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Merge pull request #428 from kjslag/memmap-memory-leak

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fix np.memmap memory leak
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Andrej 2024-02-27 08:41:24 -08:00 committed by GitHub
commit f68ac2200d
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1 changed files with 6 additions and 3 deletions
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train.py
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@ -113,10 +113,13 @@ ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=
# poor man's data loader # poor man's data loader
data_dir = os.path.join('data', dataset) data_dir = os.path.join('data', dataset)
train_data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
val_data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')
def get_batch(split): def get_batch(split):
data = train_data if split == 'train' else val_data # We recreate np.memmap every batch to avoid a memory leak, as per
# https://stackoverflow.com/questions/45132940/numpy-memmap-memory-usage-want-to-iterate-once/61472122#61472122
if split == 'train':
data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
else:
data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')
ix = torch.randint(len(data) - block_size, (batch_size,)) ix = torch.randint(len(data) - block_size, (batch_size,))
x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix]) x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix]) y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])