Add source: tmt/data/dataset.py

tmt/data/dataset.py

@@ -0,0 +1,75 @@
+"""
+dataset.py — loads wikitext-2 or tinystories and chunks into fixed-length blocks.
+"""
+from __future__ import annotations
+
+from typing import Dict
+
+import torch
+from datasets import load_dataset
+from torch.utils.data import DataLoader, Dataset
+
+
+class BlockDataset(Dataset):
+    """Chunks a flat token sequence into non-overlapping blocks of seq_len."""
+
+    def __init__(self, tokens: torch.Tensor, seq_len: int) -> None:
+        self.seq_len = seq_len
+        n_blocks = len(tokens) // (seq_len + 1)
+        # +1 so we can shift for next-token targets
+        self.data = tokens[: n_blocks * (seq_len + 1)].reshape(n_blocks, seq_len + 1)
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
+        chunk = self.data[idx]
+        return {"input_ids": chunk}
+
+
+def load_text_dataset(
+    name: str = "wikitext-2",
+    seq_len: int = 256,
+    batch_size: int = 16,
+    tokenizer_name: str = "gpt2",
+) -> Dict[str, DataLoader]:
+    """
+    Returns {"train": DataLoader, "validation": DataLoader}.
+    Supported names: "wikitext-2", "tinystories".
+    """
+    from transformers import AutoTokenizer
+
+    tok = AutoTokenizer.from_pretrained(tokenizer_name)
+    if tok.pad_token is None:
+        tok.add_special_tokens({"pad_token": "[PAD]"})
+
+    if name == "wikitext-2":
+        raw = load_dataset("wikitext", "wikitext-2-raw-v1")
+    elif name == "tinystories":
+        raw = load_dataset("roneneldan/TinyStories")
+    else:
+        raise ValueError(f"Unknown dataset: {name}")
+
+    def tokenize(examples):
+        return tok(examples["text"], truncation=False, return_attention_mask=False)
+
+    tokenized = raw.map(tokenize, batched=True, remove_columns=raw["train"].column_names)
+
+    loaders = {}
+    for split in ("train", "validation"):
+        if split not in tokenized:
+            continue
+        all_ids = []
+        for sample in tokenized[split]["input_ids"]:
+            all_ids.extend(sample)
+        flat = torch.tensor(all_ids, dtype=torch.long)
+        ds = BlockDataset(flat, seq_len)
+        loaders[split] = DataLoader(
+            ds,
+            batch_size=batch_size,
+            shuffle=(split == "train"),
+            num_workers=2,
+            pin_memory=True,
+        )
+
+    return loaders