“core13.0”

Megatron-LM/megatron/core/datasets/megatron_dataset.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+import hashlib
+import json
+from abc import ABC, abstractmethod
+from collections import OrderedDict
+from typing import Dict, Iterable, List, Optional, Union
+
+import numpy
+import torch
+
+from megatron.core.datasets.blended_megatron_dataset_config import BlendedMegatronDatasetConfig
+from megatron.core.datasets.indexed_dataset import IndexedDataset
+from megatron.core.datasets.utils import Split
+
+LowLevelDataset = Union[IndexedDataset, Iterable]
+
+
+class MegatronDataset(ABC, torch.utils.data.Dataset):
+    """The highest level wrapper class from which all dataset classes should inherit
+
+    Args:
+        dataset (LowLevelDataset): The dataset around which to build the MegatronDataset
+
+        dataset_path (Optional[str]): The real path on disk to the dataset, for bookkeeping
+
+        indices (numpy.ndarray): The set of the documents indices to expose
+
+        num_samples (Optional[int]): The minimum number of samples to build from the indexed
+            dataset. When None, build as many samples as correspond to one epoch.
+
+        index_split (Split): The indices Split
+
+        config (BlendedMegatronDatasetConfig): The config
+    """
+
+    def __init__(
+        self,
+        dataset: LowLevelDataset,
+        dataset_path: Optional[str],
+        indices: numpy.ndarray,
+        num_samples: Optional[int],
+        index_split: Split,
+        config: BlendedMegatronDatasetConfig,
+    ) -> None:
+        self.dataset = dataset
+        self.dataset_path = dataset_path
+        self.indices = indices
+        self.num_samples = num_samples
+        self.index_split = index_split
+        self.config = config
+
+        self.unique_identifiers = OrderedDict()
+
+        self.unique_identifiers["class"] = type(self).__name__
+        self.unique_identifiers["dataset_path"] = self.dataset_path
+        self.unique_identifiers["num_samples"] = self.num_samples
+        self.unique_identifiers["index_split"] = self.index_split.name
+        for attr in self._key_config_attributes():
+            self.unique_identifiers[attr] = getattr(self.config, attr)
+
+        self.unique_description = json.dumps(
+            self.unique_identifiers, indent=4, default=lambda obj: obj.unique_identifiers
+        )
+        self.unique_description_hash = hashlib.md5(
+            self.unique_description.encode("utf-8"), usedforsecurity=False
+        ).hexdigest()
+
+    @staticmethod
+    def numel_low_level_dataset(low_level_dataset: LowLevelDataset) -> int:
+        """Return the number of elements in the underlying low level dataset for the purpose of
+        segregating the train/valid/test split indices
+
+        It may be that the low level dataset can be split any number of ways, depending on the mid
+        level dataset it supports, which is why we define the "number of elements" function
+        separately from the __len__ function here in the mid level dataset class
+
+        Args:
+            low_level_dataset (LowLevelDataset): The underlying low level dataset
+
+        Returns:
+            int: The number of elements in the underlying low level dataset
+        """
+        raise NotImplementedError
+
+    @staticmethod
+    def build_low_level_dataset(
+        dataset_path: str, config: BlendedMegatronDatasetConfig
+    ) -> LowLevelDataset:
+        """Build the low level dataset via a function to be called from within
+        BlendedMegatronDatasetBuilder.build_generic_dataset
+
+        It may be that the low level dataset spans any subset of train/valid/test splits, which is
+        why we define a static "build" function separately from the constructor in the mid level
+        dataset class
+
+        Args:
+            dataset_path (str): The real path on disk to the dataset
+
+            config (BlendedMegatronDatasetConfig): The dataset config
+
+        Returns:
+            LowLevelDataset: The low level dataset
+        """
+        raise NotImplementedError
+
+    @staticmethod
+    def _key_config_attributes() -> List[str]:
+        """Return all config attributes which contribute to uniquely identifying the dataset.
+
+        These attributes will be used to build a uniquely identifying string and MD5 hash which
+        will be used to cache/load dataset resources from run to run.
+
+        Returns:
+            List[str]: The key config attributes
+        """
+        return ["random_seed", "sequence_length", "split", "split_matrix", "tokenizer"]
+
+    @abstractmethod
+    def __len__(self) -> int:
+        """Return the length of the dataset
+
+        Returns:
+            int: See abstract implementation
+        """
+        pass
+
+    @abstractmethod
+    def __getitem__(self, idx: int) -> Dict[str, Union[torch.Tensor, numpy.ndarray]]:
+        """Return from the dataset
+
+        Args:
+            idx (int): The index into the dataset
+
+        Returns:
+            Dict[str, Union[torch.Tensor, numpy.ndarray]]: See abstract implementation
+        """
+        pass

Megatron-LM/megatron/core/datasets/megatron_tokenizer.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+import json
+from abc import ABC, abstractmethod
+from collections import OrderedDict
+from typing import Any
+
+import numpy
+
+
+class MegatronTokenizer(ABC):
+    """Abstract class for tokenizer
+
+    Absent a config or class-specific tracking of which objects are uniquely identifying, we must
+    include all key word arguments as unique identifiers
+
+    Args:
+        tokenizer_paths (Tuple[str]): All tokenizer source paths or prefixes
+
+        tokenizer_options (Dict[str, Any]): All tokenizer options
+    """
+
+    def __init__(self, *tokenizer_paths: str, **tokenizer_options: Any):
+        self.unique_identifiers = OrderedDict()
+        self.unique_identifiers["class"] = type(self).__name__
+        self.unique_identifiers["tokenizer_path"] = list(tokenizer_paths)
+        for option in tokenizer_options:
+            self.unique_identifiers[option] = str(tokenizer_options[option])
+
+        self.unique_description = json.dumps(self.unique_identifiers, indent=4)
+
+        super().__init__()
+
+    @abstractmethod
+    def tokenize(self, text: str) -> numpy.ndarray:
+        """Convert text to embedding ids
+
+        Args:
+            text (str): The text to convert
+
+        Returns:
+            numpy.ndarray: The converted embedding ids
+        """
+        pass
+
+    def detokenize(self, ids: numpy.ndarray) -> str:
+        """Convert embedding ids to text
+
+        Args:
+            ids (numpy.ndarray): The ids to convert
+
+        Returns:
+            str: The converted text
+
+        Raises:
+            NotImplementedError: Non-abstract, optional method
+        """
+        raise NotImplementedError("{} has no method 'detokenize'".format(type(self).__name__))
+
+    def offsets(self, ids: list[int], text: str) -> list[int]:
+        """Convert embedding ids to text offsets
+
+        Args:
+            ids (list[int]): The ids to convert
+            text (str): The text to convert
+
+        Returns:
+            list[int]: The converted offsets
+
+        Raises:
+            NotImplementedError: Non-abstract, optional method
+        """
+        raise NotImplementedError("{} has no method 'offsets'".format(type(self).__name__))
+
+    @property
+    @abstractmethod
+    def vocab(self):
+        """Dictionary from vocab text token to id token"""
+        pass
+
+    @property
+    @abstractmethod
+    def inv_vocab(self):
+        """Dictionary from vocab id token to text token"""
+        pass
+
+    @property
+    @abstractmethod
+    def vocab_size(self):
+        """The vocabulary size"""
+        pass
+
+    @property
+    def cls(self):
+        """The CLS token id
+
+        Raises:
+            NotImplementedError: Non-abstract, optional attribute
+        """
+        raise NotImplementedError("{} has no attribute 'cls'".format(type(self).__name__))
+
+    @property
+    def sep(self):
+        """The SEP token id
+
+        Raises:
+            NotImplementedError: Non-abstract, optional attribute
+        """
+        raise NotImplementedError("{} has no attribute 'sep'".format(type(self).__name__))
+
+    @property
+    def pad(self):
+        """The PAD token id
+
+        Raises:
+            NotImplementedError: Non-abstract, optional attribute
+        """
+        raise NotImplementedError("{} has no attribute 'pad'".format(type(self).__name__))
+
+    @property
+    def eod(self):
+        """The EOD token id
+
+        Raises:
+            NotImplementedError: Non-abstract, optional attribute
+        """
+        raise NotImplementedError("{} has no attribute 'eod'".format(type(self).__name__))
+
+    @property
+    def bos(self):
+        """The BOS token id
+
+        Raises:
+            NotImplementedError: Non-abstract, optional attribute
+        """
+        raise NotImplementedError("{} has no attribute 'bos'".format(type(self).__name__))
+
+    @property
+    def eos(self):
+        """The EOS token id
+
+        Raises:
+            NotImplementedError: Non-abstract, optional attribute
+        """
+        raise NotImplementedError("{} has no attribute 'eos'".format(type(self).__name__))
+
+    @property
+    def mask(self):
+        """The MASK token id
+
+        Raises:
+            NotImplementedError: Non-abstract, optional attribute
+        """
+        raise NotImplementedError("{} has no attribute 'mask'".format(type(self).__name__))

Megatron-LM/megatron/core/datasets/multimodal_dataset.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+from dataclasses import dataclass
+from typing import Callable, Dict
+
+import torch
+
+from megatron.core.datasets.gpt_dataset import GPTDatasetConfig, MockGPTDataset
+
+
+@dataclass
+class MultimodalDatasetConfig(GPTDatasetConfig):
+    """Configuration object for Megatron Core Multimodal datasets.
+
+    Note: This is unused at the moment and may be missing features. Follow-up changes will use this.
+    """
+
+    image_h: int = None
+    """Image height."""
+
+    image_w: int = None
+    """Image width."""
+
+    # Function to preprocess the data sample to a format expected by a specific model. By default, do nothing.
+    preprocess_func: Callable[[Dict[str, torch.Tensor]], Dict[str, torch.Tensor]] = lambda x: x
+    """Optional function to preprocess data samples for a specific model."""
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+
+        assert self.image_h is not None
+        assert self.image_w is not None
+
+
+class MockMultimodalDataset(MockGPTDataset):
+    """Mock multimodal dataset.
+
+
+    This is unused at the moment and may be missing features. Follow-up changes will use this.
+    """
+
+    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
+        """Return a sample that contains a dummy image, text sequence and the associated labels and cost and attention masks.
+
+        Args:
+            idx (int): The integer seed for mock data generation.
+
+        Returns:
+            Dict[str, torch.Tensor]: The mock data.
+        """
+        # Get a text sample.
+        sample = super().__getitem__(idx)
+
+        # Add mock input image.
+        sample["image"] = torch.zeros(
+            (3, self.config.image_h, self.config.image_w), dtype=torch.float32
+        )
+
+        # Run optional data preprocessing.
+        preprocess_func = self.config.preprocess_func
+
+        return preprocess_func(sample)

Megatron-LM/megatron/core/datasets/object_storage_utils.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+import os
+from dataclasses import dataclass
+from typing import Any, Dict, Protocol, Tuple
+
+import torch
+
+try:
+    import boto3
+    import botocore.exceptions as exceptions
+except ModuleNotFoundError:
+    pass
+
+from megatron.core.msc_utils import MultiStorageClientFeature
+
+S3_PREFIX = "s3://"
+MSC_PREFIX = "msc://"
+
+
+@dataclass
+class ObjectStorageConfig:
+    """Config when the data (.bin) file and the index (.idx) file are in object storage
+
+    Attributes:
+
+        path_to_idx_cache (str): The local directory where we will store the index (.idx) file
+
+        bin_chunk_nbytes (int): If the number of bytes is too small, then we send a request to S3
+        at each call of the `read` method in _S3BinReader, which is slow, because each request
+        has a fixed cost independent of the size of the byte range requested. If the number of
+        bytes is too large, then we only rarely have to send requests to S3, but it takes a lot
+        of time to complete the request when we do, which can block training. We've found that
+        256 * 1024 * 1024 (i.e., 256 MiB) has worked well (though we have not put that much
+        effort into tuning it), so we default to it.
+    """
+
+    path_to_idx_cache: str
+
+    bin_chunk_nbytes: int = 256 * 1024 * 1024
+
+
+# S3Config is deprecated, use ObjectStorageConfig instead
+S3Config = ObjectStorageConfig
+
+
+class S3Client(Protocol):
+    """The protocol which all s3 clients should abide by"""
+
+    def download_file(self, Bucket: str, Key: str, Filename: str) -> None:
+        """Download the file from S3 to the local file system"""
+        ...
+
+    def upload_file(self, Filename: str, Bucket: str, Key: str) -> None:
+        """Upload the file to S3"""
+        ...
+
+    def head_object(self, Bucket: str, Key: str) -> Dict[str, Any]:
+        """Get the metadata of the file in S3"""
+        ...
+
+    def get_object(self, Bucket: str, Key: str, Range: str) -> Dict[str, Any]:
+        """Get the file from S3"""
+        ...
+
+    def close(self) -> None:
+        """Close the S3 client"""
+        ...
+
+
+def _remove_s3_prefix(path: str) -> str:
+    """Remove the S3 prefix from a path
+
+    Args:
+        path (str): The path
+
+    Returns:
+        str: The path without the S3 prefix
+    """
+    return path.removeprefix(S3_PREFIX)
+
+
+def _is_s3_path(path: str) -> bool:
+    """Ascertain whether a path is in S3
+
+    Args:
+        path (str): The path
+
+    Returns:
+        bool: True if the path is in S3, False otherwise
+    """
+    return path.startswith(S3_PREFIX)
+
+
+def _remove_msc_prefix(path: str) -> str:
+    """
+    Remove the MSC prefix from a path
+
+    Args:
+        path (str): The path
+
+    Returns:
+        str: The path without the MSC prefix
+    """
+    return path.removeprefix(MSC_PREFIX)
+
+
+def _is_msc_path(path: str) -> bool:
+    """Checks whether a path is in MSC path (msc://profile/path/to/file)
+
+    Args:
+        path (str): The path
+
+    Returns:
+        bool: True if the path is in MSC path, False otherwise
+    """
+    return path.startswith(MSC_PREFIX)
+
+
+def _s3_download_file(client: S3Client, s3_path: str, local_path: str) -> None:
+    """Download the object at the given S3 path to the given local file system path
+
+    Args:
+        client (S3Client): The S3 client
+
+        s3_path (str): The S3 source path
+
+        local_path (str): The local destination path
+    """
+    dirname = os.path.dirname(local_path)
+    os.makedirs(dirname, exist_ok=True)
+    parsed_s3_path = parse_s3_path(s3_path)
+    client.download_file(parsed_s3_path[0], parsed_s3_path[1], local_path)
+
+
+def _s3_object_exists(client: S3Client, path: str) -> bool:
+    """Ascertain whether the object at the given S3 path exists in S3
+
+    Args:
+        client (S3Client): The S3 client
+
+        path (str): The S3 path
+
+    Raises:
+        botocore.exceptions.ClientError: The error code is 404
+
+    Returns:
+        bool: True if the object exists in S3, False otherwise
+    """
+    parsed_s3_path = parse_s3_path(path)
+    try:
+        _ = client.head_object(bucket=parsed_s3_path[0], key=parsed_s3_path[1])
+    except exceptions.ClientError as e:
+        if e.response["Error"]["Code"] != "404":
+            raise e
+    return True
+
+
+def is_object_storage_path(path: str) -> bool:
+    """Ascertain whether a path is in object storage
+
+    Args:
+        path (str): The path
+
+    Returns:
+        bool: True if the path is in object storage (s3:// or msc://), False otherwise
+    """
+    return _is_s3_path(path) or _is_msc_path(path)
+
+
+def get_index_cache_path(idx_path: str, object_storage_config: ObjectStorageConfig) -> str:
+    """Get the index cache path for the given path
+
+    Args:
+        idx_path (str): The path to the index file
+
+        object_storage_config (ObjectStorageConfig): The object storage config
+
+    Returns:
+        str: The index cache path
+    """
+    if _is_s3_path(idx_path):
+        cache_idx_path = os.path.join(
+            object_storage_config.path_to_idx_cache, _remove_s3_prefix(idx_path)
+        )
+    elif _is_msc_path(idx_path):
+        cache_idx_path = os.path.join(
+            object_storage_config.path_to_idx_cache, _remove_msc_prefix(idx_path)
+        )
+    else:
+        raise ValueError(f"Invalid path: {idx_path}")
+
+    return cache_idx_path
+
+
+def parse_s3_path(path: str) -> Tuple[str, str]:
+    """Parses the given S3 path returning correspsonding bucket and key.
+
+    Args:
+        path (str): The S3 path
+
+    Returns:
+        Tuple[str, str]: A (bucket, key) tuple
+    """
+    assert _is_s3_path(path)
+    parts = path.replace(S3_PREFIX, "").split("/")
+    bucket = parts[0]
+    if len(parts) > 1:
+        key = "/".join(parts[1:])
+        assert S3_PREFIX + bucket + "/" + key == path
+    else:
+        key = ""
+    return bucket, key
+
+
+def get_object_storage_access(path: str) -> str:
+    """Get the object storage access"""
+    return "s3" if _is_s3_path(path) else "msc"
+
+
+def dataset_exists(path_prefix: str, idx_path: str, bin_path: str) -> bool:
+    """Check if the dataset exists on object storage
+
+    Args:
+        path_prefix (str): The prefix to the index (.idx) and data (.bin) files
+
+        idx_path (str): The path to the index file
+
+        bin_path (str): The path to the data file
+
+    Returns:
+        bool: True if the dataset exists on object storage, False otherwise
+    """
+    if _is_s3_path(path_prefix):
+        s3_client = boto3.client("s3")
+        return _s3_object_exists(s3_client, idx_path) and _s3_object_exists(s3_client, bin_path)
+    elif _is_msc_path(path_prefix):
+        msc = MultiStorageClientFeature.import_package()
+        return msc.exists(idx_path) and msc.exists(bin_path)
+    else:
+        raise ValueError(f"Invalid path: {path_prefix}")
+
+
+def cache_index_file(remote_path: str, local_path: str) -> None:
+    """Download a file from object storage to a local path with distributed training support.
+    The download only happens on Rank 0, and other ranks will wait for the file to be available.
+
+    Note that this function does not include any barrier synchronization. The caller (typically
+    in blended_megatron_dataset_builder.py) is responsible for ensuring proper synchronization
+    between ranks using torch.distributed.barrier() after this function returns.
+
+    Args:
+        remote_path (str): The URL of the file to download (e.g., s3://bucket/path/file.idx
+            or msc://profile/path/file.idx)
+        local_path (str): The local destination path where the file should be saved
+
+    Raises:
+        ValueError: If the remote_path is not a valid S3 or MSC path
+    """
+    torch_dist_enabled = torch.distributed.is_initialized()
+
+    if torch_dist_enabled:
+        rank = torch.distributed.get_rank()
+    else:
+        rank = 0
+
+    if _is_s3_path(remote_path):
+        s3_client = boto3.client("s3")
+
+        if not torch_dist_enabled or rank == 0:
+            _s3_download_file(s3_client, remote_path, local_path)
+
+        assert os.path.exists(local_path)
+    elif _is_msc_path(remote_path):
+        msc = MultiStorageClientFeature.import_package()
+
+        if not torch_dist_enabled or rank == 0:
+            msc.download_file(remote_path, local_path)
+
+        assert os.path.exists(local_path)
+    else:
+        raise ValueError(f"Invalid path: {remote_path}")

Megatron-LM/megatron/core/datasets/readme.md

+# Data Pipeline
+
+## Data pre-processing
+
+Data preprocessing is built around the following classes:
+
+1. `IndexedDatasetBuilder`
+2. `IndexedDataset`
+
+At the moment, an end-to-end data preprocessing implementation is left to the user. See the class docstring(s) for more details.
+
+#### IndexedDatasetBuilder
+
+The `IndexedDatasetBuilder` is capable of building and merging `IndexedDataset` instances.
+
+#### IndexedDataset
+
+The `IndexedDataset` class is the lowest-level data interface in Megatron Core. Internally, an `IndexedDataset` instance references two binaries: the data file (`.bin`) contains document/sequence data and the index file (`.idx`) contains document/sequence metadata.
+
+The index file stores dataset-level metadata first:
+- The index header, for backward compatibility
+- The index version, for backward compatibility
+- A numeric code corresponding to the data type used to write data to the data file
+- The number of sequences in the dataset
+- The number of documents in the dataset
+
+The index file stores document-level and sequence-level metadata second:
+- In order, the number of elements per sequence
+- In order, the byte offset (pointer) per sequence
+- In order, the consecutive sequence index range `[...)` per document
+- In order, the mode per sequence (in the multimodal case)
+
+## Data loading: construction
+
+Building the data loaders is a distributed-aware process built around the following classes:
+
+1. `BlendedMegatronDatasetConfig`
+2. `BlendedMegatronDatasetBuilder`
+3. `IndexedDataset`
+3. `MegatronDataset`
+4. `BlendedDataset`
+
+See the class docstrings for more details.
+
+#### BlendedMegatronDatasetConfig (extendable)
+
+The `BlendedMegatronDatasetConfig` class parameterizes the `BlendedMegatronDatasetBuilder` and in turn the `MegatronDataset` and `BlendedDataset`.
+
+Different training/inference regimes will require different extensions e.g. the `GPTDatasetConfig`
+
+#### BlendedMegatronDatasetBuilder
+
+The `BlendedMegatronDatasetBuilder` class builds the highest-level data interfaces in Megatron Core.
+
+**NB:** All ranks should attempt to build the dataset via the `BlendedMegatronDatasetBuilder` or the program will hang. Which ranks follow through on their attempts can be controlled via the `BlendedMegatronDatasetConfig`.
+
+#### IndexedDataset
+
+The `IndexedDataset` class is the lowest-level data interface in Megatron Core.
+
+The `IndexedDataset` should already exist on disk before attempting to build any of the high-level data interfaces.
+
+
+#### MegatronDataset (extendable)
+
+The `MegatronDataset` abstract class is a high-level data interface in Megatron Core. It is an abstraction built upon the `IndexedDataset`.
+
+Different training/inference regimes will require different extensions e.g. the `GPTDataset`
+
+#### BlendedDataset
+
+The `BlendedDataset` class is a high-level data interface in Megatron Core. It is an abstraction built upon the `MegatronDataset`.
+
+The `BlendedDataset` is only necessary when a blend multiple data distributions, i.e. multiple `MegatronDataset` instances, should contribute to a certain dataset split. The blend can be controlled via the `BlendedMegatronDatasetConfig`.
+
+## Data loading: implementation
+
+### GPTDataset
+
+The `GPTDataset` is parameterized by the following variables: the underlying `IndexedDataset` instance `indexed_dataset`, the split indices `indexed_indices` (the congituous subset of document or sequence indices used for training, validation, and testing), the number of samples `N`, the sequence length `S`, and the random seed `R`.
+
+The `GPTDataset` creates three index mappings to facilitate lookup: (1) the document index, (2) the sample index, and (3) the shuffle index.
+
+1. The document index _Do_idx_ is a 1-D array mapping from _i_ to document index of length `E * |indexed_indices|` where `E` corresponds to the minimum number of epochs such that `E * |indexed_indices| >= N`. The document index is shuffled according to `R`.
+
+    ```
+    Given:
+
+    N = 15
+    indexed_indices = [5, 6, 7, 8, 9]
+    E = 3
+
+    Then, for example:
+
+    Do_idx = [8, 8, 9, 6, 7, 5, 8, 5, 6, 6, 5, 9, 7, 7, 9]
+    ```
+
+2. The sample index _Sa_idx_ is a 2-D array mapping from _j_ to pairs of (_i_, _Do_idx_[ _i_ ] offset) of shape `[N + 1, 2]`. The rows _j_ and _j_ + 1 serve as the left and right bounds for the _j_-th sample. 
+
+    ```
+    Given:
+
+    S = 1024
+
+    Then, for example:
+
+    Sa_idx[0] = (0, 0)
+    Sa_idx[1] = (0, 1024)       => Do_idx[0] has length greater than S
+    Sa_idx[2] = (1, 512)        => Do_idx[0] has length 1536
+    Sa_idx[3] = (2, 0)          => Do_idx[1] has length 1536
+    Sa_idx[4] = (5, 300)        => Do_idx[2:5] are shorter documents relative to Do_idx[0:2]
+    Sa_idx[5] = (6, 24)         => Do_idx[5] has length 1300
+    ```
+
+3. The shuffle index _Sh_idx_ is a 1-D array mapping from _k_ to _j_ of length `N`. The shuffle index is shuffled according to `R`.
+
+    ```
+    Given
+
+    N = 10
+
+    Then, for example:
+
+    Sh_idx = [4, 0, 2, 6, 1, 9, 5, 8, 7, 3]
+    ```
+
+To query the `GPTDataset` for the _k_-th sample we do the following
+
+-  Use the shuffle index to get the index _j_ into the sample index.
+
+    ```
+    j = Sh_idx[k]
+    ```
+- Use the sample index to get the left and right sample-bounding indices into the document index and the starting token offset for each document.
+
+    ```
+    i, offset = Sa_idx[j]
+    i_next, offset_next = Sa_idx[j + 1]
+    ```
+- Use the document index to retrieve `S` tokens from consecutive (in the document index) documents.
+
+    ```
+    sample = []
+    sample += indexed_dataset[Do_idx[i]][offset:]
+    if i != i_next:
+        sample += indexed_dataset[Do_idx[i + 1:i_next]]
+    sample += indexed_dataset[Do_idx[i_next]][:offset_next]
+    ```
+
+To save time during initialization, each index is built/cached sequentially on one process rank and subsequently loaded in parallel on other process ranks. The cached indices are unique to a hash generated in the `MegatronDataset.__init__` function.
+
+### BlendedDataset
+
+The `BlendedDataset` is parameterized by the following variables: the underlying `MegatronDataset` instances `D`, the weights `W` (one per dataset), and the size `S`. The `BlendedDataset` will draw samples from contributing datasets in proportion to the weights until achieving a composite dataset of the desired size. During each sampling step, we draw a single sample from the dataset which has the greatest sampling error.
+
+The `BlendedDataset` creates two "blending" indices to facilitate lookup: (1) the dataset index and (2) the dataset sample index.
+
+1. The dataset index _Da_idx_ is a 1-D array mapping from _i_ to dataset index of length `S`.
+
+    ```
+    Given
+
+    D = [d0, d1, d2]
+    W = [1/2, 1/4, 1/4]
+    S = 4
+
+    Then, for example:
+
+    Da_idx = [0, 1, 2, 0]
+
+    ```
+
+2. The dataset sample index _Sa_idx_ is a 1-D mapping from _i_ to the sample index for dataset _Da_idx[i]_ of length `S`.
+
+    ```
+    Given
+
+    Da_idx = [0, 1, 2, 0]
+
+    Then, for example:
+
+    Sa_idx = [0, 0, 0, 1]
+    ```
+
+To query the `BlendedDataset` for the _k_-th sample we do the following
+
+- Use the dataset index to retrieve the corresponding dataset from `D` and the dataset sample index to retrieve the corresponding sample from that dataset.
+
+    ```
+    sample = D[Da_idx[k]][Sa_idx[k]]
+    ```
+
+To save time during initialization, each index is built/cached sequentially on one process rank and subsequently loaded in parallel on other process ranks. The cached indices are unique to a hash generated in the `BlendedDataset.__init__` function.

Megatron-LM/megatron/core/datasets/retro/__init__.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+from .config import RetroGPTChunkDatasets
+from .query.multi_split_gpt_dataset import MultiSplitGPTDataset, MultiSplitGPTDatasetConfig
+from .query.retro_dataset import get_retro_datasets

Megatron-LM/megatron/core/datasets/retro/config/__init__.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""
+Exports:
+
+  - Embedder: Base class for all Bert embedders.
+  - RetroBertEmbedders: Container class for in-memory and on-disk embedders.
+  - RetroPreprocessingConfig: Configuration class for all of Retro preprocessing.
+  - RetroGPTChunkDatasets: Container class for train, valid, and test datasets.
+  - RetroTokenizers: Container class for GPT and Bert tokenizers.
+"""
+
+from .bert_embedders import Embedder, RetroBertEmbedders
+from .config import RetroPreprocessingConfig
+from .gpt_chunk_datasets import RetroGPTChunkDatasets
+from .tokenizers import RetroTokenizers

Megatron-LM/megatron/core/datasets/retro/config/bert_embedders.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""Container dataclass for holding both in-memory and on-disk Bert embedders."""
+
+import abc
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+
+
+class Embedder(abc.ABC):
+    """Base class for all Bert embedders.
+
+    All embedders should be able to embed either an entire text dataset (to a 2D
+    numpy array), or a single text string (to a 1D numpy array).
+    """
+
+    @abc.abstractmethod
+    def embed_text_dataset(self, text_dataset: torch.utils.data.Dataset) -> np.ndarray:
+        """Embed a text dataset.
+
+        Args:
+            text_dataset (torch.utils.data.Dataset): Text dataset to embed.
+                Each sample of the text dataset should output a dict with a key 'text'
+                and a string value.
+
+        Returns:
+            A 2D ndarray with shape (len(text_dataset), dimension(embedder)).
+        """
+
+    @abc.abstractmethod
+    def embed_text(self, text: str) -> np.ndarray:
+        """Embed a simple string of text.
+
+        Args:
+            text (str): A single text sample.
+
+        Returns:
+            A 1D ndarray with shape (dimensions(embedder),).
+        """
+
+
+@dataclass
+class RetroBertEmbedders:
+    """Container dataclass for in-memory and on-disk Bert embedders."""
+
+    disk: Embedder
+    mem: Embedder

Megatron-LM/megatron/core/datasets/retro/config/config.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""Retro preprocessing config."""
+
+from dataclasses import dataclass
+
+from megatron.core.transformer import TransformerConfig
+
+from .bert_embedders import RetroBertEmbedders
+from .gpt_chunk_datasets import RetroGPTChunkDatasets
+from .tokenizers import RetroTokenizers
+
+
+@dataclass
+class RetroPreprocessingConfig(TransformerConfig):
+    """Configuration object for Retro preprocessing.
+
+    *Note* : Arguments prefixed with '--retro-gpt-*' or '--retro-bert-*' are
+    included and named as such to more easily handle managing both models
+    running at the same time. Megatron is not optimized to run two models at
+    once, so this naming convention makes it clearer.
+
+    Args:
+
+        retro_project_dir (str): Retro project directory, which contains the preprocessed data for for pretraining. This directory is built during preprocessing (see tools/retro/README.md), and contains subdirectories for the chunk database and pretraining neighbors.
+        retro_tasks (str): Comma-separated list of tasks to run. Run entire preprocesing pipeline by using '--retro-tasks build'. Alternatively, run individual stages with tasks (in this order) 'db-build', 'index-build', or 'query-pretraining-neighbors'. For example, '--retro-tasks db-build,index-build,query-pretraining-neighbors' is equivalent to '--retro-tasks build'; or the argument can contain a subset of these tasks. Stages must always be run in the correct order (listed above).
+        retro_task_validate (float): If defined, validate a randomly sampled subset of the existing results of the given task. Each task implements a 'validate' method that is responsible for sampling a `retro_task_validate` fraction of the existing results, and then checking for bitwise equality with the current code base. (E.g., `--retro-task-validate 0.01`.)
+        retro_block_size (int): Number of chunks to process at a time when generating Bert embeddings and querying the search index. Partial results for each block are generally saved to disk in separate files.
+        retro_doc_block_size (int): Number of documents to processe at time when processing token datasets into chunk databases. The partial chunk database for each block is saved into a separate file.
+        retro_gpt_seed (int): Random seed used for python, numpy, pytorch, and cuda.
+        retro_gpt_data_path (str): Path to the training dataset. Accepted format: 1) a single data path, 2) multiple datasets in the form: dataset1-weight dataset1-path dataset2-weight dataset2-path ... It is used with --split when a single dataset used for all three: train, valid and test. It is exclusive to the other --*-data-path args.
+        retro_gpt_data_cache_path (str): Path to a directory to hold cached index files.
+        retro_gpt_split (str): Comma-separated list of proportions for training, validation, and test split. For example the split `90,5,5` will use 90%% of data for training, 5%% for validation and 5%% for test.
+        retro_gpt_train_samples (int): Total number of samples to train over all training runs.
+        retro_gpt_eval_interval (int): GPT evaluation interval.
+        retro_gpt_eval_iters (int): GPT evaluation iterations.
+        retro_gpt_tokenizer_type (str): GPT tokenizer type.
+        retro_gpt_tokenizer_model (str): GPT tokenizer model file.
+        retro_gpt_vocab_file (str): GPT vocab file.
+        retro_gpt_merge_file (str): GPT merge file.
+        retro_gpt_seq_length (int): GPT sequence length.
+        retro_gpt_global_batch_size (int): GPT global batch size.
+        retro_gpt_chunk_length (int): GPT chunk length.
+        retro_bert_tokenizer_type (str): Bert tokenizer type (for when using '--bert-embedder-type megatron').
+        retro_bert_vocab_file (str): Bert vocab file.
+        retro_bert_batch_size (int): Micro-batch size for processing Bert embeddings.
+        retro_bert_max_chunk_length (int): Maximum sequence length for Bert embeddings. (Named 'chunk' here in reference to these Bert sequences being converted from GPT chunks.)
+        retro_index_type (str): A 'faiss-base' index is a simple, un-optimized wrapper around a Faiss index. A 'faiss-par-add' index optimizes the 'add()' method by making it multi-node and multi-process, but with bit-wise equivalent results.
+        retro_index_str (str): Index string used for calling faiss.index_factory(). For example, 'IVF262144_HNSW32,Flat' or 'OPQ32_256,IVF4194304_HNSW32,PQ32'.
+        retro_index_ntrain (int): Number of database chunks to use for training the index. This value must be less or equal to the total number of chunks in the database.
+        retro_index_train_load_fraction (float): Fraction of sampled chunks to use for training the index. Useful when our total sampled embeddings use too much memory; lowering the load fraction is less costly than re-embedding a new sampled dataset from scratch.
+        retro_index_add_load_fraction (float): Fraction of database chunks to use for adding to the index. Useful when our total index size would use too much memory; lowering the load fraction is less costly than re-designing our token datasets.
+        retro_index_delete_training_embeddings (bool): Delete training embeddings for the search index. Useful for debugging.
+        retro_index_delete_added_codes (bool): Delete added codes for the search index. Useful for debugging.
+        retro_query_ef_search (int): Index ef-search parameter for Hierarchical Navigable Small Worlds (HNSW) during querying.
+        retro_query_nprobe (int): Index nprobe parameter for Inverted File (IVF) during querying.
+        retro_query_num_neighbors_query (int): Number of neighbors to retrieve when calling index.search().
+        retro_query_num_neighbors_save (int): Number of neighbors to save to disk after the index's returned neighbors. If longer than target value, neighbors truncated; and if shorter than target value, neighbors are padded with -1's.
+        retro_bert_embedders (RetroBertEmbedders): Set of Bert embedders used for embedding chunks. Contains entries: 1) 'mem' for an in-memory embedder, and 2) 'disk' for an embedder that saves results in blocks to disk.
+        retro_gpt_chunk_datasets (RetroGPTChunkDatasets): GPT datasets for 'train', 'valid', and 'test'.
+        retro_tokenizers (RetroTokenizers): GPT ('gpt') and Bert ('bert') tokenizers.
+    """
+
+    # Basic.
+    retro_project_dir: str = None
+    retro_tasks: str = 'build'
+    retro_task_validate: float = None
+    retro_block_size: int = 100000
+    retro_doc_block_size: int = 100000
+
+    # GPT.
+    retro_gpt_seed: int = 1234
+    retro_gpt_data_path: list = None  # basic list here, for parsing purposes
+    retro_gpt_data_cache_path: str = None
+    retro_gpt_split: str = '969,30,1'
+    retro_gpt_train_samples: int = None
+    retro_gpt_eval_interval: int = None
+    retro_gpt_eval_iters: int = None
+    retro_gpt_tokenizer_type: str = None
+    retro_gpt_tokenizer_model: str = None
+    retro_gpt_vocab_file: str = None
+    retro_gpt_merge_file: str = None
+    retro_gpt_seq_length: int = None
+    retro_gpt_global_batch_size: int = None
+    retro_gpt_chunk_length: int = 64
+
+    # Bert.
+    retro_bert_tokenizer_type: str = None
+    retro_bert_vocab_file: str = None
+    retro_bert_batch_size: int = 128
+    retro_bert_max_chunk_length: int = 256
+
+    # Index.
+    retro_index_type: str = 'faiss-par-add'
+    retro_index_str: str = None
+    retro_index_ntrain: int = None
+    retro_index_train_load_fraction: float = 1.0
+    retro_index_add_load_fraction: float = 1.0
+    retro_index_delete_training_embeddings: bool = True
+    retro_index_delete_added_codes: bool = True
+
+    # Query.
+    retro_query_ef_search: int = 256
+    retro_query_nprobe: int = 65536
+    retro_query_num_neighbors_query: int = 200
+    retro_query_num_neighbors_save: int = 20
+
+    # Tools.
+    retro_bert_embedders: RetroBertEmbedders = None
+    retro_gpt_chunk_datasets: RetroGPTChunkDatasets = None
+    retro_tokenizers: RetroTokenizers = None
+
+    def __post_init__(self) -> None:
+        """Validate Retro config."""
+
+        # Validate required attributes.
+        assert self.retro_project_dir is not None
+        assert self.retro_tasks is not None
+        assert self.retro_gpt_data_path is not None or self.retro_gpt_data_cache_path is not None
+        assert self.retro_gpt_train_samples is not None
+        assert self.retro_gpt_eval_interval is not None
+        assert self.retro_gpt_eval_iters is not None
+        assert self.retro_gpt_tokenizer_type is not None
+        assert self.retro_gpt_tokenizer_model is not None or (
+            self.retro_gpt_vocab_file is not None and self.retro_gpt_merge_file is not None
+        )
+        assert self.retro_gpt_seq_length is not None
+        assert self.retro_gpt_global_batch_size is not None
+        assert self.retro_bert_tokenizer_type is not None
+        assert self.retro_bert_vocab_file is not None
+        assert self.retro_index_str is not None
+        assert self.retro_index_ntrain is not None
+
+        # Split retro tasks.
+        self.retro_tasks = self.retro_tasks.split(",")

Megatron-LM/megatron/core/datasets/retro/config/gpt_chunk_datasets.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""Container dataclass for GPT chunk datasets (train, valid, and test)."""
+
+from dataclasses import dataclass
+
+
+@dataclass
+class RetroGPTChunkDatasets:
+    """Container dataclass for GPT chunk datasets."""
+
+    # Each dict contains 'dataset', 'neighbor_dir', and 'num_active_chunks'.
+    train: dict = None
+    valid: dict = None
+    test: dict = None

Megatron-LM/megatron/core/datasets/retro/config/tokenizers.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""Container class for GPT and Bert tokenizers."""
+
+from dataclasses import dataclass
+
+from megatron.core.datasets.megatron_tokenizer import MegatronTokenizer
+
+
+@dataclass
+class RetroTokenizers:
+    """Container class for GPT and Bert tokenizers."""
+
+    gpt: MegatronTokenizer = None
+    bert: MegatronTokenizer = None

Megatron-LM/megatron/core/datasets/retro/db/__init__.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""
+Exports:
+
+  - build_db: Build a chunk database from a list of indexed datasets.
+"""
+
+from .build import build_db

Megatron-LM/megatron/core/datasets/retro/db/build.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""Build a chunk database from a list of indexed datasets.
+
+Building a chunk database consists of.
+
+  - Breaking each document of each indexed dataset into consecutive
+      retro_gpt_chunk_length chunks.
+  - Re-tokenize each chunk into Bert, and discard any chunks with empty Bert
+      tokens.
+  - Save chunk offsets to disk for each indexed dataset.
+"""
+
+import os
+import types
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from typing import Dict, List, Tuple
+
+import numpy as np
+import torch
+
+from megatron.core.datasets.indexed_dataset import IndexedDataset
+from megatron.core.datasets.retro.config import RetroPreprocessingConfig
+from megatron.core.datasets.retro.utils import (
+    extract_data_config,
+    get_blocks_by_rank,
+    log_retro_rank_0,
+    retro_makedir,
+)
+
+from .utils import (
+    get_indexed_dataset_infos,
+    get_indexed_dataset_infos_path,
+    get_individual_chunk_db,
+    get_individual_db_dir,
+    get_individual_db_paths,
+    get_individual_doc_offsets,
+    get_merged_db_path_map,
+    init_indexed_dataset_infos,
+    save_indexed_dataset_infos,
+)
+
+try:
+    from tqdm import tqdm
+
+    HAVE_TQDM = True
+except ImportError:
+    HAVE_TQDM = False
+
+try:
+    import h5py
+
+    HAVE_H5PY = True
+except ImportError:
+    HAVE_H5PY = False
+
+
+def build_partial_db(
+    config: types.SimpleNamespace,
+    dataset_idx: int,
+    n_datasets: int,
+    indexed_dataset: IndexedDataset,
+    block_id: int,
+    n_blocks: int,
+    block: dict,
+    proc_id: int,
+    n_procs: int,
+) -> Tuple[int, list, list, dict]:
+    """Process a document index range of the indexed dataset.
+
+    The chunk database is built in parallel blocks, since de-tokenizing &
+    re-tokenizing for Bert-length computation is expensive. This method
+    iterates each document and extracts sequential 'chunk-length' sequences
+    from each document.
+
+    Args:
+        config (types.SimpleNamespace): Subset of Retro config, containing
+            'chunk_length', 'gpt_eod', 'gpt_detokenize', 'bert_tokenize', and 'task_validate'.
+        dataset_idx (int): Index of this dataset out of all blended datasets.
+        n_datasets (int): Total number of blended datasets.
+        indexed_dataset (IndexedDataset): Indexed dataset to be chunked.
+        block_id (int): Block index out of all blocks to be processed.
+        n_blocks (int):  Total number of blocks to be processed.
+        block (dict): Range information such as start/end points for chunking idnexed dataset.
+        proc_id (int): Process ID for tracking parallel process order.
+        n_procs (int): Total number of parallel processes.
+
+    Returns:
+        A tuple containing:
+
+        - Process ID.
+        - List of valid chunks.
+        - List of invalid chunks (i.e., chunks that converted to empty Bert embeddings.).
+        - Dict mapping document ID to number of valid chunks.
+    """
+
+    if not HAVE_TQDM:
+        raise ImportError("tqdm is required to use the RetroDataset. Please install tqdm.")
+
+    # Document start/end indexes.
+    doc_range = block["range"]
+    n_docs = doc_range[1] - doc_range[0]
+    n_docs_per_proc = int(np.ceil(n_docs / n_procs))
+    doc_start_id = doc_range[0] + proc_id * n_docs_per_proc
+    doc_end_id = min(doc_range[1], doc_start_id + n_docs_per_proc)
+
+    # Print progress.
+    progress_proc_ids = set(range(n_procs)) if torch.distributed.get_rank() == 0 else set()
+    if proc_id in progress_proc_ids:
+        log_retro_rank_0(
+            " > building partial chunk db, proc %d / %d, docs %d:%d / %d."
+            % (proc_id, n_procs, doc_start_id, doc_end_id, n_docs)
+        )
+
+    # Progress bars (snapshot of overall progress).
+    doc_id_iter = range(doc_start_id, doc_end_id)
+    pbar = (
+        tqdm(doc_id_iter, "parse doc chunks", miniters=len(doc_id_iter) // 20)
+        if proc_id in progress_proc_ids
+        else doc_id_iter
+    )
+
+    # Iterate documents & parse chunks.
+    chunk_db_valid: List[Tuple] = []
+    chunk_db_invalid: List[Tuple] = []
+    doc_size_map = {}
+    for doc_id in pbar:
+        # Progress description.
+        try:
+            pbar.set_description(
+                "%sds %d / %d, block %d / %d, proc %d / %d."
+                % (
+                    "" if config.task_validate is None else "[validate] ",
+                    dataset_idx,
+                    n_datasets,
+                    block_id,
+                    n_blocks,
+                    proc_id,
+                    n_procs,
+                )
+            )
+        except Exception:
+            pass
+
+        # Remove EOD token.
+        doc = indexed_dataset.get(doc_id)
+        if doc[-1].item() == config.gpt_eod:
+            doc = doc[:-1]
+        doc_len = len(doc)
+
+        # Chunk start/end indexes.
+        chunk_start_idxs = list(range(0, doc_len, config.chunk_length))
+        chunk_end_idxs = [min(doc_len, s + config.chunk_length) for s in chunk_start_idxs]
+
+        # Re-tokenize each chunk to Bert/Wordpiece (empty bert -> 'invalid').
+        doc_size_map[doc_id] = 0
+        for i, chunk_start_idx in enumerate(chunk_start_idxs):
+            # Re-tokenize.
+            chunk_end_idx = chunk_end_idxs[i]
+            gpt_token_ids = indexed_dataset.get(
+                idx=doc_id, offset=chunk_start_idx, length=chunk_end_idx - chunk_start_idx
+            )
+            text = config.gpt_detokenize(gpt_token_ids.tolist())
+            bert_token_ids = config.bert_tokenize(text)
+
+            # 'Valid' for non-empty Bert chunks; 'invalid' otherwise.
+            if len(bert_token_ids) == 0:
+                _chunk_db = chunk_db_invalid
+            else:
+                _chunk_db = chunk_db_valid
+                doc_size_map[doc_id] += 1
+            _chunk_db.append((doc_id, chunk_start_idx, chunk_end_idx, len(bert_token_ids)))
+
+    return proc_id, chunk_db_valid, chunk_db_invalid, doc_size_map
+
+
+def build_block_db(
+    config: RetroPreprocessingConfig,
+    dataset_idx: int,
+    n_datasets: int,
+    indexed_dataset: IndexedDataset,
+    n_procs: int,
+    executor: ProcessPoolExecutor,
+    n_missing_blocks: int,
+    block_idx: int,
+    block: dict,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Split each document within block into consecutive retro_gpt_chunk_length size chunks.
+
+    Args:
+        config (RetroPreprocessingConfig): For DB building, we make use of attributes
+            'chunk_length', 'gpt_eod', 'gpt_detokenize', 'bert_tokenize', and 'task_validate'.
+        dataset_idx (int): Index of this dataset out of all blended datasets.
+        n_datasets (int): Total number of blended datasets.
+        indexed_dataset (IndexedDataset): Indexed dataset to be chunked.
+        n_procs (int): Total number of parallel processes.
+            executor (ProcessPoolExecutor): Executor for launching parallel processes.
+        n_missing_blocks (int):  Total number of blocks to be processed.
+        block_idx (int): Block index out of all blocks to be processed.
+        block (dict): Range information such as start/end points for chunking idnexed dataset.
+
+    Returns:
+        A tuple containing:
+
+        - List of valid chunks.
+        - List of invalid chunks (i.e., chunks that converted to empty Bert embeddings.).
+        - Dict mapping document ID to number of valid chunks.
+    """
+
+    # Build partial dbs.
+    log_retro_rank_0(" > build partial dbs.")
+    futures = []
+    for proc_id in range(n_procs):  # not true process id
+        futures.append(
+            executor.submit(
+                build_partial_db,
+                types.SimpleNamespace(
+                    chunk_length=config.retro_gpt_chunk_length,
+                    gpt_eod=config.retro_tokenizers.gpt.eod,
+                    gpt_detokenize=config.retro_tokenizers.gpt.detokenize,
+                    bert_tokenize=config.retro_tokenizers.bert.tokenize,
+                    task_validate=config.retro_task_validate,
+                ),
+                dataset_idx,
+                n_datasets,
+                indexed_dataset,
+                block_idx,
+                n_missing_blocks,
+                block,
+                proc_id,
+                n_procs,
+            )
+        )
+    partial_chunk_dbs = []
+    for future in as_completed(futures):
+        partial_chunk_dbs.append(future.result())
+
+    # Concatenate chunks.
+    partial_chunk_dbs.sort(key=lambda item: item[0])  # sort by proc_id
+    chunk_db_valid = [
+        item for partial_chunk_db in partial_chunk_dbs for item in partial_chunk_db[1]
+    ]
+    chunk_db_invalid = [
+        item for partial_chunk_db in partial_chunk_dbs for item in partial_chunk_db[2]
+    ]
+
+    # Convert to numpy.
+    log_retro_rank_0(" > converting chunk db to numpy.")
+    chunk_db_valid = np.array(chunk_db_valid, dtype="uint32")
+    chunk_db_invalid = np.array(chunk_db_invalid, dtype="uint32")
+
+    # Document offsets.
+    doc_sizes = [
+        (d, s) for partial_chunk_db in partial_chunk_dbs for d, s in partial_chunk_db[3].items()
+    ]
+    doc_sizes.sort(key=lambda item: item[0])
+    doc_offsets = np.cumsum([item[1] for item in doc_sizes]).astype("uint64")
+    doc_offsets = np.stack(
+        (np.array([item[0] for item in doc_sizes], dtype="uint64"), doc_offsets), axis=1
+    )
+
+    return chunk_db_valid, chunk_db_invalid, doc_offsets
+
+
+def save_block_db(
+    block: dict, chunk_db_valid: np.ndarray, chunk_db_invalid: np.ndarray, doc_offsets: np.ndarray
+) -> None:
+    """Save block of chunked tokens to disk. These blocks are later used for
+    training and adding to the vector index.
+
+    Args:
+        block (dict): Range information such as start/end points for chunking idnexed dataset.
+        chunk_db_valid (np.ndarray): Array of valid chunk indexes.
+        chunk_db_invalid (np.ndarray): Array of invalid chunk indexes.
+        doc_offsets (np.ndarray): Array of document offsets by chunks.
+    """
+    if not HAVE_H5PY:
+        raise ImportError("h5py is required to use the RetroDataset. Please install h5py.")
+
+    log_retro_rank_0(" > saving individual db.")
+    with h5py.File(block["path"], "w") as f:
+        dset = f.create_dataset("chunks_valid", data=chunk_db_valid)
+        dset = f.create_dataset("chunks_invalid", data=chunk_db_invalid)
+        dset = f.create_dataset("doc_offsets", data=doc_offsets)
+
+
+def build_individual_db(
+    config: RetroPreprocessingConfig, dataset_idx: int, n_datasets: int, dataset_info: dict
+) -> None:
+    """Process a single indexed dataset & extract chunks.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+        dataset_idx (int): Dataset index within blended dataset.
+        n_datasets (int): Total number of datasets within blended dataset.
+        dataset_info (dict): Metadata for dataset
+            (see `save_indexed_dataset_infos()` in `utils.py` for more detail).
+    """
+
+    # Make directory.
+    db_dir = get_individual_db_dir(config.retro_project_dir, dataset_info["prefix"])
+    retro_makedir(config, db_dir)
+
+    # Indexed dataset.
+    indexed_dataset = dataset_info["dataset"]
+
+    # Missing DB blocks (split by documents).
+    blocks = get_blocks_by_rank(
+        db_dir,
+        len(indexed_dataset),
+        config.retro_doc_block_size,
+        validate=lambda f: f["chunks_valid"].shape == (0,) or f["chunks_valid"].shape[1] == 4,
+        sample=config.retro_task_validate,
+    )
+    if config.retro_task_validate is None:
+        active_blocks = blocks.missing
+    else:
+        assert blocks.n_missing_world == 0
+        active_blocks = blocks.existing
+
+    # Prevent missing-path-write race condition.
+    torch.distributed.barrier()
+
+    # Nothing to do?
+    if config.retro_task_validate is None and not active_blocks:
+        return
+
+    # Num processes.
+    if blocks.n_missing_world == 1:
+        n_procs = 128
+    elif blocks.n_missing_world <= 2:
+        n_procs = 64
+    elif blocks.n_missing_world <= 4:
+        n_procs = 32
+    elif blocks.n_missing_world <= 8:
+        n_procs = 16
+    else:
+        n_procs = 8
+
+    # Process documents in parallel.
+    with ProcessPoolExecutor(max_workers=n_procs) as executor:
+        for block_idx, block in enumerate(active_blocks):
+            if block is not None:
+                # Build block DB.
+                chunk_db_valid, chunk_db_invalid, doc_offsets = build_block_db(
+                    config=config,
+                    dataset_idx=dataset_idx,
+                    n_datasets=n_datasets,
+                    indexed_dataset=indexed_dataset,
+                    n_procs=n_procs,
+                    executor=executor,
+                    n_missing_blocks=len(active_blocks),
+                    block_idx=block_idx,
+                    block=block,
+                )
+
+                if config.retro_task_validate is None:
+                    # Save block DB.
+                    save_block_db(
+                        block=block,
+                        chunk_db_valid=chunk_db_valid,
+                        chunk_db_invalid=chunk_db_invalid,
+                        doc_offsets=doc_offsets,
+                    )
+
+                else:
+                    # Load existing block DB.
+                    with h5py.File(block["path"]) as f:
+                        existing_chunks_valid = np.copy(f["chunks_valid"])
+                        existing_chunks_invalid = np.copy(f["chunks_invalid"])
+                        existing_doc_offsets = np.copy(f["doc_offsets"])
+
+                    # Check equality.
+                    log_retro_rank_0(" > validate.")
+                    assert np.array_equal(existing_chunks_valid, chunk_db_valid)
+                    assert np.array_equal(existing_chunks_invalid, chunk_db_invalid)
+                    assert np.array_equal(existing_doc_offsets, doc_offsets)
+
+            # Wait for all ranks to finish block.
+            log_retro_rank_0(" > waiting for all ranks to finish block.")
+            torch.distributed.barrier()
+
+    log_retro_rank_0(" > finished saving individual db.")
+
+
+def build_individual_dbs(
+    config: RetroPreprocessingConfig, indexed_dataset_infos: List[Dict]
+) -> None:
+    """Iterate each indexed dataset & process its chunks.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+        indexed_dataset_infos (List[Dict]): Preprocessing metadata for each dataset.
+    """
+
+    # Build individual DBs.
+    log_retro_rank_0(" > build individual chunk dbs.")
+    for ds_idx, ds_info in enumerate(indexed_dataset_infos):
+        # Progress.
+        log_retro_rank_0(
+            " > building individual db, dataset %d / %d ... '%s'."
+            % (ds_idx, len(indexed_dataset_infos), ds_info["prefix"])
+        )
+
+        # Process single dataset.
+        build_individual_db(config, ds_idx, len(indexed_dataset_infos), ds_info)
+
+
+def update_chunk_counts(
+    config: RetroPreprocessingConfig, indexed_dataset_infos: List[Dict]
+) -> None:
+    """Set n_chunks_train & n_chunks sampled for each individual DB.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+        indexed_dataset_infos (List[Dict]): Preprocessing metadata for each dataset
+            (i.e., 'prefix', 'ratio', 'n_chunks', etc.).
+    """
+
+    if torch.distributed.get_rank() != 0:
+        return
+
+    # Data ratio sum (for setting index training chunks).
+    data_ratio_sum = sum([d["ratio"] for d in indexed_dataset_infos])
+
+    # Training split size (split at document level).
+    train_fraction = float(extract_data_config(config).split.split(",")[0]) / 100
+    assert train_fraction > 0 and train_fraction <= 1
+
+    # Set n_chunks (including n_chunks_sampled for unambiguity).
+    log_retro_rank_0(" > compute n_chunks.")
+    for ds_index, ds_info in enumerate(indexed_dataset_infos):
+        db_paths = get_individual_db_paths(config.retro_project_dir, ds_info["prefix"])
+
+        # Update counts.
+        ds_info["n_docs"] = len(ds_info["dataset"].document_indices) - 1
+        ds_info["n_docs_train"] = int(train_fraction * ds_info["n_docs"])
+        ds_info["n_chunks"] = 0  # previously, 'n_chunks_valid'
+        ds_info["n_chunks_train"] = 0
+        ds_info["n_chunks_invalid"] = 0
+        for db_path in tqdm(
+            db_paths, "%d/%d, %s" % (ds_index, len(indexed_dataset_infos), ds_info["prefix"])
+        ):
+            with h5py.File(db_path, "r") as f:
+                ds_info["n_chunks"] += len(f["chunks_valid"])
+                ds_info["n_chunks_invalid"] += len(f["chunks_invalid"])
+                ds_info["n_chunks_train"] += (
+                    (np.copy(f["chunks_valid"][:, 0]) < ds_info["n_docs_train"]).sum().item()
+                )
+
+        ds_info["n_chunks_sampled"] = int(
+            config.retro_index_ntrain * ds_info["ratio"] / data_ratio_sum
+        )
+
+        # Verify counts.
+        assert ds_info["n_chunks_train"] <= ds_info["n_chunks"], "n_train (%d) > n_total (%d)." % (
+            ds_info["n_chunks_train"],
+            ds_info["n_chunks"],
+        )
+        assert (
+            ds_info["n_chunks_sampled"] <= ds_info["n_chunks_train"]
+        ), "n_sampled (%d) > n_train (%d)." % (
+            ds_info["n_chunks_sampled"],
+            ds_info["n_chunks_train"],
+        )
+
+
+def merge_dbs(project_dir: str, indexed_dataset_infos: List[Dict], db_type: str) -> None:
+    """Merge individual DBs into single DB.
+
+    Args:
+        project_dir (str): Retro project dir.
+        indexed_dataset_infos (List[Dict]): Preprocessing metadata for each dataset
+            (i.e., 'prefix', 'ratio', 'n_chunks', etc.).
+        db_type (str): DB type (e.g., 'sampled', 'train', or 'valid').
+    """
+
+    if not HAVE_H5PY:
+        raise ImportError("h5py is required to use the RetroDataset. Please install h5py.")
+
+    if torch.distributed.get_rank() != 0:
+        return
+
+    log_retro_rank_0(" > build %s chunk db." % db_type)
+
+    # Count chunks.
+    if db_type == "sampled":
+        n_chunks_key = "n_chunks_sampled"
+        n_docs_key = None
+    elif db_type == "train":
+        n_chunks_key = "n_chunks_train"
+        n_docs_key = "n_docs_train"
+    elif db_type == "valid":
+        n_docs_key = None
+    else:
+        raise Exception("handle db_type '%s'." % db_type)
+
+    if db_type == "valid":
+        n_chunks = sum(m["n_chunks"] - m["n_chunks_train"] for m in indexed_dataset_infos)
+    else:
+        n_chunks = sum(m[n_chunks_key] for m in indexed_dataset_infos)
+        n_docs = None if n_docs_key is None else sum(m[n_docs_key] for m in indexed_dataset_infos)
+
+    # DB path.
+    db_path = get_merged_db_path_map(project_dir)[db_type]
+
+    # Delete existing chunk db if incorrect size.
+    if os.path.exists(db_path):
+        try:
+            f = h5py.File(db_path)
+            n_alloc = len(f["chunks"])  # total allocated
+            n_written = f["n_written"][0].item()  # total written
+            f.close()
+
+            if n_chunks != n_alloc or n_chunks != n_written:
+                os.remove(db_path)
+
+        except Exception as e:
+            if isinstance(e, OSError):
+                os.remove(db_path)
+            elif isinstance(e, KeyError):
+                f.close()
+                os.remove(db_path)
+            else:
+                raise e
+
+    # Build merged chunk db.
+    if not os.path.exists(db_path):
+        os.makedirs(os.path.dirname(db_path), exist_ok=True)
+        f = h5py.File(db_path, "w")
+
+        # Initialize output arrays.
+        merged_chunk_db: np.ndarray = f.create_dataset("chunks", (n_chunks, 5), dtype="uint32")
+        merged_doc_offsets: np.ndarray = (
+            None
+            if n_docs_key is None
+            else f.create_dataset("doc_offsets", (n_docs, 3), dtype="uint64")
+        )
+        n_written = f.create_dataset("n_written", (1,), dtype="uint64")
+        n_written[0] = 0
+
+        # Iterate indexed datasets & collect chunks.
+        chunk_start_index = 0
+        doc_start_index = 0
+        doc_start_offset = 0
+        for ds_idx, ds_info in enumerate(indexed_dataset_infos):
+            log_retro_rank_0(
+                " > merging dbs; '%s', dataset %d / %d ... '%s'."
+                % (db_type, ds_idx, len(indexed_dataset_infos), ds_info["prefix"])
+            )
+            individual_chunk_db: np.ndarray = get_individual_chunk_db(project_dir, ds_idx, ds_info)
+            individual_doc_offsets: np.ndarray = (
+                None
+                if n_docs_key is None
+                else get_individual_doc_offsets(project_dir, ds_idx, ds_info)
+            )
+
+            if db_type == "valid":
+                individual_chunk_db = individual_chunk_db[ds_info["n_chunks_train"] :]
+                if n_docs_key is None:
+                    individual_doc_offsets = None
+                else:
+                    train_doc_offset = individual_doc_offsets[ds_info["n_docs_train"] - 1, 2]
+                    individual_doc_offsets = np.copy(
+                        individual_doc_offsets[ds_info["n_docs_train"] :]
+                    )
+                    individual_doc_offsets[:, 2] -= train_doc_offset
+
+                    log_retro_rank_0("~~~")
+                    log_retro_rank_0(individual_doc_offsets)
+                    log_retro_rank_0(train_doc_offset)
+                    raise Exception("test me.")
+            else:
+                individual_chunk_db = individual_chunk_db[: ds_info[n_chunks_key]]
+                individual_doc_offsets = (
+                    None
+                    if n_docs_key is None
+                    else np.copy(individual_doc_offsets[: ds_info[n_docs_key]])
+                )
+
+            merged_chunk_db[chunk_start_index : chunk_start_index + len(individual_chunk_db)] = (
+                individual_chunk_db
+            )
+            chunk_start_index += len(individual_chunk_db)
+            n_written[0] = chunk_start_index
+            if n_docs_key is not None:
+                individual_doc_offsets[:, 2] += doc_start_offset
+                doc_end_index = doc_start_index + individual_doc_offsets.shape[0]
+                merged_doc_offsets[doc_start_index:doc_end_index] = individual_doc_offsets
+                doc_start_index = doc_end_index
+                doc_start_offset = individual_doc_offsets[-1, 2].item()
+
+        f.close()
+
+
+def build_merged_dbs(project_dir: str, indexed_dataset_infos: List[Dict]) -> None:
+    """Merge individual dataset components into single database.
+
+    This method merges databases for DB types:
+    - 'sampled': used for training the vector index.
+    - 'train': used for adding to the trained vector index.
+    - 'valid': can be used for validating/testing the vector index.
+
+    Args:
+        project_dir (str): Retro project dir.
+        indexed_dataset_infos (List[Dict]): Preprocessing metadata for each dataset
+            (i.e., 'prefix', 'ratio', 'n_chunks', etc.).
+    """
+    merge_dbs(project_dir, indexed_dataset_infos, "sampled")
+    merge_dbs(project_dir, indexed_dataset_infos, "train")
+    merge_dbs(project_dir, indexed_dataset_infos, "valid")
+
+
+def build_db(config: RetroPreprocessingConfig) -> None:
+    """Extract token chunks from each indexed dataset.
+
+    Iterate each document of each indexed dataset, extract that document's chunks,
+        and save to a 'DB' (hdf5 file).
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+    """
+
+    project_dir = config.retro_project_dir
+
+    # Indexed dataset info.
+    if config.retro_task_validate is None:
+        indexed_dataset_infos = init_indexed_dataset_infos(config)
+    else:
+        indexed_dataset_infos = get_indexed_dataset_infos(config.retro_project_dir)
+    # Build individual dbs.
+    build_individual_dbs(config, indexed_dataset_infos)
+
+    # If validating, return here.
+    if config.retro_task_validate is not None:
+        return
+
+    # Single-process going forward.
+    if torch.distributed.get_rank() != 0:
+        return
+
+    # Update n_chunks & save indexed dataset infos.
+    if not os.path.exists(get_indexed_dataset_infos_path(project_dir)):
+        update_chunk_counts(config, indexed_dataset_infos)
+        save_indexed_dataset_infos(project_dir, indexed_dataset_infos)
+    indexed_dataset_infos = get_indexed_dataset_infos(project_dir)
+
+    # Builded merged dbs.
+    build_merged_dbs(project_dir, indexed_dataset_infos)

Megatron-LM/megatron/core/datasets/retro/db/dataset.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""A DBDataset is for iterating the chunks of the chunk database.
+
+This dataset is used for both training a vector index, and adding vectors to a
+trained index.
+"""
+
+from typing import List
+
+import numpy as np
+import torch
+
+from megatron.core.datasets.indexed_dataset import IndexedDataset
+
+try:
+    from tqdm import tqdm
+
+    HAVE_TQDM = True
+except ImportError:
+    HAVE_TQDM = False
+
+
+class DBDataset(torch.utils.data.Dataset):
+    """Dataset for iterating chunks.
+
+    Args:
+        db_path (str): Path of HDF5-format chunk database.
+        indexed_datasets (List[IndexedDataset]): Indexed datasets used to build database.
+        chunks (np.ndarray): Array of chunk indexes, for indexing into indexed datasets.
+            Format [dataset_idx, doc_id, start_idx, end_idx, bert_length].
+        chunk_length (int): Max GPT chunk length (e.g., 64).
+        eod_token_id (int): EOD token ID.
+    """
+
+    def __init__(
+        self,
+        db_path: str,
+        indexed_datasets: List[IndexedDataset],
+        chunks: np.ndarray,
+        chunk_length: int,
+        eod_token_id: int,
+    ):
+        assert chunks.shape[1] == 5, (
+            "expected 5 columns (dataset_idx, "
+            "doc_idx, token_start_idx, token_end_idx, bert_chunk_length); "
+            "found %d columns." % chunks.shape[1]
+        )
+
+        self.db_path = db_path
+        self.indexed_datasets = indexed_datasets
+        self.chunks = chunks
+        self.doc_chunk_map = None
+
+        self.max_chunk_length = chunk_length
+        self.eod_token_id = eod_token_id
+
+    def __len__(self) -> int:
+        """Length of DB dataset.
+
+        Returns:
+            Number of chunks contained in the dataset.
+        """
+        return self.chunks.shape[0]
+
+    def __getitem__(self, chunk_id: int) -> dict:
+        """DB dataset sample.
+
+        Args:
+            chunk_id (int): Index of chunk within dataset.
+
+        Returns:
+            A dict containing:
+            - 'doc_id': Document index within indexed dataset.
+            - 'text': GPT token IDs.
+        """
+
+        # Chunk start/end indexes.
+        indexed_dataset_id, doc_id, token_start_idx, token_end_idx, _ = [
+            value.item() for value in self.chunks[chunk_id]
+        ]
+        chunk_length = token_end_idx - token_start_idx
+        indexed_dataset = self.indexed_datasets[indexed_dataset_id]
+
+        # Chunk token ids.
+        token_ids = indexed_dataset.get(doc_id, offset=token_start_idx, length=chunk_length)
+
+        # Extend chunks to max_chunk_length by padding with EOD tokens.
+        if chunk_length != self.max_chunk_length:
+            assert chunk_length < self.max_chunk_length, "invalid chunk len."
+            token_ids = token_ids.tolist()
+            token_ids += [self.eod_token_id] * (self.max_chunk_length - chunk_length)
+
+        return {"doc_id": doc_id, "text": np.array(token_ids, dtype=np.int64)}
+
+    def load_doc_tuples(self) -> None:
+        """Load the dataset & document ids.
+
+        Load the dataset id & document id of each chunk in the database, to
+        be used for causality filtering during querying.
+        """
+        if not HAVE_TQDM:
+            raise ImportError("tqdm is required to use the DBDataset. Please install tqdm.")
+
+        self.doc_tuples = np.zeros(shape=(len(self), 2), dtype="uint32")
+        block_size = int(1e6)
+        for start_idx in tqdm(
+            range(0, len(self), block_size),
+            "load doc tuples",
+            miniters=(len(self) // block_size) // 10,
+            disable=torch.distributed.get_rank() != 0,
+        ):
+            end_idx = min(len(self), start_idx + block_size)
+            self.doc_tuples[start_idx:end_idx] = self.chunks[start_idx:end_idx, :2]

Megatron-LM/megatron/core/datasets/retro/db/utils.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""Utilities for building a chunk database."""
+
+import glob
+import json
+import os
+from typing import Dict, List, Optional
+
+import numpy as np
+
+from megatron.core.datasets.indexed_dataset import IndexedDataset
+from megatron.core.datasets.retro.config import RetroPreprocessingConfig
+from megatron.core.models.retro.utils import get_gpt_data_dir
+
+from .dataset import DBDataset
+
+try:
+    import h5py
+
+    HAVE_H5PY = True
+except ImportError:
+    HAVE_H5PY = False
+
+
+def get_db_dir(project_dir: str) -> str:
+    """Sub-directory for DB data.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+
+    Returns:
+        Path of the DB sub-directory within the project.
+    """
+    return os.path.join(project_dir, "db")
+
+
+def init_indexed_dataset_infos(config: RetroPreprocessingConfig) -> List[Dict]:
+    """Gather meta-info about each indexed dataset.
+
+    The returned info array allows for easy access to the configuration, and
+    helps remove ambiguity.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+
+    Returns:
+        List of processing metadata for each dataset, including:
+        - ratio: Data split weight.
+        - prefix: Relative path to dataset under DB sub-directory.
+    """
+
+    data_dir = get_gpt_data_dir(config.retro_project_dir)
+    data_blend: List[str] = config.retro_gpt_data_path
+    assert len(data_blend) % 2 == 0, "currently, only blended dataset is supported."
+
+    # Dataset infos.
+    infos = []
+    for i in range(0, len(data_blend), 2):
+        ratio = float(data_blend[i])
+        prefix = data_blend[i + 1]
+        path = os.path.join(data_dir, prefix + ".bin")
+        assert os.path.exists(path), "couldn't find '%s'." % path
+        infos.append({"ratio": ratio, "prefix": prefix})
+
+    # Load indexed datasets.
+    load_indexed_datasets(config.retro_project_dir, infos)
+
+    return infos
+
+
+def get_indexed_dataset_infos_path(project_dir: str) -> str:
+    """Path to indexed dataset meta-infos.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+
+    Returns:
+        Path to the `indexed_dataset_infos.json` file.
+    """
+    return os.path.join(get_db_dir(project_dir), "indexed_dataset_infos.json")
+
+
+def save_indexed_dataset_infos(project_dir: str, indexed_dataset_infos: List[Dict]) -> None:
+    """Save dataset order & meta-info.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        indexed_dataset_infos (List[Dict]): List of metadata for each dataset,
+            with each entry containing:
+
+        - ratio: Data split weight.
+        - prefix: Relative path to dataset under DB sub-directory.
+        - n_docs: Number of documents.
+        - n_docs_train: Number of documents used for pretraining.
+        - n_chunks: Number of valid chunks.
+        - n_chunks_train: Number of valid chunks used for pretraining.
+        - n_chunks_invalid: Number of invalid chunks.
+        - n_chunks_sampled: Number of valid chunks used for vector index training.
+    """
+
+    # Remove 'dataset' field.
+    clean_infos = []
+    for info in indexed_dataset_infos:
+        info = dict(info)
+        del info["dataset"]
+        clean_infos.append(info)
+
+    # Save.
+    with open(get_indexed_dataset_infos_path(project_dir), "w") as f:
+        json.dump(clean_infos, f, indent=4)
+
+
+def load_indexed_datasets(project_dir: str, indexed_dataset_infos: List[Dict]) -> None:
+    """Loaded indexed datasets into memory-mapped datasets.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        indexed_dataset_infos (List[Dict]): List of metadata for each dataset
+            (see `save_indexed_dataset_infos()` for more details.
+    """
+    data_dir = get_gpt_data_dir(project_dir)
+    for info in indexed_dataset_infos:
+        info["dataset"] = IndexedDataset(os.path.join(data_dir, info["prefix"]), mmap=True)
+
+
+def get_indexed_dataset_infos(project_dir: str) -> List[Dict]:
+    """Load indexed dataset meta-infos.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+
+    Returns:
+        List of metadata for each dataset (see `save_indexed_dataset_infos()` for more details.
+    """
+
+    # Load json.
+    path = get_indexed_dataset_infos_path(project_dir)
+    with open(path) as f:
+        infos = json.load(f)
+
+    # Load indexed datasets.
+    load_indexed_datasets(project_dir, infos)
+
+    return infos
+
+
+def get_individual_db_dir(project_dir: str, prefix: str) -> str:
+    """Individual DB's directory.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        prefix (str): Unique relative path to dataset within project dir.
+
+    Returns:
+        Path to the given datasets's chunk database.
+    """
+    return os.path.join(get_db_dir(project_dir), "individual", prefix)
+
+
+def get_individual_db_paths(project_dir: str, prefix: str) -> List[str]:
+    """Get paths of all database blocks of an individual dataset.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        prefix (str): Unique relative path to dataset within project dir.
+
+    Returns:
+        Paths to each HDF5 chunk database files that comprises this datasets full chunk database.
+    """
+    return sorted(glob.glob(get_individual_db_dir(project_dir, prefix) + "/*hdf5"))
+
+
+def get_individual_chunk_db(project_dir: str, ds_id: int, ds_info: dict) -> np.ndarray:
+    """Load individual dataset's chunk DB.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        ds_id (int): Index of dataset within blended dataset.
+        ds_info (dict): Preprocessing metadata for dataset
+            (see `save_indexed_dataset_infos()` for more detail).
+
+    Returns:
+        Array of chunk start/end indexes for this dataset,
+            where the chunk indexes can be used for indexing into
+            the corresponding indexed dataset.
+    """
+
+    if not HAVE_H5PY:
+        raise ImportError("h5py is required to use the RetroDataset. Please install h5py.")
+
+    paths = get_individual_db_paths(project_dir, ds_info["prefix"])
+    # *Note*: convert to dataset, rather than copying to memory.
+    db = np.zeros((ds_info["n_chunks"], 5), dtype="uint32")
+    db[:, 0] = ds_id
+    start_idx = 0
+    for path in paths:
+        f = h5py.File(path, "r")
+        n_chunks_current = f["chunks_valid"].shape[0]
+        db[start_idx : (start_idx + n_chunks_current), 1:] = f["chunks_valid"]
+        start_idx += n_chunks_current
+        f.close()
+
+    assert start_idx == ds_info["n_chunks"]
+
+    return db
+
+
+def get_individual_doc_offsets(project_dir: str, ds_id: int, ds_info: dict) -> np.ndarray:
+    """Load individual dataset's document offsets.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        ds_id (int): Index of dataset within blended dataset.
+        ds_info (dict): Preprocessing metadata for dataset
+            (see `save_indexed_dataset_infos()` for more detail).
+
+    Returns:
+        Array of document offsets by chunk index for this dataset.
+    """
+
+    if not HAVE_H5PY:
+        raise ImportError("h5py is required to use the RetroDataset. Please install h5py.")
+
+    paths = get_individual_db_paths(project_dir, ds_info["prefix"])
+    # *Note*: convert to dataset, rather than copying to memory.
+    doc_offsets = np.zeros((ds_info["n_docs"], 3), dtype="uint64")
+    doc_offsets[:, 0] = ds_id
+    start_idx = 0
+    start_offset = 0
+    for path in paths:
+        with h5py.File(path) as f:
+            current_doc_offsets = np.copy(f["doc_offsets"])
+            current_doc_offsets[:, 1] += start_offset
+            current_ndocs = current_doc_offsets.shape[0]
+            doc_offsets[start_idx : (start_idx + current_ndocs), 1:] = current_doc_offsets
+            start_idx += current_ndocs
+            start_offset = current_doc_offsets[-1, 1].item()
+
+    return doc_offsets
+
+
+def get_merged_db_path_map(project_dir: str) -> dict:
+    """Paths to merged datasets.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+
+    Returns:
+        A dict of chunk databases, one for each of:
+        - sampled: Chunks used for training the vector index.
+        - train: Chunks used for pretraining 'train' dataset.
+        - valid: Chunks used for pretraining 'valid' dataset.
+    """
+    base_dir = get_db_dir(project_dir)
+    return {
+        "sampled": os.path.join(base_dir, "merged", "sampled.hdf5"),
+        "train": os.path.join(base_dir, "merged", "train.hdf5"),
+        "valid": os.path.join(base_dir, "merged", "valid.hdf5"),
+    }
+
+
+def get_merged_dataset(
+    project_dir: str,
+    chunk_length: int,
+    eod_token_id: int,
+    db_type: str,
+    indexed_dataset_infos: Optional[List[Dict]] = None,
+) -> DBDataset:
+    """Get merged dataset.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        chunk_length (int): GPT chunk length (e.g., 64).
+        eod_token_id (int): EOD token ID.
+        db_type (str): DB type (e.g., 'sampled', 'train', or 'valid').
+        indexed_dataset_infos (Optional[List[Dict]]): Optionally, pre-loaded list
+            of dataset metadata (see `save_indexed_dataset_infos()` for more detail).
+            If not provided, the indexed dataset infos will be loaded from disk.
+
+    Returns:
+        A DBDataset, which is a dataset that wraps the HDF5 chunk index array.
+    """
+    if not HAVE_H5PY:
+        raise ImportError("h5py is required to use the RetroDataset. Please install h5py.")
+
+    if not indexed_dataset_infos:
+        indexed_dataset_infos = get_indexed_dataset_infos(project_dir)
+
+    # Load chunks.
+    db_path = get_merged_db_path_map(project_dir)[db_type]
+    f = h5py.File(db_path, "r")
+    chunks = f["chunks"]
+
+    # DB dataset.
+    indexed_datasets = [info["dataset"] for info in indexed_dataset_infos]
+    dataset = DBDataset(
+        db_path=db_path,
+        indexed_datasets=indexed_datasets,
+        chunks=chunks,
+        chunk_length=chunk_length,
+        eod_token_id=eod_token_id,
+    )
+
+    return dataset
+
+
+def get_merged_sampled_dataset(
+    project_dir: str,
+    chunk_length: int,
+    eod_token_id: int,
+    indexed_dataset_infos: Optional[List[Dict]] = None,
+) -> DBDataset:
+    """Get sampled dataset (for training the vector index).
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        chunk_length (int): GPT chunk length (e.g., 64).
+        eod_token_id (int): EOD token ID.
+        indexed_dataset_infos (Optional[List[Dict]]): Optionally, pre-loaded list
+            of dataset metadata (see `save_indexed_dataset_infos()` for more detail).
+            If not provided, the indexed dataset infos will be loaded from disk.
+
+    Returns:
+        A DBDataset, which is a dataset that wraps the HDF5 chunk index array.
+    """
+    return get_merged_dataset(
+        project_dir, chunk_length, eod_token_id, "sampled", indexed_dataset_infos
+    )
+
+
+def get_merged_train_dataset(
+    project_dir: str,
+    chunk_length: int,
+    eod_token_id: int,
+    indexed_dataset_infos: Optional[List[Dict]] = None,
+) -> DBDataset:
+    """Get training dataset (for adding to the vector index).
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        chunk_length (int): GPT chunk length (e.g., 64).
+        eod_token_id (int): EOD token ID.
+        indexed_dataset_infos (Optional[List[Dict]]): Optionally, pre-loaded list of
+            dataset metadata (see `save_indexed_dataset_infos()` for more detail).
+            If not provided, the indexed dataset infos will be loaded from disk.
+
+    Returns:
+        A DBDataset, which is a dataset that wraps the HDF5 chunk index array.
+    """
+    return get_merged_dataset(
+        project_dir, chunk_length, eod_token_id, "train", indexed_dataset_infos
+    )
+
+
+def get_merged_valid_dataset(
+    project_dir: str,
+    chunk_length: int,
+    eod_token_id: int,
+    indexed_dataset_infos: Optional[List[Dict]] = None,
+) -> DBDataset:
+    """Get validation dataset (for testing the vector index).
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        chunk_length (int): GPT chunk length (e.g., 64).
+        eod_token_id (int): EOD token ID.
+        indexed_dataset_infos (Optional[List[Dict]]): Optionally, pre-loaded list
+            of dataset metadata (see `save_indexed_dataset_infos()` for more detail).
+            If not provided, the indexed dataset infos will be loaded from disk.
+
+    Returns:
+        A DBDataset, which is a dataset that wraps the HDF5 chunk index array.
+    """
+    return get_merged_dataset(
+        project_dir, chunk_length, eod_token_id, "valid", indexed_dataset_infos
+    )
+
+
+def get_merged_datasets(project_dir: str, chunk_length: int, eod_token_id: int) -> dict:
+    """Get all merged datasets.
+
+    Args:
+        project_dir (str): Path to Retro project dir.
+        chunk_length (int): GPT chunk length (e.g., 64).
+        eod_token_id (int): EOD token ID.
+
+    Returns:
+        A dict mapping DB type ('sampled', 'train', or 'valid') to the corresponding DBDataset,
+            which is a dataset that wraps the HDF5 chunk index array.
+    """
+    fns = {
+        "sampled": get_merged_sampled_dataset,
+        "train": get_merged_train_dataset,
+        "valid": get_merged_valid_dataset,
+    }
+    datasets = {key: fn(project_dir, chunk_length, eod_token_id) for key, fn in fns.items()}
+    return datasets

Megatron-LM/megatron/core/datasets/retro/external_libs.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""Required external libraries for Retro preprocessing."""
+
+import importlib
+
+required_libs = ["faiss", "h5py", "transformers"]  # for huggingface bert
+
+for lib in required_libs:
+    try:
+        globals()[lib] = importlib.import_module(lib)
+    except ImportError as e:
+        pass

Megatron-LM/megatron/core/datasets/retro/index/__init__.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""
+Exports:
+
+  - train_index: Train an index on representative vectors.
+  - add_to_index: Add vectors to a trained index.
+  - build_index: Wrapper function that calls above two functions.
+"""
+
+from .build import add_to_index, build_index, train_index

Megatron-LM/megatron/core/datasets/retro/index/build.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""Construct an index.
+
+Constructing an index generally happens in two phases:
+
+  - index.train(): Train an index on a representative set of vectors.
+  - index.add(): Add vectors to an index, to be available for retrieval.
+"""
+
+import os
+import shutil
+
+import numpy as np
+import torch
+
+from megatron.core.datasets.retro.config import RetroPreprocessingConfig
+from megatron.core.datasets.retro.db.utils import (
+    get_merged_sampled_dataset,
+    get_merged_train_dataset,
+)
+from megatron.core.datasets.retro.utils import GPTToTextDataset
+
+from .factory import IndexFactory
+from .utils import (
+    get_training_data_block_dir,
+    get_training_data_block_paths,
+    get_training_data_merged_path,
+    get_training_data_root_dir,
+)
+
+try:
+    from tqdm import tqdm
+
+    HAVE_TQDM = True
+except ImportError:
+    HAVE_TQDM = False
+
+try:
+    import h5py
+
+    HAVE_H5PY = True
+except ImportError:
+    HAVE_H5PY = False
+
+##################################################
+# Train index.
+##################################################
+
+
+def get_empty_index_path(config: RetroPreprocessingConfig) -> str:
+    """Path of empty index.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+
+    Returns:
+        Path to the empty (trained, but without added samples) vector index.
+    """
+    index = IndexFactory.get_index(config.retro_index_type)
+    empty_index_path = index.get_empty_index_path(config)
+    return empty_index_path
+
+
+def get_block_nload(block_path: str, load_fraction: float) -> int:
+    """Compute number of blocks to load.
+
+    This is computed by multiplying the total number of available blocks with the
+    fraction of blocks to load.
+
+    Args:
+        block_path (str): Path to HDF5 file containing block of data. File must contain key 'data'.
+        load_fraction (float): Fraction (0 < load_fraction <= 1) of block samples to load.
+
+    Returns:
+        Number of block samples to load.
+    """
+    if not HAVE_H5PY:
+        raise ImportError(
+            "h5py is required to use the merge_embedding_blocks function. Please install h5py."
+        )
+
+    with h5py.File(block_path) as fi:
+        return int(load_fraction * fi["data"].shape[0])
+
+
+def merge_embedding_blocks(config: RetroPreprocessingConfig) -> None:
+    """Merge individual embedding blocks into a single binary mmap file.
+
+    The embeddings are initially stored in block-sized (e.g., ~100k embeddings per
+    block) HDF5 files. These individual block files must be merged into a single
+    file before training, to be based as a numpy mmap array to the index.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+    """
+
+    if not HAVE_TQDM:
+        raise ImportError(
+            "tqdm is required to use the merge_embedding_blocks function. Please install tqdm."
+        )
+
+    if not HAVE_H5PY:
+        raise ImportError(
+            "h5py is required to use the merge_embedding_blocks function. Please install h5py."
+        )
+
+    if torch.distributed.get_rank() != 0:
+        return
+
+    # Get block, merged paths.
+    load_fraction = config.retro_index_train_load_fraction
+    block_paths = get_training_data_block_paths(config)
+    bin_path = get_training_data_merged_path(config)
+
+    # Skip, if already built.
+    if os.path.exists(bin_path):
+        return
+
+    # Merge blocks.
+    with open(bin_path, "wb") as fo:
+        byte_offset = 0
+        for block_idx, block_path in enumerate(
+            tqdm(
+                block_paths,
+                "merge train embeddings",
+                miniters=len(block_paths) // 10,
+                disable=torch.distributed.get_rank() != 0,
+            )
+        ):
+            with h5py.File(block_path) as fi:
+                nload = get_block_nload(block_path, load_fraction)
+                block = np.array(fi["data"][:nload], copy=False)
+
+                fo.write(block.tobytes())
+
+                byte_offset += block.size * block.itemsize
+                fo.seek(byte_offset)
+
+
+def get_text_dataset_for_training(config: RetroPreprocessingConfig) -> GPTToTextDataset:
+    """Convert GPT token chunk dataset to a text dataset for passing to the
+    embedder.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+
+    Returns:
+        The text dataset consisting of tokens converted from sampled chunk database.
+    """
+    gpt_dataset = get_merged_sampled_dataset(
+        project_dir=config.retro_project_dir,
+        chunk_length=config.retro_gpt_chunk_length,
+        eod_token_id=config.retro_tokenizers.gpt.eod,
+    )
+    text_dataset = GPTToTextDataset(gpt_dataset, config.retro_tokenizers.gpt)
+    return text_dataset
+
+
+def embed_training_chunks(config: RetroPreprocessingConfig) -> None:
+    """Embed DB chunks.
+
+    Store chunks in blocks on disk. These blocks will later be merged into
+    a single dataset for training the index.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+    """
+
+    merged_train_data_path = get_training_data_merged_path(config)
+    if os.path.exists(merged_train_data_path):
+        return
+
+    # Get training text dataset.
+    text_dataset = get_text_dataset_for_training(config)
+
+    # Embed dataset.
+    embedder = config.retro_bert_embedders.disk
+    embedder.embed_text_dataset("index", get_training_data_block_dir(config), text_dataset)
+
+    # Merge embeddings.
+    merge_embedding_blocks(config)
+
+
+def train_on_embeddings(config: RetroPreprocessingConfig) -> None:
+    """Train index on embedded DB chunks.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+    """
+    index = IndexFactory.get_index(config.retro_index_type)
+    index.train(config)
+
+
+def remove_embeddings(config: RetroPreprocessingConfig) -> None:
+    """Remove embeddings after training.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+    """
+    torch.distributed.barrier()
+    if torch.distributed.get_rank() != 0:
+        return
+    empty_index_path = get_empty_index_path(config)
+    assert os.path.isfile(empty_index_path)
+    shutil.rmtree(get_training_data_root_dir(config), ignore_errors=True)
+
+
+def _train_index(config: RetroPreprocessingConfig) -> None:
+    """Train index on DB chunks.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+    """
+
+    # Check if trained index already exists.
+    if not os.path.isfile(get_empty_index_path(config)):
+        # Embed training chunks.
+        embed_training_chunks(config)
+
+        # Train index on embeddings.
+        train_on_embeddings(config)
+
+    # Wait for (single-process) training to complete.
+    torch.distributed.barrier()
+
+    # Remove embeddings.
+    if config.retro_index_delete_training_embeddings:
+        remove_embeddings(config)
+
+
+def train_index(config: RetroPreprocessingConfig) -> None:
+    """Entry point for training the index.
+
+    We select whether to train a new index, or validate an existing index.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+    """
+
+    # Train new index.
+    if config.retro_task_validate is None:
+        _train_index(config)
+
+    # Validate existing trained index.
+    else:
+        from .validate import validate_training_embeddings
+
+        validate_training_embeddings(config)
+
+
+##################################################
+# Add to index.
+##################################################
+
+
+def get_text_dataset_for_adding(config: RetroPreprocessingConfig) -> GPTToTextDataset:
+    """Convert GPT token chunk dataset to a text dataset for passing to the
+    embedder.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+
+    Returns:
+        The text dataset that consists of tokens converted from the 'train' chunk database.
+        These are the chunks used for retrieval by the pretraining 'train' dataset.
+    """
+    gpt_dataset = get_merged_train_dataset(
+        project_dir=config.retro_project_dir,
+        chunk_length=config.retro_gpt_chunk_length,
+        eod_token_id=config.retro_tokenizers.gpt.eod,
+    )
+    text_dataset = GPTToTextDataset(gpt_dataset, config.retro_tokenizers.gpt)
+    return text_dataset
+
+
+def _add_to_index(config: RetroPreprocessingConfig) -> str:
+    """Add DB chunks to index.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+
+    Returns:
+        Path to the populated index.
+    """
+
+    # Get index.
+    index = IndexFactory.get_index(config.retro_index_type)
+
+    # Get text dataset.
+    text_dataset = get_text_dataset_for_adding(config)
+
+    # Add to index.
+    output_index_path = index.add(config, text_dataset)
+
+    return output_index_path
+
+
+def add_to_index(config: RetroPreprocessingConfig) -> None:
+    """Entry point for adding to the index.
+
+    We select whether to add to a new index, or validate an existing index.
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+    """
+
+    # Add to new index.
+    if config.retro_task_validate is None:
+        _add_to_index(config)
+
+    # Validate existing encodings.
+    else:
+        from .validate import validate_added_encodings
+
+        validate_added_encodings(config)
+
+
+##################################################
+# Build index (train + add).
+##################################################
+
+
+def build_index(config: RetroPreprocessingConfig) -> None:
+    """Build index.
+
+    Building index involves sequentially running stages above:
+    - Train index (on sampled training chunks).
+    - Add to index (on all training chunks).
+
+    Args:
+        config (RetroPreprocessingConfig): Retro preprocessing config.
+    """
+
+    # Train index.
+    train_index(config)
+
+    # Add to index.
+    add_to_index(config)

Megatron-LM/megatron/core/datasets/retro/index/factory.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""The IndexFactory constructs an index from an index type string."""
+
+from megatron.core.datasets.retro.index.index import Index
+
+from .indexes import FaissBaseIndex, FaissParallelAddIndex
+
+
+class IndexFactory:
+    """Get index.
+
+    Index type generally read from argument '--retro-index-ty'.
+    """
+
+    @classmethod
+    def get_index_class(cls, index_type: str) -> type:
+        """Get an index class, given a type string.
+
+        Args:
+            index_type (str): One of 'faiss-base' (naive Faiss index wrapper) or 'faiss-par-add' (Faiss index wrapper with near embarrassingly parallel index.add().
+
+        Returns:
+            An `Index` sub-type corresponding to the `index_type`.
+        """
+        return {"faiss-base": FaissBaseIndex, "faiss-par-add": FaissParallelAddIndex}[index_type]
+
+    @classmethod
+    def get_index(cls, index_type: str) -> Index:
+        """Construct an index from an index type string.
+
+        Args:
+            index_type (str): One of 'faiss-base' (naive Faiss index wrapper) or 'faiss-par-add' (Faiss index wrapper with near embarrassingly parallel index.add().
+
+        Returns:
+            An `Index` instance corresponding to the `index_type`.
+        """
+        index_class = cls.get_index_class(index_type)
+        index = index_class()
+        return index

Megatron-LM/megatron/core/datasets/retro/index/index.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""Base class for all vector indexes.
+
+A vector index is a type of retrieval database that is queried using vectors,
+and returns vectors that are 'similar' (e.g., by cosine distance) to the query
+vector. The construction and usage of an index generally has the following
+pattern:
+
+  - Train the index on representative vectors.
+  - Add vectors to the index (i.e., vectors available for retrieval)
+  - Query index with new vector, to retrieve similar vector indexes.
+"""
+
+import abc
+import os
+from typing import Tuple
+
+import numpy as np
+import torch
+
+from megatron.core.datasets.retro.config import Embedder, RetroPreprocessingConfig
+from megatron.core.datasets.retro.utils import GPTToTextDataset
+
+from .utils import get_index_dir
+
+try:
+    import faiss
+
+    HAVE_FAISS = True
+except ImportError:
+    HAVE_FAISS = False
+
+
+class Index(abc.ABC):
+    """Abstract base class for indexes.
+
+    *Note* : While currently only Faiss-based classes are implemented, in the
+    future, this class will be extended with other types of indexes that have
+    different performance-accuracy trade-offs.
+
+    The primary methods to override are:
+    - train() : Train index on the sampled training chunks.
+    - add() : Add all training chunks to index.
+    """
+
+    @classmethod
+    def make_object_verbose(cls, index: "faiss.Index", verbose: bool) -> None:
+        """Make index object verbose.
+
+        Args:
+            index (faiss.Index): Faiss object to set verbose.
+            verbose (bool): Sets whether index should log status updates during training and adding.
+        """
+        if not HAVE_FAISS:
+            raise ImportError("faiss is required to use the Index class. Please install faiss.")
+
+        assert isinstance(verbose, bool)
+        faiss.ParameterSpace().set_index_parameter(index, "verbose", verbose)
+
+    def get_empty_index_path(self, config: RetroPreprocessingConfig) -> str:
+        """Get file path to empty index (i.e., trained, but unpopulated).
+
+        Args:
+            config (RetroPreprocessingConfig): Retro preprocessing config.
+
+        Returns:
+            File path to empty index
+                (i.e., this index has had index.train() called, but not yet index.add()).
+        """
+        return os.path.join(
+            get_index_dir(config), "empty_%.3f.faissindex" % config.retro_index_train_load_fraction
+        )
+
+    def get_empty_index(self, config: RetroPreprocessingConfig) -> "faiss.Index":
+        """Get empty index (i.e., trained, but unpopulated).
+
+        Args:
+            config (RetroPreprocessingConfig): Retro preprocessing config.
+
+        Returns:
+            Empty Faiss index, loaded from storage.
+        """
+        if not HAVE_FAISS:
+            raise ImportError("faiss is required to use the Index class. Please install faiss.")
+        return faiss.read_index(self.get_empty_index_path(config))
+
+    def get_added_index_path(self, config: RetroPreprocessingConfig) -> str:
+        """Get file path to index that has been populated with vectors.
+
+        Args:
+            config (RetroPreprocessingConfig): Retro preprocessing config.
+
+        Returns:
+            File path to added index
+                (i.e., this index has had both index.train() and index.add() called).
+        """
+        return os.path.join(
+            get_index_dir(config),
+            "added_%.3f_%.3f.faissindex"
+            % (config.retro_index_train_load_fraction, config.retro_index_add_load_fraction),
+        )
+
+    def get_added_index(self, config: RetroPreprocessingConfig) -> "faiss.Index":
+        """Get index that has been populated with vectors.
+
+        Args:
+            config (RetroPreprocessingConfig): Retro preprocessing config.
+
+        Returns:
+            'Added' (i.e., populated) Faiss index, loaded from storage.
+        """
+        if not HAVE_FAISS:
+            raise ImportError("faiss is required to use the Index class. Please install faiss.")
+        return faiss.read_index(self.get_added_index_path(config))
+
+    @abc.abstractmethod
+    def train(self, config: RetroPreprocessingConfig) -> None:
+        """Train index on a representative set of vectors.
+
+        Args:
+            config (RetroPreprocessingConfig): Retro preprocessing config.
+        """
+
+    @abc.abstractmethod
+    def add(self, config: RetroPreprocessingConfig, text_dataset: GPTToTextDataset) -> None:
+        """Add vectors to index.
+
+        Args:
+            config (RetroPreprocessingConfig): Retro preprocessing config.
+            text_dataset (GPTToTextDataset): Text dataset that will be embedded
+                and added to the index.
+        """
+
+    def embed_text_dataset_block(
+        self, embedder: Embedder, text_dataset: GPTToTextDataset, _range: Tuple[int, int]
+    ) -> np.ndarray:
+        """Embed a range of a text dataset.
+
+        Args:
+            embedder (Embedder): Embedder used for embedding a text dataset.
+            text_dataset (GPTToTextDataset): Text dataset that will be embedded.
+            _range (Tuple[int, int]): Start/end sample indices within
+                text dataset used for embedding.
+
+        Returns:
+            An array of embeddings, with shape (len(text_dataset), dimension(embedder)).
+        """
+        sub_dataset = torch.utils.data.Subset(text_dataset, range(*_range))
+        return embedder.embed_text_dataset(sub_dataset)