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prepare.py

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+import os
+import numpy as np
+
+from tqdm import tqdm
+from datasets import load_dataset
+from transformers import LlamaTokenizerFast
+
+num_proc = 4
+block_size = 1024
+
+if __name__ == "__main__":
+
+    # features: ['text', 'id', 'dump', 'url', 'date', 'file_path', 'language', 'language_score', 'token_count']
+    ds = load_dataset("HuggingFaceFW/fineweb", name="sample-10BT", num_proc=num_proc)
+
+    split_ds = ds["train"].train_test_split(test_size=0.0005, seed=2357, shuffle=True)
+    split_ds['val'] = split_ds.pop('test')
+
+    tokenizer = LlamaTokenizerFast.from_pretrained(
+        "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+        pad_token="<s>",
+        model_max_length=None,
+        add_bos_token=False,
+        add_eos_token=True,
+    )
+
+    # tokenize the dataset.
+    # it does the truncation, padding, and overlowing
+    # into a new sequence with it's own bos and eos token for us.
+    def process(example):
+        ids = tokenizer(
+            example['text'],
+            truncation=False,
+            return_length=True,
+            padding=False,
+            return_tensors='pt',
+        )['input_ids'][0]
+        out = {'ids': ids, 'len': len(ids)}
+        return out
+
+    tokenized = split_ds.map(
+        process,
+        remove_columns=ds['train'].column_names,
+        desc="tokenizing the splits",
+        num_proc=num_proc,
+    )
+
+    # concatenate all the ids in each dataset into one large file we can use for training
+    for split, dset in tokenized.items():
+        arr_len = np.sum(dset['len'], dtype=np.uint64)
+        filename = os.path.join(os.path.dirname(__file__), f'{split}.bin')
+        dtype = np.uint16 # (can do since vocab_size == 32000 is < 2**16)
+        arr = np.memmap(filename, dtype=dtype, mode='w+', shape=(arr_len,))
+        total_batches = 1024
+
+        idx = 0
+        for batch_idx in tqdm(range(total_batches), desc=f'writing {filename}'):
+            # Batch together samples for faster write
+            batch = dset.shard(num_shards=total_batches, index=batch_idx, contiguous=True).with_format('numpy')
+            arr_batch = np.concatenate(batch['ids'], axis=0)
+            # Write into mmap
+            arr[idx : idx + len(arr_batch)] = arr_batch
+            idx += len(arr_batch)
+        arr.flush()
+
+    # train.bin is ~22GB, val.bin ~8.5MB
+    # train has ~11B tokens (11,774,429,883)
+    # val has ~5.9M tokens (5,908,112)

train.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f3721c1e56bb6fe461982e65429a9c33c8af6307146c9673489da756c1df5c94
+size 23548859766

val.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1c66e01f7bb7ba8465cb49e819ec7f3c409f1b9673ef33a4d12bb0711f85aec2
+size 11816224