chore: More verbose dataloading
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1 changed files with 19 additions and 8 deletions
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@ -1,4 +1,5 @@
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from abc import abstractmethod, ABC
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from itertools import accumulate
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from os.path import join, curdir
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from typing import Callable
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@ -53,23 +54,33 @@ class Dataset(TorchDataset, ABC):
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self.chunk_offsets = self.get_offsets()
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if self.size == -1:
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# Just use the whole dataset
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self.bytes = ''.join(tqdm(self.data, desc="Encoding data")).encode('utf-8', errors='replace')
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self.bytes = ''.join(tqdm(self.data, desc="Encoding data", leave=False)).encode('utf-8', errors='replace')
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else:
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# Use only partition, calculate offsets
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self.bytes = ''.join(tqdm(self.data[:len(self.chunk_offsets)], desc="Encoding data")).encode('utf-8', errors='replace')
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self.bytes = (''.join(tqdm(self.data[:len(self.chunk_offsets)], desc="Encoding data", leave=False))
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.encode('utf-8', errors='replace'))
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bytes_array = np.frombuffer(self.bytes, dtype=np.uint8) # Zero-copy
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self.tensor = torch.from_numpy(bytes_array).to(torch.long)
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bytes_array = np.frombuffer(self.bytes, dtype=np.uint8) # Zero-copy
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self.tensor = torch.from_numpy(bytes_array).to(torch.long, non_blocking=True)
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def get_offsets(self):
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"""
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Calculate for each chunk how many bytes came before it
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"""
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data = self.data
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size = self.size
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if size == -1:
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return [0, *accumulate(tqdm(map(len, data), desc="Calculating offsets", leave=False, total=len(data)))]
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offsets = [0]
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while len(offsets) <= len(self.data) and (self.size == -1 or offsets[-1] < self.size):
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idx = len(offsets) - 1
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offsets.append(offsets[idx] + len(self.data[idx]))
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print(offsets)
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total = 0
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append = offsets.append
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for chunk in tqdm(data):
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if total >= size:
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break
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total += len(chunk)
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append(total)
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return offsets
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def get_chunked_item(self, idx: int, offsets: list[int], context_length: int):
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