When I interleave data sets, get a tokenized batch, feed the batch to the pytorch data loader, I get errors:
# -*- coding: utf-8 -*-
"""issues with dataloader and custom data sets
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1sbs95as_66mtK9VK_vbaE9gLE-Tjof1-
"""
!pip install datasets
!pip install pytorch
!pip install transformers
token = None
batch_size = 10
from datasets import load_dataset
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
probe_network = GPT2LMHeadModel.from_pretrained("gpt2")
device = torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu")
probe_network = probe_network.to(device)
# -- Get batch from dataset
from datasets import load_dataset
# path, name = 'brando/debug1_af', 'debug1_af'
path, name = 'brando/debug0_af', 'debug0_af'
remove_columns = []
dataset = load_dataset(path, name, streaming=True, split="train", token=token).with_format("torch")
print(f'{dataset=}')
batch = dataset.take(batch_size)
# print(f'{next(iter(batch))=}')
# - Prepare functions to tokenize batch
def preprocess(examples): # gets the raw text batch according to the specific names in table in data set & tokenize
return tokenizer(examples["link"], padding="max_length", max_length=128, truncation=True, return_tensors="pt")
def map(batch): # apply preprocess to batch to all examples in batch represented as a dataset
return batch.map(preprocess, batched=True, remove_columns=remove_columns)
tokenized_batch = batch.map(preprocess, batched=True, remove_columns=remove_columns)
tokenized_batch = map(batch)
# print(f'{next(iter(tokenized_batch))=}')
from torch.utils.data import Dataset, DataLoader, SequentialSampler
dataset = tokenized_batch
print(f'{type(dataset)=}')
print(f'{dataset.__class__=}')
print(f'{isinstance(dataset, Dataset)=}')
# for i, d in enumerate(dataset):
# assert isinstance(d, dict)
# # dd = dataset[i]
# # assert isinstance(dd, dict)
loader_opts = {}
classifier_opts = {}
# data_loader = DataLoader(dataset, shuffle=False, batch_size=loader_opts.get('batch_size', 1),
# num_workers=loader_opts.get('num_workers', 0), drop_last=False, sampler=SequentialSampler(range(512)) )
data_loader = DataLoader(dataset, shuffle=False, batch_size=loader_opts.get('batch_size', 1),
num_workers=loader_opts.get('num_workers', 0), drop_last=False, sampler=None)
print(f'{iter(data_loader)=}')
print(f'{next(iter(data_loader))=}')
print('Done\a')
with error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/collate.py in collate(batch, collate_fn_map)
126 try:
--> 127 return elem_type({key: collate([d[key] for d in batch], collate_fn_map=collate_fn_map) for key in elem})
128 except TypeError:
9 frames
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'NoneType'>
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/collate.py in collate(batch, collate_fn_map)
148 return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed]
149
--> 150 raise TypeError(default_collate_err_msg_format.format(elem_type))
151
152
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'NoneType'>
why? And why doesn't the single data set c4 and wiki-text give this error? Only interleaved data sets?
Ideally I don't want to write my own collate_function.
For some reason when the data sets are intersected the collate function gets confused because there are extra rows so it doesn't know how to merge things? The way I fixed it is by only keeping the columns I want:
# -- Get data set
# remove_columns = ['text', 'timestamp', 'url']
keep_col = ['text']
# keep the strings in dataaset.column_names that intersect with keep_col str list, one liner
print('-- interleaving datasets')
datasets = [load_dataset(path, name, streaming=True, split="train").with_format("torch") for path, name in zip(path, name)]
[print(f'{dataset.description=}') for dataset in datasets]
dataset = interleave_datasets(datasets, probabilities)
remove_columns = [col for col in dataset.column_names if col not in keep_col]
print(f'{dataset=}')
batch = dataset.take(batch_size)
but also doing this in the collate works if you know the text field you want (assuming "text" due to how common it is):
def collate_tokenize(data):
print(f'{data[0]=}')
text_batch = [element["text"] for element in data]
tokenized = tokenizer(text_batch, padding='longest', truncation=True, return_tensors='pt')
return tokenized
data_loader = DataLoader(tokenized_batch, shuffle=False, batch_size=8, num_workers=0, drop_last=False, collate_fn=collate_tokenize)
# data_loader = DataLoader(tokenized_batch, shuffle=False, batch_size=8, num_workers=0, drop_last=False)
# num_batches = len(list(data_loader))
batch = next(iter(data_loader))
print(f'{batch=}')
print('Done!\a')
full code:
def test_interleaved_data_set_2_data_loader():
""" https://colab.research.google.com/drive/1QWDhA6Q64qijXYnwIGn63Aq9Eg5qt8tQ#scrollTo=Wjyy6QYimvIm """
remove_columns = []
# -- Get probe network
from datasets import load_dataset
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
probe_network = GPT2LMHeadModel.from_pretrained("gpt2")
device = torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu")
probe_network = probe_network.to(device)
from datasets import interleave_datasets
path, name = ['c4', 'wikitext'], ['en', 'wikitext-103-v1']
probabilities = [1.0/len(path)] * len(path)
batch_size = 512
# -- Get data set
# remove_columns = ['text', 'timestamp', 'url']
keep_col = ['text']
# keep the strings in dataaset.column_names that intersect with keep_col str list, one liner
print('-- interleaving datasets')
datasets = [load_dataset(path, name, streaming=True, split="train").with_format("torch") for path, name in zip(path, name)]
[print(f'{dataset.description=}') for dataset in datasets]
dataset = interleave_datasets(datasets, probabilities)
remove_columns = [col for col in dataset.column_names if col not in keep_col]
print(f'{dataset=}')
batch = dataset.take(batch_size)
# - Prepare functions to tokenize batch
def preprocess(examples):
return tokenizer(examples["text"], padding="max_length", max_length=128, truncation=True, return_tensors="pt")
def map(batch):
return batch.map(preprocess, batched=True, remove_columns=remove_columns)
# tokenized_batch = batch.map(preprocess, batched=True, remove_columns=remove_columns)
tokenized_batch = map(batch)
print(f'{next(iter(tokenized_batch))=}')
# -- Get data loader
from torch.utils.data import DataLoader, Dataset
# def collate_tokenize(data):
# print(f'{data[0]=}')
# text_batch = [element["text"] for element in data]
# tokenized = tokenizer(text_batch, padding='longest', truncation=True, return_tensors='pt')
# return tokenized
# data_loader = DataLoader(tokenized_batch, shuffle=False, batch_size=8, num_workers=0, drop_last=False, collate_fn=collate_tokenize)
data_loader = DataLoader(tokenized_batch, shuffle=False, batch_size=8, num_workers=0, drop_last=False)
# num_batches = len(list(data_loader))
batch = next(iter(data_loader))
print(f'{batch=}')
print('Done!\a')