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Custom dataset loaders for different training corpora.
Usage in config:
data:
dataset: wikitext103
# or
dataset: pile
# or
dataset: your_custom_dataset
"""
from typing import Optional
import torch
from torch.utils.data import DataLoader
def build_wikitext_dataloader(
tokenizer,
split: str = "train",
seq_len: int = 512,
batch_size: int = 32,
num_workers: int = 4,
cache_dir: Optional[str] = None,
):
"""WikiText-103 dataset."""
from datasets import load_dataset
ds = load_dataset("wikitext", "wikitext-103-v1", split=split, cache_dir=cache_dir)
def tokenize_and_chunk(examples):
all_ids = []
for text in examples["text"]:
all_ids.extend(tokenizer(text, truncation=False, padding=False)["input_ids"])
chunks = [all_ids[i:i + seq_len] for i in range(0, len(all_ids) - seq_len, seq_len)]
return {"input_ids": chunks}
ds = ds.map(tokenize_and_chunk, batched=True, remove_columns=["text"])
ds.set_format(type="torch")
def collate_fn(examples):
ids = torch.stack([e["input_ids"] for e in examples])
return {"input_ids": ids, "attention_mask": torch.ones_like(ids)}
return DataLoader(
ds, batch_size=batch_size, shuffle=(split == "train"),
num_workers=num_workers, collate_fn=collate_fn, pin_memory=True
)
def build_c4_dataloader(
tokenizer,
split: str = "train",
seq_len: int = 512,
batch_size: int = 32,
num_workers: int = 4,
cache_dir: Optional[str] = None,
streaming: bool = False,
):
"""C4 (Colossal Clean Crawled Corpus) dataset."""
from datasets import load_dataset
ds = load_dataset("c4", "en", split=split, cache_dir=cache_dir, streaming=streaming)
def tokenize_and_chunk(examples):
all_ids = []
for text in examples["text"]:
all_ids.extend(tokenizer(text, truncation=False, padding=False)["input_ids"])
chunks = [all_ids[i:i + seq_len] for i in range(0, len(all_ids) - seq_len, seq_len)]
return {"input_ids": chunks}
ds = ds.map(tokenize_and_chunk, batched=True, remove_columns=["text", "timestamp", "url"])
if not streaming:
ds.set_format(type="torch")
def collate_fn(examples):
ids = torch.stack([e["input_ids"] for e in examples])
return {"input_ids": ids, "attention_mask": torch.ones_like(ids)}
return DataLoader(
ds, batch_size=batch_size, shuffle=(split == "train" and not streaming),
num_workers=num_workers, collate_fn=collate_fn, pin_memory=True
)
def build_pile_dataloader(
tokenizer,
split: str = "train",
seq_len: int = 512,
batch_size: int = 32,
num_workers: int = 4,
cache_dir: Optional[str] = None,
streaming: bool = True, # Pile很大，推荐streaming
):
"""The Pile dataset (825GB)."""
from datasets import load_dataset
ds = load_dataset("EleutherAI/pile", split=split, cache_dir=cache_dir, streaming=streaming)
def tokenize_and_chunk(examples):
all_ids = []
for text in examples["text"]:
all_ids.extend(tokenizer(text, truncation=False, padding=False)["input_ids"])
chunks = [all_ids[i:i + seq_len] for i in range(0, len(all_ids) - seq_len, seq_len)]
return {"input_ids": chunks}
ds = ds.map(tokenize_and_chunk, batched=True, remove_columns=["text", "meta"])
def collate_fn(examples):
ids = torch.stack([e["input_ids"] for e in examples])
return {"input_ids": ids, "attention_mask": torch.ones_like(ids)}
return DataLoader(
ds, batch_size=batch_size, shuffle=False, # streaming不支持shuffle
num_workers=num_workers, collate_fn=collate_fn
)
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