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Add custom dataset support.

This commit is contained in:
Colin 2024-02-26 00:31:47 +08:00
parent e5f97af291
commit 1ef3e419cb
1 changed files with 31 additions and 12 deletions
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43
wit/lit_train.py
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@ -6,7 +6,7 @@ from typing import Dict, Tuple
import datasets import datasets
import pytorch_lightning as pl import pytorch_lightning as pl
import torch import torch
from torch.utils.data import ConcatDataset, DataLoader from torch.utils.data import ConcatDataset, DataLoader, Dataset
from transformers import ( from transformers import (
BatchEncoding, BatchEncoding,
DefaultDataCollator, DefaultDataCollator,
@ -22,9 +22,9 @@ learning_rate = 0.0001
use_tril_attention_mask = None use_tril_attention_mask = None
precision = "16-mixed" # "precision:bf16-mixed,16-mixed,32-true" precision = "16-mixed" # "precision:bf16-mixed,16-mixed,32-true"
tokenizer_name_or_path = None tokenizer_name_or_path = None
dataset_name = "/home/colin/develop/dataset/liwu/MNBVC/wiki/20230197/0.jsonl.gz" dataset_name = ["/home/colin/develop/dataset/liwu/MNBVC/wiki"]
dataset_name = "/home/colin/develop/dataset/liwu/MNBVC/wiki" dataset_name = ["/home/colin/develop/dataset/liwu/MNBVC/wiki/20230198/58.jsonl.gz"]
train_batch_size = 8 train_batch_size = 1
val_batch_size = 1 val_batch_size = 1
accumulate_grad_batches = 32 accumulate_grad_batches = 32
num_proc = 8 num_proc = 8
@ -34,6 +34,22 @@ resume_from_ckpt_path = None
seed = 42 seed = 42
class SpecialDataset(Dataset):
def __init__(self, size=4096):
self.size = size
self.features = []
def __len__(self):
return self.size
def __getitem__(self, idx):
output = {}
output["input_ids"] = torch.randint(0, 4096, [128])
output["labels"] = output["input_ids"]
output["token_type_ids"] = torch.zeros([128])
return output
def split_raw_dataset( def split_raw_dataset(
raw_dataset: datasets.DatasetDict, raw_dataset: datasets.DatasetDict,
) -> Tuple[datasets.Dataset, datasets.Dataset]: ) -> Tuple[datasets.Dataset, datasets.Dataset]:
@ -106,17 +122,17 @@ if __name__ == "__main__":
model_dir = snapshot_download(model_name) model_dir = snapshot_download(model_name)
lit_module = LitModule(model_dir, learning_rate, use_tril_attention_mask) lit_module = LitModule(model_dir, learning_rate, use_tril_attention_mask)
# datasets
# tokenizer = load_tokenizer("./custom_models/gpt2")
tokenizer = QWenTokenizer("./wit_b64.tiktoken", "./wit_char.tiktoken") tokenizer = QWenTokenizer("./wit_b64.tiktoken", "./wit_char.tiktoken")
train_dataset_list = [] train_dataset_list = []
val_dataset_list = [] val_dataset_list = []
for dataset_name in dataset_name: for dn in dataset_name:
dataset_args = dataset_name.split(":") datanames = dn.split(".")
raw_dataset = datasets.load_dataset( if datanames[-1] == "gz" and datanames[-2] == "jsonl":
"json", data_files="/home/colin/develop/dataset/liwu/MNBVC/wiki/20230197/0.jsonl.gz" raw_dataset = datasets.load_dataset("json", data_files=dn)
) elif datanames[-1] == "json":
# raw_dataset = datasets.load_dataset(*dataset_args) raw_dataset = datasets.load_dataset("json", data_files=dn)
else:
raw_dataset = datasets.load_dataset(dn)
train_dataset, val_dataset = split_raw_dataset(raw_dataset) train_dataset, val_dataset = split_raw_dataset(raw_dataset)
train_dataset = process_dataset(train_dataset, tokenizer) train_dataset = process_dataset(train_dataset, tokenizer)
val_dataset = process_dataset(val_dataset, tokenizer) val_dataset = process_dataset(val_dataset, tokenizer)
@ -125,6 +141,9 @@ if __name__ == "__main__":
train_dataset = ConcatDataset(train_dataset_list) train_dataset = ConcatDataset(train_dataset_list)
val_dataset = ConcatDataset(val_dataset_list) val_dataset = ConcatDataset(val_dataset_list)
train_dataset = SpecialDataset()
val_dataset = SpecialDataset()
# dataloaders # dataloaders
train_dataloader = DataLoader( train_dataloader = DataLoader(
train_dataset, train_dataset,