train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=TrainGlobalConfig.batch_size, sampler=RandomSampler(train_dataset), pin_memory=False, drop_last=True, num_workers=TrainGlobalConfig.num_workers, collate_fn=collate_fn, ) val_loader = torch.utils.data.DataLoader( validation_dat...
self.sampler =torch.utils.data.RandomSampler if shuffle else \ torch.utils.data.SequentialSampler self.batch_sampler = torch.utils.data.BatchSampler self.sample_iter = self.batch_sampler( self.sampler(range(len(dataset))), batch_size = batch_size,drop_last = drop_last) def __next__(self)...
背后,这会将先 shuffle、packing、预处理好的数据进一步做好序列并行的准备:先将每行 pad 或截断到指定的训练长度,再按 zigzag 切分并按顺序写入数据集,最后在训练时用 SequentialSampler 读取训练数据。 # src/llamafactory/data/loader.py@sequence_parallel_decoratordef get_dataset(...) loss 计算则需要在 Tra...
背后,这会将先 shuffle、packing、预处理好的数据进一步做好序列并行的准备:先将每行 pad 或截断到指定的训练长度,再按 zigzag 切分并按顺序写入数据集,最后在训练时用 SequentialSampler 读取训练数据。 # src/llamafactory/data/loader.py @sequence_parallel_decorator def get_dataset(...) loss 计算则需要在 ...
Set up a dataset ^^^ .. TODO: Update this to use Ray Data. Expand All @@ -182,8 +182,8 @@ Use the :func:`ray.train.torch.prepare_data_loader` utility function, which: 1. Adds a ``DistributedSampler`` to your ``DataLoader``. 2. Moves the batches to the right device. Note...
assert train_dataset if not params.random_sample: sampler = dataset.randomSequentialSampler(train_dataset, params.batchSize) else: sampler = None train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=params.batchSize, \ shuffle=True, sampler=sampler, num_workers=int(params.workers)...
接下来构造dataset数据集 train_ds=PretrainDataset(data_path_list,max_length=max_seq_len,memmap=True)train_sampler=torch.utils.data.distributed.DistributedSampler(train_ds) 2.PretrainDataset的调用 进入PretrainDataset进行查看 classPretrainDataset(Dataset):def__init__(self,data_path_lst,max_length=256...
背后,这会将先 shuffle、packing、预处理好的数据进一步做好序列并行的准备:先将每行 pad 或截断到指定的训练长度,再按 zigzag 切分并按顺序写入数据集,最后在训练时用 SequentialSampler 读取训练数据。 复制 # src/llamafactory/data/loader.py @sequence_parallel_decorator...
背后,这会将先 shuffle、packing、预处理好的数据进一步做好序列并行的准备:先将每行 pad 或截断到指定的训练长度,再按 zigzag 切分并按顺序写入数据集,最后在训练时用 SequentialSampler 读取训练数据。 # src/llamafactory/data/loader.py @sequence_parallel_decorator...
train_sampler = BucketingSampler(train_dataset, batch_size=args.batch_size) else: train_sampler = DistributedBucketingSampler(train_dataset, batch_size=args.batch_size, num_replicas=args.world_size, rank=args.rank) train_loader = AudioDataLoader(train_dataset, num_workers=args.num_workers...