598 lines
19 KiB
Python
598 lines
19 KiB
Python
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# Copyright (c) 2021, EleutherAI
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# This file is based on code by the authors denoted below and has been modified from its original version.
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#
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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# copied from fairseq/fairseq/data/indexed_dataset.py
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# Removed IndexedRawTextDataset since it relied on Fairseq dictionary
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# other slight modifications to remove fairseq dependencies
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# Added document index to index file and made it accessible.
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# An empty sentence no longer separates documents.
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import os
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import shutil
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import struct
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from functools import lru_cache
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from itertools import accumulate
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import numpy as np
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import torch
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def __best_fitting_dtype(vocab_size=None):
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if vocab_size is not None and vocab_size < 65500:
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return np.uint16
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else:
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return np.int32
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def infer_dataset_impl(path):
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if IndexedDataset.exists(path):
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with open(index_file_path(path), "rb") as f:
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magic = f.read(8)
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if magic == IndexedDataset._HDR_MAGIC:
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return "cached"
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elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]:
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return "mmap"
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else:
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return None
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else:
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print(f"Dataset does not exist: {path}")
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print(
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"Path should be a basename that both .idx and .bin can be appended to get full filenames."
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)
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return None
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def make_builder(out_file, impl, vocab_size=None):
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if impl == "mmap":
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return MMapIndexedDatasetBuilder(
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out_file, dtype=__best_fitting_dtype(vocab_size)
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)
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else:
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return IndexedDatasetBuilder(out_file)
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def make_dataset(path, impl, skip_warmup=False):
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if not IndexedDataset.exists(path):
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print(f"Dataset does not exist: {path}")
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print(
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"Path should be a basename that both .idx and .bin can be appended to get full filenames."
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)
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return None
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if impl == "infer":
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impl = infer_dataset_impl(path)
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if impl == "lazy" and IndexedDataset.exists(path):
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return IndexedDataset(path)
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elif impl == "cached" and IndexedDataset.exists(path):
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return IndexedCachedDataset(path)
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elif impl == "mmap" and MMapIndexedDataset.exists(path):
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return MMapIndexedDataset(path, skip_warmup)
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print(f"Unknown dataset implementation: {impl}")
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return None
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def dataset_exists(path, impl):
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if impl == "mmap":
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return MMapIndexedDataset.exists(path)
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else:
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return IndexedDataset.exists(path)
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def read_longs(f, n):
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a = np.empty(n, dtype=np.int64)
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f.readinto(a)
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return a
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def write_longs(f, a):
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f.write(np.array(a, dtype=np.int64))
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dtypes = {
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1: np.uint8,
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2: np.int8,
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3: np.int16,
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4: np.int32,
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5: np.int64,
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6: np.float32,
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7: np.float64,
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8: np.uint16,
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}
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def code(dtype):
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for k in dtypes.keys():
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if dtypes[k] == dtype:
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return k
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raise ValueError(dtype)
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def index_file_path(prefix_path):
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return prefix_path + ".idx"
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def data_file_path(prefix_path):
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return prefix_path + ".bin"
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def create_doc_idx(sizes):
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doc_idx = [0]
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for i, s in enumerate(sizes):
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if s == 0:
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doc_idx.append(i + 1)
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return doc_idx
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class IndexedDataset(torch.utils.data.Dataset):
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"""Loader for IndexedDataset"""
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_HDR_MAGIC = b"TNTIDX\x00\x00"
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def __init__(self, path):
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super().__init__()
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self.path = path
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self.data_file = None
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self.read_index(path)
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def read_index(self, path):
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with open(index_file_path(path), "rb") as f:
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magic = f.read(8)
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assert magic == self._HDR_MAGIC, (
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"Index file doesn't match expected format. "
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"Make sure that --dataset-impl is configured properly."
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)
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version = f.read(8)
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assert struct.unpack("<Q", version) == (1,)
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code, self.element_size = struct.unpack("<QQ", f.read(16))
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self.dtype = dtypes[code]
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self._len, self.s = struct.unpack("<QQ", f.read(16))
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self.doc_count = struct.unpack("<Q", f.read(8))
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self.dim_offsets = read_longs(f, self._len + 1)
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self.data_offsets = read_longs(f, self._len + 1)
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self.sizes = read_longs(f, self.s)
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self.doc_idx = read_longs(f, self.doc_count)
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def read_data(self, path):
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self.data_file = open(data_file_path(path), "rb", buffering=0)
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def check_index(self, i):
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if i < 0 or i >= self._len:
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raise IndexError("index out of range")
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def __del__(self):
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if self.data_file:
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self.data_file.close()
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# @lru_cache(maxsize=8)
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def __getitem__(self, idx):
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if not self.data_file:
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self.read_data(self.path)
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if isinstance(idx, int):
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i = idx
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self.check_index(i)
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tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]]
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a = np.empty(tensor_size, dtype=self.dtype)
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self.data_file.seek(self.data_offsets[i] * self.element_size)
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self.data_file.readinto(a)
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return a
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elif isinstance(idx, slice):
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start, stop, step = idx.indices(len(self))
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if step != 1:
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raise ValueError("Slices into indexed_dataset must be contiguous")
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sizes = self.sizes[self.dim_offsets[start] : self.dim_offsets[stop]]
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size = sum(sizes)
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a = np.empty(size, dtype=self.dtype)
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self.data_file.seek(self.data_offsets[start] * self.element_size)
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self.data_file.readinto(a)
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offsets = list(accumulate(sizes))
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sents = np.split(a, offsets[:-1])
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return sents
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def __len__(self):
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return self._len
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def num_tokens(self, index):
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return self.sizes[index]
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def size(self, index):
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return self.sizes[index]
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@staticmethod
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def exists(path):
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return os.path.exists(index_file_path(path)) and os.path.exists(
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data_file_path(path)
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)
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@property
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def supports_prefetch(self):
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return False # avoid prefetching to save memory
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class IndexedCachedDataset(IndexedDataset):
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def __init__(self, path):
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super().__init__(path)
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self.cache = None
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self.cache_index = {}
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@property
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def supports_prefetch(self):
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return True
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def prefetch(self, indices):
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if all(i in self.cache_index for i in indices):
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return
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if not self.data_file:
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self.read_data(self.path)
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indices = sorted(set(indices))
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total_size = 0
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for i in indices:
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total_size += self.data_offsets[i + 1] - self.data_offsets[i]
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self.cache = np.empty(total_size, dtype=self.dtype)
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ptx = 0
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self.cache_index.clear()
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for i in indices:
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self.cache_index[i] = ptx
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size = self.data_offsets[i + 1] - self.data_offsets[i]
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a = self.cache[ptx : ptx + size]
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self.data_file.seek(self.data_offsets[i] * self.element_size)
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self.data_file.readinto(a)
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ptx += size
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if self.data_file:
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# close and delete data file after prefetch so we can pickle
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self.data_file.close()
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self.data_file = None
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# @lru_cache(maxsize=8)
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def __getitem__(self, idx):
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if isinstance(idx, int):
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i = idx
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self.check_index(i)
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tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]]
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a = np.empty(tensor_size, dtype=self.dtype)
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ptx = self.cache_index[i]
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np.copyto(a, self.cache[ptx : ptx + a.size])
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return a
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elif isinstance(idx, slice):
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# Hack just to make this work, can optimizer later if necessary
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sents = []
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for i in range(*idx.indices(len(self))):
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sents.append(self[i])
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return sents
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class IndexedDatasetBuilder(object):
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element_sizes = {
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np.uint8: 1,
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np.int8: 1,
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np.int16: 2,
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np.int32: 4,
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np.int64: 8,
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np.float32: 4,
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np.float64: 8,
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}
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def __init__(self, out_file, dtype=np.int32):
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self.out_file = open(out_file, "wb")
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self.dtype = dtype
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self.data_offsets = [0]
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self.dim_offsets = [0]
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self.sizes = []
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self.element_size = self.element_sizes[self.dtype]
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self.doc_idx = [0]
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def add_item(self, np_array):
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assert isinstance(np_array, np.ndarray) and np_array.dtype == self.dtype
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bytes = self.out_file.write(np_array)
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self.data_offsets.append(self.data_offsets[-1] + bytes / self.element_size)
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for s in np_array.shape:
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self.sizes.append(s)
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self.dim_offsets.append(self.dim_offsets[-1] + len(np_array.shape))
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def end_document(self):
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self.doc_idx.append(len(self.sizes))
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def merge_file_(self, another_file):
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index = IndexedDataset(another_file)
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assert index.dtype == self.dtype
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begin = self.data_offsets[-1]
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for offset in index.data_offsets[1:]:
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self.data_offsets.append(begin + offset)
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self.sizes.extend(index.sizes)
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begin = self.dim_offsets[-1]
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for dim_offset in index.dim_offsets[1:]:
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self.dim_offsets.append(begin + dim_offset)
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with open(data_file_path(another_file), "rb") as f:
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while True:
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data = f.read(1024)
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if data:
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self.out_file.write(data)
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else:
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break
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def finalize(self, index_file):
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self.out_file.close()
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index = open(index_file, "wb")
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index.write(b"TNTIDX\x00\x00")
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index.write(struct.pack("<Q", 1))
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index.write(struct.pack("<QQ", code(self.dtype), self.element_size))
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index.write(struct.pack("<QQ", len(self.data_offsets) - 1, len(self.sizes)))
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index.write(struct.pack("<Q", len(self.doc_idx)))
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write_longs(index, self.dim_offsets)
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write_longs(index, self.data_offsets)
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write_longs(index, self.sizes)
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write_longs(index, self.doc_idx)
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index.close()
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def _warmup_mmap_file(path):
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with open(path, "rb") as stream:
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while stream.read(100 * 1024 * 1024):
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pass
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class MMapIndexedDataset(torch.utils.data.Dataset):
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class Index(object):
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_HDR_MAGIC = b"MMIDIDX\x00\x00"
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@classmethod
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def writer(cls, path, dtype):
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class _Writer(object):
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def __enter__(self):
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self._file = open(path, "wb")
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# Write Magic string so we can check the file format then opening it again.
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self._file.write(cls._HDR_MAGIC)
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# Write version number
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# Little endian unsigned 64 Bit integer
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self._file.write(struct.pack("<Q", 1))
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# Little endian unsigned 8 Bit integer
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self._file.write(struct.pack("<B", code(dtype)))
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return self
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@staticmethod
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def _get_pointers(sizes):
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pointers = np.zeros(len(sizes), dtype=np.int64)
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sizes = np.array(sizes, dtype=np.int64)
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np.cumsum(sizes[:-1], out=pointers[1:])
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pointers = pointers * dtype().itemsize
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return pointers
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def write(self, sizes, doc_idx):
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pointers = self._get_pointers(sizes)
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||
|
|
# Little endian unsigned 64 Bit integer
|
||
|
|
self._file.write(struct.pack("<Q", len(sizes)))
|
||
|
|
# Little endian unsigned 64 Bit integer
|
||
|
|
self._file.write(struct.pack("<Q", len(doc_idx)))
|
||
|
|
|
||
|
|
sizes = np.array(sizes, dtype=np.int32)
|
||
|
|
self._file.write(sizes.tobytes(order="C"))
|
||
|
|
del sizes
|
||
|
|
|
||
|
|
pointers = np.array(pointers, dtype=np.int64)
|
||
|
|
self._file.write(pointers.tobytes(order="C"))
|
||
|
|
del pointers
|
||
|
|
|
||
|
|
doc_idx = np.array(doc_idx, dtype=np.int64)
|
||
|
|
self._file.write(doc_idx.tobytes(order="C"))
|
||
|
|
|
||
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
||
|
|
self._file.close()
|
||
|
|
|
||
|
|
return _Writer()
|
||
|
|
|
||
|
|
def __init__(self, path, skip_warmup=False):
|
||
|
|
with open(path, "rb") as stream:
|
||
|
|
magic_test = stream.read(9)
|
||
|
|
assert self._HDR_MAGIC == magic_test, (
|
||
|
|
"Index file doesn't match expected format. "
|
||
|
|
"Make sure that --dataset-impl is configured properly."
|
||
|
|
)
|
||
|
|
# Little endian unsigned 64 Bit integer
|
||
|
|
version = struct.unpack("<Q", stream.read(8))
|
||
|
|
assert (1,) == version
|
||
|
|
|
||
|
|
# Little endian unsigned 8 Bit integer
|
||
|
|
(dtype_code,) = struct.unpack("<B", stream.read(1))
|
||
|
|
self._dtype = dtypes[dtype_code]
|
||
|
|
self._dtype_size = self._dtype().itemsize
|
||
|
|
|
||
|
|
self._len = struct.unpack("<Q", stream.read(8))[0]
|
||
|
|
self._doc_count = struct.unpack("<Q", stream.read(8))[0]
|
||
|
|
offset = stream.tell()
|
||
|
|
|
||
|
|
if not skip_warmup:
|
||
|
|
print(" warming up index mmap file...")
|
||
|
|
_warmup_mmap_file(path)
|
||
|
|
|
||
|
|
self._bin_buffer_mmap = np.memmap(path, mode="r", order="C")
|
||
|
|
self._bin_buffer = memoryview(self._bin_buffer_mmap)
|
||
|
|
print(" reading sizes...")
|
||
|
|
self._sizes = np.frombuffer(
|
||
|
|
self._bin_buffer, dtype=np.int32, count=self._len, offset=offset
|
||
|
|
)
|
||
|
|
print(" reading pointers...")
|
||
|
|
self._pointers = np.frombuffer(
|
||
|
|
self._bin_buffer,
|
||
|
|
dtype=np.int64,
|
||
|
|
count=self._len,
|
||
|
|
offset=offset + self._sizes.nbytes,
|
||
|
|
)
|
||
|
|
print(" reading document index...")
|
||
|
|
self._doc_idx = np.frombuffer(
|
||
|
|
self._bin_buffer,
|
||
|
|
dtype=np.int64,
|
||
|
|
count=self._doc_count,
|
||
|
|
offset=offset + self._sizes.nbytes + self._pointers.nbytes,
|
||
|
|
)
|
||
|
|
|
||
|
|
def __del__(self):
|
||
|
|
self._bin_buffer_mmap._mmap.close()
|
||
|
|
del self._bin_buffer_mmap
|
||
|
|
|
||
|
|
@property
|
||
|
|
def dtype(self):
|
||
|
|
return self._dtype
|
||
|
|
|
||
|
|
@property
|
||
|
|
def sizes(self):
|
||
|
|
return self._sizes
|
||
|
|
|
||
|
|
@property
|
||
|
|
def doc_idx(self):
|
||
|
|
return self._doc_idx
|
||
|
|
|
||
|
|
@lru_cache(maxsize=8)
|
||
|
|
def __getitem__(self, i):
|
||
|
|
return self._pointers[i], self._sizes[i]
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return self._len
|
||
|
|
|
||
|
|
def __init__(self, path, skip_warmup=False):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
self._path = None
|
||
|
|
self._index = None
|
||
|
|
self._bin_buffer = None
|
||
|
|
|
||
|
|
self._do_init(path, skip_warmup)
|
||
|
|
|
||
|
|
def __getstate__(self):
|
||
|
|
return self._path
|
||
|
|
|
||
|
|
def __setstate__(self, state):
|
||
|
|
self._do_init(state)
|
||
|
|
|
||
|
|
def _do_init(self, path, skip_warmup):
|
||
|
|
self._path = path
|
||
|
|
self._index = self.Index(index_file_path(self._path), skip_warmup)
|
||
|
|
|
||
|
|
if not skip_warmup:
|
||
|
|
print(" warming up data mmap file...")
|
||
|
|
_warmup_mmap_file(data_file_path(self._path))
|
||
|
|
print(" creating numpy buffer of mmap...")
|
||
|
|
self._bin_buffer_mmap = np.memmap(
|
||
|
|
data_file_path(self._path), mode="r", order="C"
|
||
|
|
)
|
||
|
|
print(" creating memory view of numpy buffer...")
|
||
|
|
self._bin_buffer = memoryview(self._bin_buffer_mmap)
|
||
|
|
|
||
|
|
def __del__(self):
|
||
|
|
self._bin_buffer_mmap._mmap.close()
|
||
|
|
del self._bin_buffer_mmap
|
||
|
|
del self._index
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return len(self._index)
|
||
|
|
|
||
|
|
# @lru_cache(maxsize=8)
|
||
|
|
def __getitem__(self, idx):
|
||
|
|
if isinstance(idx, int):
|
||
|
|
ptr, size = self._index[idx]
|
||
|
|
np_array = np.frombuffer(
|
||
|
|
self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr
|
||
|
|
)
|
||
|
|
return np_array
|
||
|
|
elif isinstance(idx, slice):
|
||
|
|
start, stop, step = idx.indices(len(self))
|
||
|
|
if step != 1:
|
||
|
|
raise ValueError("Slices into indexed_dataset must be contiguous")
|
||
|
|
ptr = self._index._pointers[start]
|
||
|
|
sizes = self._index._sizes[idx]
|
||
|
|
offsets = list(accumulate(sizes))
|
||
|
|
total_size = sum(sizes)
|
||
|
|
np_array = np.frombuffer(
|
||
|
|
self._bin_buffer, dtype=self._index.dtype, count=total_size, offset=ptr
|
||
|
|
)
|
||
|
|
sents = np.split(np_array, offsets[:-1])
|
||
|
|
return sents
|
||
|
|
|
||
|
|
def get(self, idx, offset=0, length=None):
|
||
|
|
"""Retrieves a single item from the dataset with the option to only
|
||
|
|
return a portion of the item.
|
||
|
|
|
||
|
|
get(idx) is the same as [idx] but get() does not support slicing.
|
||
|
|
"""
|
||
|
|
ptr, size = self._index[idx]
|
||
|
|
if length is None:
|
||
|
|
length = size - offset
|
||
|
|
ptr += offset * np.dtype(self._index.dtype).itemsize
|
||
|
|
np_array = np.frombuffer(
|
||
|
|
self._bin_buffer, dtype=self._index.dtype, count=length, offset=ptr
|
||
|
|
)
|
||
|
|
return np_array
|
||
|
|
|
||
|
|
@property
|
||
|
|
def sizes(self):
|
||
|
|
return self._index.sizes
|
||
|
|
|
||
|
|
@property
|
||
|
|
def doc_idx(self):
|
||
|
|
return self._index.doc_idx
|
||
|
|
|
||
|
|
def get_doc_idx(self):
|
||
|
|
return self._index._doc_idx
|
||
|
|
|
||
|
|
def set_doc_idx(self, doc_idx_):
|
||
|
|
self._index._doc_idx = doc_idx_
|
||
|
|
|
||
|
|
@property
|
||
|
|
def supports_prefetch(self):
|
||
|
|
return False
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def exists(path):
|
||
|
|
return os.path.exists(index_file_path(path)) and os.path.exists(
|
||
|
|
data_file_path(path)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class MMapIndexedDatasetBuilder(object):
|
||
|
|
def __init__(self, out_file, dtype=np.int64):
|
||
|
|
self._data_file = open(out_file, "wb")
|
||
|
|
self._dtype = dtype
|
||
|
|
self._sizes = []
|
||
|
|
self._doc_idx = [0]
|
||
|
|
|
||
|
|
@property
|
||
|
|
def dtype(self):
|
||
|
|
return self._dtype
|
||
|
|
|
||
|
|
def add_item(self, np_array):
|
||
|
|
assert isinstance(np_array, np.ndarray) and np_array.dtype == self.dtype
|
||
|
|
self._data_file.write(np_array.tobytes(order="C"))
|
||
|
|
self._sizes.append(np_array.size)
|
||
|
|
|
||
|
|
def end_document(self):
|
||
|
|
self._doc_idx.append(len(self._sizes))
|
||
|
|
|
||
|
|
def merge_file_(self, another_file):
|
||
|
|
# Concatenate index
|
||
|
|
index = MMapIndexedDataset.Index(index_file_path(another_file))
|
||
|
|
assert index.dtype == self._dtype
|
||
|
|
|
||
|
|
for size in index.sizes:
|
||
|
|
self._sizes.append(size)
|
||
|
|
|
||
|
|
# Concatenate data
|
||
|
|
with open(data_file_path(another_file), "rb") as f:
|
||
|
|
shutil.copyfileobj(f, self._data_file)
|
||
|
|
|
||
|
|
def finalize(self, index_file):
|
||
|
|
self._data_file.close()
|
||
|
|
|
||
|
|
with MMapIndexedDataset.Index.writer(index_file, self._dtype) as index:
|
||
|
|
index.write(self._sizes, self._doc_idx)
|