modules/voice_conversion/fairseq/data/indexed_dataset.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import shutil
import struct
from functools import lru_cache

import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from fairseq.data.huffman import HuffmanMMapIndexedDataset, HuffmanMMapIndex

from . import FairseqDataset

from typing import Union


def best_fitting_int_dtype(
    max_int_to_represent,
) -> Union[np.uint16, np.uint32, np.int64]:

    if max_int_to_represent is None:
        return np.uint32  # Safe guess
    elif max_int_to_represent < 65500:
        return np.uint16
    elif max_int_to_represent < 4294967295:
        return np.uint32
    else:
        return np.int64
        # we avoid np.uint64 because it doesn't save space and its type promotion behaves unexpectedly
        # https://github.com/numpy/numpy/issues/5745


def get_available_dataset_impl():
    return list(map(str, DATASET_IMPL_CHOICES))


def infer_dataset_impl(path):
    if IndexedRawTextDataset.exists(path):
        return "raw"
    elif IndexedDataset.exists(path):
        with open(index_file_path(path), "rb") as f:
            magic = f.read(8)
            if magic == IndexedDataset._HDR_MAGIC:
                return "cached"
            elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]:
                return "mmap"
            elif magic == HuffmanMMapIndex._HDR_MAGIC[:8]:
                return "huffman"
            else:
                return None
    elif FastaDataset.exists(path):
        return "fasta"
    else:
        return None


def make_builder(out_file, impl, vocab_size=None):
    if impl == "mmap":
        return MMapIndexedDatasetBuilder(
            out_file, dtype=best_fitting_int_dtype(vocab_size)
        )
    elif impl == "fasta":
        raise NotImplementedError
    elif impl == "huffman":
        raise ValueError(
            "Use HuffmanCodeBuilder directly as it has a different interface."
        )
    else:
        return IndexedDatasetBuilder(out_file)


def make_dataset(path, impl, fix_lua_indexing=False, dictionary=None):
    if impl == "raw" and IndexedRawTextDataset.exists(path):
        assert dictionary is not None
        return IndexedRawTextDataset(path, dictionary)
    elif impl == "lazy" and IndexedDataset.exists(path):
        return IndexedDataset(path, fix_lua_indexing=fix_lua_indexing)
    elif impl == "cached" and IndexedDataset.exists(path):
        return IndexedCachedDataset(path, fix_lua_indexing=fix_lua_indexing)
    elif impl == "mmap" and MMapIndexedDataset.exists(path):
        return MMapIndexedDataset(path)
    elif impl == "fasta" and FastaDataset.exists(path):
        from fairseq.data.fasta_dataset import EncodedFastaDataset

        return EncodedFastaDataset(path, dictionary)
    elif impl == "huffman" and HuffmanMMapIndexedDataset.exists(path):
        return HuffmanMMapIndexedDataset(path)
    return None


def dataset_exists(path, impl):
    if impl == "raw":
        return IndexedRawTextDataset.exists(path)
    elif impl == "mmap":
        return MMapIndexedDataset.exists(path)
    elif impl == "huffman":
        return HuffmanMMapIndexedDataset.exists(path)
    else:
        return IndexedDataset.exists(path)


def read_longs(f, n):
    a = np.empty(n, dtype=np.int64)
    f.readinto(a)
    return a


def write_longs(f, a):
    f.write(np.array(a, dtype=np.int64))


_code_to_dtype = {
    1: np.uint8,
    2: np.int8,
    3: np.int16,
    4: np.int32,
    5: np.int64,
    6: np.float64,
    7: np.double,
    8: np.uint16,
    9: np.uint32,
    10: np.uint64,
}


def _dtype_header_code(dtype) -> int:
    for k in _code_to_dtype.keys():
        if _code_to_dtype[k] == dtype:
            return k
    raise ValueError(dtype)


def index_file_path(prefix_path):
    return prefix_path + ".idx"


def data_file_path(prefix_path):
    return prefix_path + ".bin"


class IndexedDataset(FairseqDataset):
    """Loader for TorchNet IndexedDataset"""

    _HDR_MAGIC = b"TNTIDX\x00\x00"

    def __init__(self, path, fix_lua_indexing=False):
        super().__init__()
        self.path = path
        self.fix_lua_indexing = fix_lua_indexing
        self.data_file = None
        self.read_index(path)

    def read_index(self, path):
        with open(index_file_path(path), "rb") as f:
            magic = f.read(8)
            assert magic == self._HDR_MAGIC, (
                "Index file doesn't match expected format. "
                "Make sure that --dataset-impl is configured properly."
            )
            version = f.read(8)
            assert struct.unpack("<Q", version) == (1,)
            code, self.element_size = struct.unpack("<QQ", f.read(16))
            self.dtype = _code_to_dtype[code]
            self._len, self.s = struct.unpack("<QQ", f.read(16))
            self.dim_offsets = read_longs(f, self._len + 1)
            self.data_offsets = read_longs(f, self._len + 1)
            self.sizes = read_longs(f, self.s)

    def read_data(self, path):
        self.data_file = open(data_file_path(path), "rb", buffering=0)

    def check_index(self, i):
        if i < 0 or i >= self._len:
            raise IndexError("index out of range")

    def __del__(self):
        if self.data_file:
            self.data_file.close()

    @lru_cache(maxsize=8)
    def __getitem__(self, i) -> torch.Tensor:
        if not self.data_file:
            self.read_data(self.path)
        self.check_index(i)
        tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]]
        a = np.empty(tensor_size, dtype=self.dtype)
        self.data_file.seek(self.data_offsets[i] * self.element_size)
        self.data_file.readinto(a)
        item = torch.from_numpy(a).long()
        if self.fix_lua_indexing:
            item -= 1  # subtract 1 for 0-based indexing
        return item

    def __len__(self):
        return self._len

    def num_tokens(self, index):
        return self.sizes[index]

    def size(self, index):
        return self.sizes[index]

    @staticmethod
    def exists(path):
        return PathManager.exists(index_file_path(path)) and PathManager.exists(
            data_file_path(path)
        )

    @property
    def supports_prefetch(self):
        return False  # avoid prefetching to save memory


class IndexedCachedDataset(IndexedDataset):
    def __init__(self, path, fix_lua_indexing=False):
        super().__init__(path, fix_lua_indexing=fix_lua_indexing)
        self.cache = None
        self.cache_index = {}

    @property
    def supports_prefetch(self):
        return True

    def prefetch(self, indices):
        if all(i in self.cache_index for i in indices):
            return
        if not self.data_file:
            self.read_data(self.path)
        indices = sorted(set(indices))
        total_size = 0
        for i in indices:
            total_size += self.data_offsets[i + 1] - self.data_offsets[i]
        self.cache = np.empty(total_size, dtype=self.dtype)
        ptx = 0
        self.cache_index.clear()
        for i in indices:
            self.cache_index[i] = ptx
            size = self.data_offsets[i + 1] - self.data_offsets[i]
            a = self.cache[ptx : ptx + size]
            self.data_file.seek(self.data_offsets[i] * self.element_size)
            self.data_file.readinto(a)
            ptx += size
        if self.data_file:
            # close and delete data file after prefetch so we can pickle
            self.data_file.close()
            self.data_file = None

    @lru_cache(maxsize=8)
    def __getitem__(self, i):
        self.check_index(i)
        tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]]
        a = np.empty(tensor_size, dtype=self.dtype)
        ptx = self.cache_index[i]
        np.copyto(a, self.cache[ptx : ptx + a.size])
        item = torch.from_numpy(a).long()
        if self.fix_lua_indexing:
            item -= 1  # subtract 1 for 0-based indexing
        return item


class IndexedRawTextDataset(FairseqDataset):
    """Takes a text file as input and binarizes it in memory at instantiation.
    Original lines are also kept in memory"""

    def __init__(self, path, dictionary, append_eos=True, reverse_order=False):
        self.tokens_list = []
        self.lines = []
        self.sizes = []
        self.append_eos = append_eos
        self.reverse_order = reverse_order
        self.read_data(path, dictionary)
        self.size = len(self.tokens_list)

    def read_data(self, path, dictionary):
        with open(path, "r", encoding="utf-8") as f:
            for line in f:
                self.lines.append(line.strip("\n"))
                tokens = dictionary.encode_line(
                    line,
                    add_if_not_exist=False,
                    append_eos=self.append_eos,
                    reverse_order=self.reverse_order,
                ).long()
                self.tokens_list.append(tokens)
                self.sizes.append(len(tokens))
        self.sizes = np.array(self.sizes)

    def check_index(self, i):
        if i < 0 or i >= self.size:
            raise IndexError("index out of range")

    @lru_cache(maxsize=8)
    def __getitem__(self, i):
        self.check_index(i)
        return self.tokens_list[i]

    def get_original_text(self, i):
        self.check_index(i)
        return self.lines[i]

    def __del__(self):
        pass

    def __len__(self):
        return self.size

    def num_tokens(self, index):
        return self.sizes[index]

    def size(self, index):
        return self.sizes[index]

    @staticmethod
    def exists(path):
        return PathManager.exists(path)


class IndexedDatasetBuilder:
    element_sizes = {
        np.uint8: 1,
        np.int8: 1,
        np.int16: 2,
        np.int32: 4,
        np.int64: 8,
        np.float64: 4,
        np.double: 8,
    }

    def __init__(self, out_file, dtype=np.int32):
        self.out_file = open(out_file, "wb")
        self.dtype = dtype
        self.data_offsets = [0]
        self.dim_offsets = [0]
        self.sizes = []
        self.element_size = self.element_sizes[self.dtype]

    def add_item(self, tensor):
        # +1 for Lua compatibility
        bytes = self.out_file.write(np.array(tensor.numpy() + 1, dtype=self.dtype))
        self.data_offsets.append(self.data_offsets[-1] + bytes / self.element_size)
        for s in tensor.size():
            self.sizes.append(s)
        self.dim_offsets.append(self.dim_offsets[-1] + len(tensor.size()))

    def merge_file_(self, another_file):
        index = IndexedDataset(another_file)
        assert index.dtype == self.dtype

        begin = self.data_offsets[-1]
        for offset in index.data_offsets[1:]:
            self.data_offsets.append(begin + offset)
        self.sizes.extend(index.sizes)
        begin = self.dim_offsets[-1]
        for dim_offset in index.dim_offsets[1:]:
            self.dim_offsets.append(begin + dim_offset)

        with open(data_file_path(another_file), "rb") as f:
            while True:
                data = f.read(1024)
                if data:
                    self.out_file.write(data)
                else:
                    break

    def finalize(self, index_file):
        self.out_file.close()
        index = open(index_file, "wb")
        index.write(b"TNTIDX\x00\x00")
        index.write(struct.pack("<Q", 1))
        index.write(
            struct.pack("<QQ", _dtype_header_code(self.dtype), self.element_size)
        )
        index.write(struct.pack("<QQ", len(self.data_offsets) - 1, len(self.sizes)))
        write_longs(index, self.dim_offsets)
        write_longs(index, self.data_offsets)
        write_longs(index, self.sizes)
        index.close()


def _warmup_mmap_file(path):
    with open(path, "rb") as stream:
        while stream.read(100 * 1024 * 1024):
            pass


class MMapIndexedDataset(torch.utils.data.Dataset):
    class Index:
        _HDR_MAGIC = b"MMIDIDX\x00\x00"

        @classmethod
        def writer(cls, path, dtype):
            class _Writer:
                def __enter__(self):
                    self._file = open(path, "wb")

                    self._file.write(cls._HDR_MAGIC)
                    self._file.write(struct.pack("<Q", 1))
                    self._file.write(struct.pack("<B", _dtype_header_code(dtype)))

                    return self

                @staticmethod
                def _get_pointers(sizes):
                    dtype_size = dtype().itemsize
                    address = 0
                    pointers = []

                    for size in sizes:
                        pointers.append(address)
                        address += size * dtype_size

                    return pointers

                def write(self, sizes):
                    pointers = self._get_pointers(sizes)

                    self._file.write(struct.pack("<Q", len(sizes)))

                    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

                def __exit__(self, exc_type, exc_val, exc_tb):
                    self._file.close()

            return _Writer()

        def __init__(self, path):
            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."
                )
                version = struct.unpack("<Q", stream.read(8))
                assert (1,) == version

                (dtype_code,) = struct.unpack("<B", stream.read(1))
                self._dtype = _code_to_dtype[dtype_code]
                self._dtype_size = self._dtype().itemsize

                self._len = struct.unpack("<Q", stream.read(8))[0]
                offset = stream.tell()

            _warmup_mmap_file(path)

            self._bin_buffer_mmap = np.memmap(path, mode="r", order="C")
            self._bin_buffer = memoryview(self._bin_buffer_mmap)
            self._sizes = np.frombuffer(
                self._bin_buffer, dtype=np.int32, count=self._len, offset=offset
            )
            self._pointers = np.frombuffer(
                self._bin_buffer,
                dtype=np.int64,
                count=self._len,
                offset=offset + self._sizes.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

        @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):
        super().__init__()

        self._path = None
        self._index = None
        self._bin_buffer = None

        self._do_init(path)

    def __getstate__(self):
        return self._path

    def __setstate__(self, state):
        self._do_init(state)

    def _do_init(self, path):
        self._path = path
        self._index = self.Index(index_file_path(self._path))

        _warmup_mmap_file(data_file_path(self._path))
        self._bin_buffer_mmap = np.memmap(
            data_file_path(self._path), mode="r", order="C"
        )
        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, i):
        ptr, size = self._index[i]
        np_array = np.frombuffer(
            self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr
        )
        if self._index.dtype != np.int64:
            np_array = np_array.astype(np.int64)

        return torch.from_numpy(np_array)

    @property
    def sizes(self):
        return self._index.sizes

    @property
    def supports_prefetch(self):
        return False

    @staticmethod
    def exists(path):
        return PathManager.exists(index_file_path(path)) and PathManager.exists(
            data_file_path(path)
        )


def get_indexed_dataset_to_local(path) -> str:
    local_index_path = PathManager.get_local_path(index_file_path(path))
    local_data_path = PathManager.get_local_path(data_file_path(path))

    assert local_index_path.endswith(".idx") and local_data_path.endswith(".bin"), (
        "PathManager.get_local_path does not return files with expected patterns: "
        f"{local_index_path} and {local_data_path}"
    )

    local_path = local_data_path[:-4]  # stripping surfix ".bin"
    assert local_path == local_index_path[:-4]  # stripping surfix ".idx"
    return local_path


class MMapIndexedDatasetBuilder:
    def __init__(self, out_file, dtype=np.int64):
        self._data_file = open(out_file, "wb")
        self._dtype = dtype
        self._sizes = []

    def add_item(self, tensor):
        np_array = np.array(tensor.numpy(), dtype=self._dtype)
        self._data_file.write(np_array.tobytes(order="C"))
        self._sizes.append(np_array.size)

    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)