0.95, 0.05])
train_dataloader = DataLoader(
train_dataset,
batch_size=train_batch_size,
num_workers=num_proc,
persistent_workers=True,
shuffle=True,
)
val_dataloader = DataLoader(
val_dataset,
batch_size=val_batch_size,
num_workers=num_proc,
persistent_workers=True,
)
lit_trainer = pl.Trainer(
accelerator="gpu",
devices=2,
precision=precision,
logger=TBLogger("./", default_hp_metric=False),
strategy=strategy,
max_epochs=max_epochs,
)
lit_trainer.fit(
lit_module,
train_dataloaders=train_dataloader,
val_dataloaders=val_dataloader,
ckpt_path=resume_from_ckpt_path,
)