Merge branch 'deepspeed-branch' into 'main'

Deepspeed branch

See merge request dcutoolkit/deeplearing/dlexamples_new!22

Deepspeed/HelloDeepSpeed/tests/test_train_bert.py

+import tempfile
+
+import numpy as np
+import pytest
+import torch
+import tqdm
+from transformers import AutoTokenizer
+
+from train_bert import create_data_iterator, create_model, load_model_checkpoint, train
+
+
+@pytest.fixture(scope="function")
+def checkpoint_dir() -> str:
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        yield tmpdirname
+
+
+def test_masking_stats(tol: float = 1e-3):
+    """Test to check that the masking probabilities
+    match what we expect them to be.
+    """
+    kwargs = {
+        "mask_prob": 0.15,
+        "random_replace_prob": 0.1,
+        "unmask_replace_prob": 0.1,
+        "batch_size": 8,
+    }
+    tokenizer = AutoTokenizer.from_pretrained("roberta-base")
+    dataloader = create_data_iterator(**kwargs)
+    num_samples = 10000
+    total_tokens = 0
+    masked_tokens = 0
+    random_replace_tokens = 0
+    unmasked_replace_tokens = 0
+    for ix, batch in tqdm.tqdm(enumerate(dataloader, start=1), total=num_samples):
+        # Since we don't mask the BOS / EOS tokens, we subtract them from the total tokens
+        total_tokens += batch["attention_mask"].sum().item() - (
+            2 * batch["attention_mask"].size(0)
+        )
+        masked_tokens += (batch["tgt_tokens"] != tokenizer.pad_token_id).sum().item()
+        random_or_unmasked = (
+            batch["tgt_tokens"] != tokenizer.pad_token_id
+        ).logical_and(batch["src_tokens"] != tokenizer.mask_token_id)
+        unmasked = random_or_unmasked.logical_and(
+            batch["src_tokens"] == batch["tgt_tokens"]
+        )
+        unmasked_replace_tokens += unmasked.sum().item()
+        random_replace_tokens += random_or_unmasked.sum().item() - unmasked.sum().item()
+        if ix == num_samples:
+            break
+    estimated_mask_prob = masked_tokens / total_tokens
+    estimated_random_tokens = random_replace_tokens / total_tokens
+    estimated_unmasked_tokens = unmasked_replace_tokens / total_tokens
+    assert np.isclose(estimated_mask_prob, kwargs["mask_prob"], atol=tol)
+    assert np.isclose(
+        estimated_random_tokens,
+        kwargs["random_replace_prob"] * kwargs["mask_prob"],
+        atol=tol,
+    )
+    assert np.isclose(
+        estimated_unmasked_tokens,
+        kwargs["unmask_replace_prob"] * kwargs["mask_prob"],
+        atol=tol,
+    )
+
+
+def test_model_checkpointing(checkpoint_dir: str):
+    """Training a small model, and ensuring
+    that both checkpointing and resuming from
+    a checkpoint work.
+    """
+    # First train a tiny model for 5 iterations
+    train_params = {
+        "checkpoint_dir": checkpoint_dir,
+        "checkpoint_every": 2,
+        "num_layers": 2,
+        "num_heads": 4,
+        "ff_dim": 64,
+        "h_dim": 64,
+        "num_iterations": 5,
+    }
+    exp_dir = train(**train_params)
+    # now check that we have 3 checkpoints
+    assert len(list(exp_dir.glob("*.pt"))) == 3
+    model = create_model(
+        num_layers=train_params["num_layers"],
+        num_heads=train_params["num_heads"],
+        ff_dim=train_params["ff_dim"],
+        h_dim=train_params["h_dim"],
+        dropout=0.1,
+    )
+    optimizer = torch.optim.Adam(model.parameters())
+    step, model, optimizer = load_model_checkpoint(exp_dir, model, optimizer)
+    assert step == 5
+    model_state_dict = model.state_dict()
+    # the saved checkpoint would be for iteration 5
+    correct_state_dict = torch.load(exp_dir / "checkpoint.iter_5.pt")
+    correct_model_state_dict = correct_state_dict["model"]
+    assert set(model_state_dict.keys()) == set(correct_model_state_dict.keys())
+    assert all(
+        torch.allclose(model_state_dict[key], correct_model_state_dict[key])
+        for key in model_state_dict.keys()
+    )
+    # Finally, try training with the checkpoint
+    train_params.pop("checkpoint_dir")
+    train_params["load_checkpoint_dir"] = str(exp_dir)
+    train_params["num_iterations"] = 10
+    train(**train_params)

Deepspeed/HelloDeepSpeed/train_bert.py

+import datetime
+import json
+import pathlib
+import re
+import string
+from functools import partial
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, TypeVar, Union
+
+import random
+import datasets
+import fire
+import loguru
+import numpy as np
+import pytz
+import sh
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, Dataset
+from torch.utils.tensorboard import SummaryWriter
+from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
+from transformers.models.roberta import RobertaConfig, RobertaModel
+from transformers.models.roberta.modeling_roberta import (
+    RobertaLMHead,
+    RobertaPreTrainedModel,
+)
+
+logger = loguru.logger
+
+######################################################################
+############### Dataset Creation Related Functions ###################
+######################################################################
+
+TokenizerType = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
+
+
+def collate_function(batch: List[Tuple[List[int], List[int]]],
+                     pad_token_id: int) -> Dict[str, torch.Tensor]:
+    """Collect a list of masked token indices, and labels, and
+    batch them, padding to max length in the batch.
+    """
+    max_length = max(len(token_ids) for token_ids, _ in batch)
+    padded_token_ids = [
+        token_ids +
+        [pad_token_id for _ in range(0, max_length - len(token_ids))]
+        for token_ids, _ in batch
+    ]
+    padded_labels = [
+        labels + [pad_token_id for _ in range(0, max_length - len(labels))]
+        for _, labels in batch
+    ]
+    src_tokens = torch.LongTensor(padded_token_ids)
+    tgt_tokens = torch.LongTensor(padded_labels)
+    attention_mask = src_tokens.ne(pad_token_id).type_as(src_tokens)
+    return {
+        "src_tokens": src_tokens,
+        "tgt_tokens": tgt_tokens,
+        "attention_mask": attention_mask,
+    }
+
+
+def masking_function(
+        text: str,
+        tokenizer: TokenizerType,
+        mask_prob: float,
+        random_replace_prob: float,
+        unmask_replace_prob: float,
+        max_length: int,
+) -> Tuple[List[int], List[int]]:
+    """Given a text string, randomly mask wordpieces for Bert MLM
+    training.
+
+    Args:
+        text (str):
+            The input text
+        tokenizer (TokenizerType):
+            The tokenizer for tokenization
+        mask_prob (float):
+            What fraction of tokens to mask
+        random_replace_prob (float):
+            Of the masked tokens, how many should be replaced with
+            random tokens (improves performance)
+        unmask_replace_prob (float):
+            Of the masked tokens, how many should be replaced with
+            the original token (improves performance)
+        max_length (int):
+            The maximum sequence length to consider. Note that for
+            Bert style models, this is a function of the number of
+            positional embeddings you learn
+
+    Returns:
+        Tuple[List[int], List[int]]:
+            The masked token ids (based on the tokenizer passed),
+            and the output labels (padded with `tokenizer.pad_token_id`)
+    """
+    # Note: By default, encode does add the BOS and EOS token
+    # Disabling that behaviour to make this more clear
+    tokenized_ids = ([tokenizer.bos_token_id] +
+                     tokenizer.encode(text,
+                                      add_special_tokens=False,
+                                      truncation=True,
+                                      max_length=max_length - 2) +
+                     [tokenizer.eos_token_id])
+    seq_len = len(tokenized_ids)
+    tokenized_ids = np.array(tokenized_ids)
+    subword_mask = np.full(len(tokenized_ids), False)
+
+    # Masking the BOS and EOS token leads to slightly worse performance
+    low = 1
+    high = len(subword_mask) - 1
+    mask_choices = np.arange(low, high)
+    num_subwords_to_mask = max(
+        int((mask_prob * (high - low)) + np.random.rand()), 1)
+    subword_mask[np.random.choice(mask_choices,
+                                  num_subwords_to_mask,
+                                  replace=False)] = True
+
+    # Create the labels first
+    labels = np.full(seq_len, tokenizer.pad_token_id)
+    labels[subword_mask] = tokenized_ids[subword_mask]
+
+    tokenized_ids[subword_mask] = tokenizer.mask_token_id
+
+    # Now of the masked tokens, choose how many to replace with random and how many to unmask
+    rand_or_unmask_prob = random_replace_prob + unmask_replace_prob
+    if rand_or_unmask_prob > 0:
+        rand_or_unmask = subword_mask & (np.random.rand(len(tokenized_ids)) <
+                                         rand_or_unmask_prob)
+        if random_replace_prob == 0:
+            unmask = rand_or_unmask
+            rand_mask = None
+        elif unmask_replace_prob == 0:
+            unmask = None
+            rand_mask = rand_or_unmask
+        else:
+            unmask_prob = unmask_replace_prob / rand_or_unmask_prob
+            decision = np.random.rand(len(tokenized_ids)) < unmask_prob
+            unmask = rand_or_unmask & decision
+            rand_mask = rand_or_unmask & (~decision)
+        if unmask is not None:
+            tokenized_ids[unmask] = labels[unmask]
+        if rand_mask is not None:
+            weights = np.ones(tokenizer.vocab_size)
+            weights[tokenizer.all_special_ids] = 0
+            probs = weights / weights.sum()
+            num_rand = rand_mask.sum()
+            tokenized_ids[rand_mask] = np.random.choice(tokenizer.vocab_size,
+                                                        num_rand,
+                                                        p=probs)
+    return tokenized_ids.tolist(), labels.tolist()
+
+
+class WikiTextMLMDataset(Dataset):
+    """A [Map style dataset](https://pytorch.org/docs/stable/data.html)
+    for iterating over the wikitext dataset. Note that this assumes
+    the dataset can fit in memory. For larger datasets
+    you'd want to shard them and use an iterable dataset (eg: see
+    [Infinibatch](https://github.com/microsoft/infinibatch))
+
+    Args:
+        Dataset (datasets.arrow_dataset.Dataset):
+            The wikitext dataset
+        masking_function (Callable[[str], Tuple[List[int], List[int]]])
+            The masking function. To generate one training instance,
+            the masking function is applied to the `text` of a dataset
+            record
+
+    """
+    def __init__(
+        self,
+        dataset: datasets.arrow_dataset.Dataset,
+        masking_function: Callable[[str], Tuple[List[int], List[int]]],
+    ) -> None:
+        self.dataset = dataset
+        self.masking_function = masking_function
+
+    def __len__(self) -> int:
+        return len(self.dataset)
+
+    def __getitem__(self, idx: int) -> Tuple[List[int], List[int]]:
+        tokens, labels = self.masking_function(self.dataset[idx]["text"])
+        return (tokens, labels)
+
+
+T = TypeVar("T")
+
+
+class InfiniteIterator(object):
+    def __init__(self, iterable: Iterable[T]) -> None:
+        self._iterable = iterable
+        self._iterator = iter(self._iterable)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self) -> T:
+        next_item = None
+        try:
+            next_item = next(self._iterator)
+        except StopIteration:
+            self._iterator = iter(self._iterable)
+            next_item = next(self._iterator)
+        return next_item
+
+
+def create_data_iterator(
+        mask_prob: float,
+        random_replace_prob: float,
+        unmask_replace_prob: float,
+        batch_size: int,
+        max_seq_length: int = 512,
+        tokenizer: str = "roberta-base",
+) -> InfiniteIterator:
+    """Create the dataloader.
+
+    Args:
+        mask_prob (float):
+            Fraction of tokens to mask
+        random_replace_prob (float):
+            Fraction of masked tokens to replace with random token
+        unmask_replace_prob (float):
+            Fraction of masked tokens to replace with the actual token
+        batch_size (int):
+            The batch size of the generated tensors
+        max_seq_length (int, optional):
+            The maximum sequence length for the MLM task. Defaults to 512.
+        tokenizer (str, optional):
+            The tokenizer to use. Defaults to "roberta-base".
+
+    Returns:
+        InfiniteIterator:
+            The torch DataLoader, wrapped in an InfiniteIterator class, to
+            be able to continuously generate samples
+
+    """
+    wikitext_dataset = datasets.load_dataset("wikitext",
+                                             "wikitext-2-v1",
+                                             split="train")
+    wikitext_dataset = wikitext_dataset.filter(
+        lambda record: record["text"] != "").map(
+            lambda record: {"text": record["text"].rstrip("\n")})
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer)
+    masking_function_partial = partial(
+        masking_function,
+        tokenizer=tokenizer,
+        mask_prob=mask_prob,
+        random_replace_prob=random_replace_prob,
+        unmask_replace_prob=unmask_replace_prob,
+        max_length=max_seq_length,
+    )
+    dataset = WikiTextMLMDataset(wikitext_dataset, masking_function_partial)
+    collate_fn_partial = partial(collate_function,
+                                 pad_token_id=tokenizer.pad_token_id)
+    dataloader = DataLoader(dataset,
+                            batch_size=batch_size,
+                            shuffle=True,
+                            collate_fn=collate_fn_partial)
+
+    return InfiniteIterator(dataloader)
+
+
+######################################################################
+############### Model Creation Related Functions #####################
+######################################################################
+
+
+class RobertaLMHeadWithMaskedPredict(RobertaLMHead):
+    def __init__(self,
+                 config: RobertaConfig,
+                 embedding_weight: Optional[torch.Tensor] = None) -> None:
+        super(RobertaLMHeadWithMaskedPredict, self).__init__(config)
+        if embedding_weight is not None:
+            self.decoder.weight = embedding_weight
+
+    def forward(  # pylint: disable=arguments-differ
+        self,
+        features: torch.Tensor,
+        masked_token_indices: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """The current `transformers` library does not provide support
+        for masked_token_indices. This function provides the support, by
+        running the final forward pass only for the masked indices. This saves
+        memory
+
+        Args:
+            features (torch.Tensor):
+                The features to select from. Shape (batch, seq_len, h_dim)
+            masked_token_indices (torch.Tensor, optional):
+                The indices of masked tokens for index select. Defaults to None.
+                Shape: (num_masked_tokens,)
+
+        Returns:
+            torch.Tensor:
+                The index selected features. Shape (num_masked_tokens, h_dim)
+
+        """
+        if masked_token_indices is not None:
+            features = torch.index_select(
+                features.view(-1, features.shape[-1]), 0, masked_token_indices)
+        return super().forward(features)
+
+
+class RobertaMLMModel(RobertaPreTrainedModel):
+    def __init__(self, config: RobertaConfig, encoder: RobertaModel) -> None:
+        super().__init__(config)
+        self.encoder = encoder
+        self.lm_head = RobertaLMHeadWithMaskedPredict(
+            config, self.encoder.embeddings.word_embeddings.weight)
+        self.lm_head.apply(self._init_weights)
+
+    def forward(
+            self,
+            src_tokens: torch.Tensor,
+            attention_mask: torch.Tensor,
+            tgt_tokens: torch.Tensor,
+    ) -> torch.Tensor:
+        """The forward pass for the MLM task
+
+        Args:
+            src_tokens (torch.Tensor):
+                The masked token indices. Shape: (batch, seq_len)
+            attention_mask (torch.Tensor):
+                The attention mask, since the batches are padded
+                to the largest sequence. Shape: (batch, seq_len)
+            tgt_tokens (torch.Tensor):
+                The output tokens (padded with `config.pad_token_id`)
+
+        Returns:
+            torch.Tensor:
+                The MLM loss
+        """
+        # shape: (batch, seq_len, h_dim)
+        sequence_output, *_ = self.encoder(input_ids=src_tokens,
+                                           attention_mask=attention_mask,
+                                           return_dict=False)
+
+        pad_token_id = self.config.pad_token_id
+        # (labels have also been padded with pad_token_id)
+        # filter out all masked labels
+        # shape: (num_masked_tokens,)
+        masked_token_indexes = torch.nonzero(
+            (tgt_tokens != pad_token_id).view(-1)).view(-1)
+        # shape: (num_masked_tokens, vocab_size)
+        prediction_scores = self.lm_head(sequence_output, masked_token_indexes)
+        # shape: (num_masked_tokens,)
+        target = torch.index_select(tgt_tokens.view(-1), 0,
+                                    masked_token_indexes)
+
+        loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
+
+        masked_lm_loss = loss_fct(
+            prediction_scores.view(-1, self.config.vocab_size), target)
+        return masked_lm_loss
+
+
+def create_model(num_layers: int, num_heads: int, ff_dim: int, h_dim: int,
+                 dropout: float) -> RobertaMLMModel:
+    """Create a Bert model with the specified `num_heads`, `ff_dim`,
+    `h_dim` and `dropout`
+
+    Args:
+        num_layers (int):
+            The number of layers
+        num_heads (int):
+            The number of attention heads
+        ff_dim (int):
+            The intermediate hidden size of
+            the feed forward block of the
+            transformer
+        h_dim (int):
+            The hidden dim of the intermediate
+            representations of the transformer
+        dropout (float):
+            The value of dropout to be used.
+            Note that we apply the same dropout
+            to both the attention layers and the
+            FF layers
+
+    Returns:
+        RobertaMLMModel:
+            A Roberta model for MLM task
+
+    """
+    roberta_config_dict = {
+        "attention_probs_dropout_prob": dropout,
+        "bos_token_id": 0,
+        "eos_token_id": 2,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": dropout,
+        "hidden_size": h_dim,
+        "initializer_range": 0.02,
+        "intermediate_size": ff_dim,
+        "layer_norm_eps": 1e-05,
+        "max_position_embeddings": 514,
+        "model_type": "roberta",
+        "num_attention_heads": num_heads,
+        "num_hidden_layers": num_layers,
+        "pad_token_id": 1,
+        "type_vocab_size": 1,
+        "vocab_size": 50265,
+    }
+    roberta_config = RobertaConfig.from_dict(roberta_config_dict)
+    roberta_encoder = RobertaModel(roberta_config)
+    roberta_model = RobertaMLMModel(roberta_config, roberta_encoder)
+    return roberta_model
+
+
+######################################################################
+########### Experiment Management Related Functions ##################
+######################################################################
+
+
+def get_unique_identifier(length: int = 8) -> str:
+    """Create a unique identifier by choosing `length`
+    random characters from list of ascii characters and numbers
+    """
+    alphabet = string.ascii_lowercase + string.digits
+    uuid = "".join(alphabet[ix]
+                   for ix in np.random.choice(len(alphabet), length))
+    return uuid
+
+
+def create_experiment_dir(checkpoint_dir: pathlib.Path,
+                          all_arguments: Dict[str, Any]) -> pathlib.Path:
+    """Create an experiment directory and save all arguments in it.
+    Additionally, also store the githash and gitdiff. Finally create
+    a directory for `Tensorboard` logs. The structure would look something
+    like
+        checkpoint_dir
+            `-experiment-name
+                |- hparams.json
+                |- githash.log
+                |- gitdiff.log
+                `- tb_dir/
+
+    Args:
+        checkpoint_dir (pathlib.Path):
+            The base checkpoint directory
+        all_arguments (Dict[str, Any]):
+            The arguments to save
+
+    Returns:
+        pathlib.Path: The experiment directory
+    """
+    # experiment name follows the following convention
+    # {exp_type}.{YYYY}.{MM}.{DD}.{HH}.{MM}.{SS}.{uuid}
+    current_time = datetime.datetime.now(pytz.timezone("US/Pacific"))
+    expname = "bert_pretrain.{0}.{1}.{2}.{3}.{4}.{5}.{6}".format(
+        current_time.year,
+        current_time.month,
+        current_time.day,
+        current_time.hour,
+        current_time.minute,
+        current_time.second,
+        get_unique_identifier(),
+    )
+    exp_dir = checkpoint_dir / expname
+    exp_dir.mkdir(exist_ok=False)
+    hparams_file = exp_dir / "hparams.json"
+    with hparams_file.open("w") as handle:
+        json.dump(obj=all_arguments, fp=handle, indent=2)
+    # Save the git hash
+    try:
+        gitlog = sh.git.log("-1", format="%H", _tty_out=False, _fg=False)
+        with (exp_dir / "githash.log").open("w") as handle:
+            handle.write(gitlog.stdout.decode("utf-8"))
+    except sh.ErrorReturnCode_128:
+        logger.info("Seems like the code is not running from"
+                    " within a git repo, so hash will"
+                    " not be stored. However, it"
+                    " is strongly advised to use"
+                    " version control.")
+    # And the git diff
+    try:
+        gitdiff = sh.git.diff(_fg=False, _tty_out=False)
+        with (exp_dir / "gitdiff.log").open("w") as handle:
+            handle.write(gitdiff.stdout.decode("utf-8"))
+    except sh.ErrorReturnCode_129:
+        logger.info("Seems like the code is not running from"
+                    " within a git repo, so diff will"
+                    " not be stored. However, it"
+                    " is strongly advised to use"
+                    " version control.")
+    # Finally create the Tensorboard Dir
+    tb_dir = exp_dir / "tb_dir"
+    tb_dir.mkdir()
+    return exp_dir
+
+
+######################################################################
+################ Checkpoint Related Functions ########################
+######################################################################
+
+
+def load_model_checkpoint(
+    load_checkpoint_dir: pathlib.Path,
+    model: torch.nn.Module,
+    optimizer: torch.optim.Optimizer,
+) -> Tuple[int, torch.nn.Module, torch.optim.Optimizer]:
+    """Loads the optimizer state dict and model state dict from the load_checkpoint_dir
+    into the passed model and optimizer. Searches for the most recent checkpoint to
+    load from
+
+    Args:
+        load_checkpoint_dir (pathlib.Path):
+            The base checkpoint directory to load from
+        model (torch.nn.Module):
+            The model to load the checkpoint weights into
+        optimizer (torch.optim.Optimizer):
+            The optimizer to load the checkpoint weigths into
+
+    Returns:
+        Tuple[int, torch.nn.Module, torch.optim.Optimizer]:
+            The checkpoint step, model with state_dict loaded and
+            optimizer with state_dict loaded
+
+    """
+    logger.info(
+        f"Loading model and optimizer checkpoint from {load_checkpoint_dir}")
+    checkpoint_files = list(
+        filter(
+            lambda path: re.search(r"iter_(?P<iter_no>\d+)\.pt", path.name) is
+            not None,
+            load_checkpoint_dir.glob("*.pt"),
+        ))
+    assert len(checkpoint_files) > 0, "No checkpoints found in directory"
+    checkpoint_files = sorted(
+        checkpoint_files,
+        key=lambda path: int(
+            re.search(r"iter_(?P<iter_no>\d+)\.pt", path.name).group("iter_no")
+        ),
+    )
+    latest_checkpoint_path = checkpoint_files[-1]
+    checkpoint_step = int(
+        re.search(r"iter_(?P<iter_no>\d+)\.pt",
+                  latest_checkpoint_path.name).group("iter_no"))
+
+    state_dict = torch.load(latest_checkpoint_path)
+    model.load_state_dict(state_dict["model"], strict=True)
+    optimizer.load_state_dict(state_dict["optimizer"])
+    logger.info(
+        f"Loading model and optimizer checkpoints done. Loaded from {latest_checkpoint_path}"
+    )
+    return checkpoint_step, model, optimizer
+
+
+######################################################################
+######################## Driver Functions ############################
+######################################################################
+
+
+def train(
+        checkpoint_dir: str = None,
+        load_checkpoint_dir: str = None,
+        # Dataset Parameters
+        mask_prob: float = 0.15,
+        random_replace_prob: float = 0.1,
+        unmask_replace_prob: float = 0.1,
+        max_seq_length: int = 512,
+        tokenizer: str = "roberta-base",
+        # Model Parameters
+        num_layers: int = 6,
+        num_heads: int = 8,
+        ff_dim: int = 512,
+        h_dim: int = 256,
+        dropout: float = 0.1,
+        # Training Parameters
+        batch_size: int = 8,
+        num_iterations: int = 10000,
+        checkpoint_every: int = 1000,
+        log_every: int = 10,
+        local_rank: int = -1,
+) -> pathlib.Path:
+    """Trains a [Bert style](https://arxiv.org/pdf/1810.04805.pdf)
+    (transformer encoder only) model for MLM Task
+
+    Args:
+        checkpoint_dir (str):
+            The base experiment directory to save experiments to
+        mask_prob (float, optional):
+            The fraction of tokens to mask. Defaults to 0.15.
+        random_replace_prob (float, optional):
+            The fraction of masked tokens to replace with random token.
+            Defaults to 0.1.
+        unmask_replace_prob (float, optional):
+            The fraction of masked tokens to leave unchanged.
+            Defaults to 0.1.
+        max_seq_length (int, optional):
+            The maximum sequence length of the examples. Defaults to 512.
+        tokenizer (str, optional):
+            The tokenizer to use. Defaults to "roberta-base".
+        num_layers (int, optional):
+            The number of layers in the Bert model. Defaults to 6.
+        num_heads (int, optional):
+            Number of attention heads to use. Defaults to 8.
+        ff_dim (int, optional):
+            Size of the intermediate dimension in the FF layer.
+            Defaults to 512.
+        h_dim (int, optional):
+            Size of intermediate representations.
+            Defaults to 256.
+        dropout (float, optional):
+            Amout of Dropout to use. Defaults to 0.1.
+        batch_size (int, optional):
+            The minibatch size. Defaults to 8.
+        num_iterations (int, optional):
+            Total number of iterations to run the model for.
+            Defaults to 10000.
+        checkpoint_every (int, optional):
+            Save checkpoint after these many steps.
+
+            ..note ::
+
+                You want this to be frequent enough that you can
+                resume training in case it crashes, but not so much
+                that you fill up your entire storage !
+
+            Defaults to 1000.
+        log_every (int, optional):
+            Print logs after these many steps. Defaults to 10.
+        local_rank (int, optional):
+            Which GPU to run on (-1 for CPU). Defaults to -1.
+
+    Returns:
+        pathlib.Path: The final experiment directory
+
+    """
+    device = (torch.device("cuda", local_rank) if (local_rank > -1)
+              and torch.cuda.is_available() else torch.device("cpu"))
+    ################################
+    ###### Create Exp. Dir #########
+    ################################
+    if checkpoint_dir is None and load_checkpoint_dir is None:
+        logger.error("Need to specify one of checkpoint_dir"
+                     " or load_checkpoint_dir")
+        return
+    if checkpoint_dir is not None and load_checkpoint_dir is not None:
+        logger.error("Cannot specify both checkpoint_dir"
+                     " and load_checkpoint_dir")
+        return
+    if checkpoint_dir:
+        logger.info("Creating Experiment Directory")
+        checkpoint_dir = pathlib.Path(checkpoint_dir)
+        checkpoint_dir.mkdir(exist_ok=True)
+        all_arguments = {
+            # Dataset Params
+            "mask_prob": mask_prob,
+            "random_replace_prob": random_replace_prob,
+            "unmask_replace_prob": unmask_replace_prob,
+            "max_seq_length": max_seq_length,
+            "tokenizer": tokenizer,
+            # Model Params
+            "num_layers": num_layers,
+            "num_heads": num_heads,
+            "ff_dim": ff_dim,
+            "h_dim": h_dim,
+            "dropout": dropout,
+            # Training Params
+            "batch_size": batch_size,
+            "num_iterations": num_iterations,
+            "checkpoint_every": checkpoint_every,
+        }
+        exp_dir = create_experiment_dir(checkpoint_dir, all_arguments)
+        logger.info(f"Experiment Directory created at {exp_dir}")
+    else:
+        logger.info("Loading from Experiment Directory")
+        load_checkpoint_dir = pathlib.Path(load_checkpoint_dir)
+        assert load_checkpoint_dir.exists()
+        with (load_checkpoint_dir / "hparams.json").open("r") as handle:
+            hparams = json.load(handle)
+        # Set the hparams
+        # Dataset Params
+        mask_prob = hparams.get("mask_prob", mask_prob)
+        tokenizer = hparams.get("tokenizer", tokenizer)
+        random_replace_prob = hparams.get("random_replace_prob",
+                                          random_replace_prob)
+        unmask_replace_prob = hparams.get("unmask_replace_prob",
+                                          unmask_replace_prob)
+        max_seq_length = hparams.get("max_seq_length", max_seq_length)
+        # Model Params
+        ff_dim = hparams.get("ff_dim", ff_dim)
+        h_dim = hparams.get("h_dim", h_dim)
+        dropout = hparams.get("dropout", dropout)
+        num_layers = hparams.get("num_layers", num_layers)
+        num_heads = hparams.get("num_heads", num_heads)
+        # Training Params
+        batch_size = hparams.get("batch_size", batch_size)
+        _num_iterations = hparams.get("num_iterations", num_iterations)
+        num_iterations = max(num_iterations, _num_iterations)
+        checkpoint_every = hparams.get("checkpoint_every", checkpoint_every)
+        exp_dir = load_checkpoint_dir
+    # Tensorboard writer
+    tb_dir = exp_dir / "tb_dir"
+    assert tb_dir.exists()
+    summary_writer = SummaryWriter(log_dir=tb_dir)
+    ################################
+    ###### Create Datasets #########
+    ################################
+    logger.info("Creating Datasets")
+    data_iterator = create_data_iterator(
+        mask_prob=mask_prob,
+        random_replace_prob=random_replace_prob,
+        unmask_replace_prob=unmask_replace_prob,
+        tokenizer=tokenizer,
+        max_seq_length=max_seq_length,
+        batch_size=batch_size,
+    )
+    logger.info("Dataset Creation Done")
+    ################################
+    ###### Create Model ############
+    ################################
+    logger.info("Creating Model")
+    model = create_model(
+        num_layers=num_layers,
+        num_heads=num_heads,
+        ff_dim=ff_dim,
+        h_dim=h_dim,
+        dropout=dropout,
+    )
+    model = model.to(device)
+    logger.info("Model Creation Done")
+    ################################
+    ###### Create Optimizer #######
+    ################################
+    logger.info("Creating Optimizer")
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+    logger.info("Optimizer Creation Done")
+    ################################
+    #### Load Model checkpoint #####
+    ################################
+    start_step = 1
+    if load_checkpoint_dir is not None:
+        checkpoint_step, model, optimizer = load_model_checkpoint(
+            load_checkpoint_dir, model, optimizer)
+        start_step = checkpoint_step + 1
+
+    ################################
+    ####### The Training Loop ######
+    ################################
+    logger.info(
+        f"Total number of model parameters: {sum([p.numel() for p in model.parameters()]):,d}"
+    )
+    model.train()
+    losses = []
+    for step, batch in enumerate(data_iterator, start=start_step):
+        if step >= num_iterations:
+            break
+        optimizer.zero_grad()
+        # Move the tensors to device
+        for key, value in batch.items():
+            batch[key] = value.to(device)
+        # Forward pass
+        loss = model(**batch)
+        # Backward pass
+        loss.backward()
+        # Optimizer Step
+        optimizer.step()
+        losses.append(loss.item())
+        if step % log_every == 0:
+            logger.info("Loss: {0:.4f}".format(np.mean(losses)))
+            summary_writer.add_scalar(f"Train/loss", np.mean(losses), step)
+        if step % checkpoint_every == 0:
+            state_dict = {
+                "model": model.state_dict(),
+                "optimizer": optimizer.state_dict(),
+            }
+            torch.save(obj=state_dict,
+                       f=str(exp_dir / f"checkpoint.iter_{step}.pt"))
+            logger.info("Saved model to {0}".format(
+                (exp_dir / f"checkpoint.iter_{step}.pt")))
+    # Save the last checkpoint if not saved yet
+    if step % checkpoint_every != 0:
+        state_dict = {
+            "model": model.state_dict(),
+            "optimizer": optimizer.state_dict(),
+        }
+        torch.save(obj=state_dict,
+                   f=str(exp_dir / f"checkpoint.iter_{step}.pt"))
+        logger.info("Saved model to {0}".format(
+            (exp_dir / f"checkpoint.iter_{step}.pt")))
+
+    return exp_dir
+
+
+if __name__ == "__main__":
+    torch.manual_seed(42)
+    np.random.seed(0)
+    random.seed(0)
+    fire.Fire(train)

Deepspeed/HelloDeepSpeed/train_bert_ds.py

+"""
+Modified version of train_bert.py that adds DeepSpeed
+"""
+
+import os
+import datetime
+import json
+import pathlib
+import re
+import string
+from functools import partial
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, TypeVar, Union
+
+import random
+import datasets
+import fire
+import logging
+import loguru
+import numpy as np
+import pytz
+import sh
+import torch
+import torch.nn as nn
+import deepspeed
+from torch.utils.data import DataLoader, Dataset
+from torch.utils.tensorboard import SummaryWriter
+from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
+from transformers.models.roberta import RobertaConfig, RobertaModel
+from transformers.models.roberta.modeling_roberta import (
+    RobertaLMHead,
+    RobertaPreTrainedModel,
+)
+
+
+def is_rank_0() -> bool:
+    return int(os.environ.get("RANK", "0")) == 0
+
+
+######################################################################
+####################### Logging Functions ############################
+######################################################################
+
+logger = loguru.logger
+
+
+def log_dist(message: str,
+             ranks: List[int] = [],
+             level: int = logging.INFO) -> None:
+    """Log messages for specified ranks only"""
+    my_rank = int(os.environ.get("RANK", "0"))
+    if my_rank in ranks:
+        if level == logging.INFO:
+            logger.info(f'[Rank {my_rank}] {message}')
+        if level == logging.ERROR:
+            logger.error(f'[Rank {my_rank}] {message}')
+        if level == logging.DEBUG:
+            logger.debug(f'[Rank {my_rank}] {message}')
+
+
+######################################################################
+############### Dataset Creation Related Functions ###################
+######################################################################
+
+TokenizerType = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
+
+
+def collate_function(batch: List[Tuple[List[int], List[int]]],
+                     pad_token_id: int) -> Dict[str, torch.Tensor]:
+    """Collect a list of masked token indices, and labels, and
+    batch them, padding to max length in the batch.
+    """
+    max_length = max(len(token_ids) for token_ids, _ in batch)
+    padded_token_ids = [
+        token_ids +
+        [pad_token_id for _ in range(0, max_length - len(token_ids))]
+        for token_ids, _ in batch
+    ]
+    padded_labels = [
+        labels + [pad_token_id for _ in range(0, max_length - len(labels))]
+        for _, labels in batch
+    ]
+    src_tokens = torch.LongTensor(padded_token_ids)
+    tgt_tokens = torch.LongTensor(padded_labels)
+    attention_mask = src_tokens.ne(pad_token_id).type_as(src_tokens)
+    return {
+        "src_tokens": src_tokens,
+        "tgt_tokens": tgt_tokens,
+        "attention_mask": attention_mask,
+    }
+
+
+def masking_function(
+        text: str,
+        tokenizer: TokenizerType,
+        mask_prob: float,
+        random_replace_prob: float,
+        unmask_replace_prob: float,
+        max_length: int,
+) -> Tuple[List[int], List[int]]:
+    """Given a text string, randomly mask wordpieces for Bert MLM
+    training.
+
+    Args:
+        text (str):
+            The input text
+        tokenizer (TokenizerType):
+            The tokenizer for tokenization
+        mask_prob (float):
+            What fraction of tokens to mask
+        random_replace_prob (float):
+            Of the masked tokens, how many should be replaced with
+            random tokens (improves performance)
+        unmask_replace_prob (float):
+            Of the masked tokens, how many should be replaced with
+            the original token (improves performance)
+        max_length (int):
+            The maximum sequence length to consider. Note that for
+            Bert style models, this is a function of the number of
+            positional embeddings you learn
+
+    Returns:
+        Tuple[List[int], List[int]]:
+            The masked token ids (based on the tokenizer passed),
+            and the output labels (padded with `tokenizer.pad_token_id`)
+    """
+    # Note: By default, encode does add the BOS and EOS token
+    # Disabling that behaviour to make this more clear
+    tokenized_ids = ([tokenizer.bos_token_id] +
+                     tokenizer.encode(text,
+                                      add_special_tokens=False,
+                                      truncation=True,
+                                      max_length=max_length - 2) +
+                     [tokenizer.eos_token_id])
+    seq_len = len(tokenized_ids)
+    tokenized_ids = np.array(tokenized_ids)
+    subword_mask = np.full(len(tokenized_ids), False)
+
+    # Masking the BOS and EOS token leads to slightly worse performance
+    low = 1
+    high = len(subword_mask) - 1
+    mask_choices = np.arange(low, high)
+    num_subwords_to_mask = max(
+        int((mask_prob * (high - low)) + np.random.rand()), 1)
+    subword_mask[np.random.choice(mask_choices,
+                                  num_subwords_to_mask,
+                                  replace=False)] = True
+
+    # Create the labels first
+    labels = np.full(seq_len, tokenizer.pad_token_id)
+    labels[subword_mask] = tokenized_ids[subword_mask]
+
+    tokenized_ids[subword_mask] = tokenizer.mask_token_id
+
+    # Now of the masked tokens, choose how many to replace with random and how many to unmask
+    rand_or_unmask_prob = random_replace_prob + unmask_replace_prob
+    if rand_or_unmask_prob > 0:
+        rand_or_unmask = subword_mask & (np.random.rand(len(tokenized_ids)) <
+                                         rand_or_unmask_prob)
+        if random_replace_prob == 0:
+            unmask = rand_or_unmask
+            rand_mask = None
+        elif unmask_replace_prob == 0:
+            unmask = None
+            rand_mask = rand_or_unmask
+        else:
+            unmask_prob = unmask_replace_prob / rand_or_unmask_prob
+            decision = np.random.rand(len(tokenized_ids)) < unmask_prob
+            unmask = rand_or_unmask & decision
+            rand_mask = rand_or_unmask & (~decision)
+        if unmask is not None:
+            tokenized_ids[unmask] = labels[unmask]
+        if rand_mask is not None:
+            weights = np.ones(tokenizer.vocab_size)
+            weights[tokenizer.all_special_ids] = 0
+            probs = weights / weights.sum()
+            num_rand = rand_mask.sum()
+            tokenized_ids[rand_mask] = np.random.choice(tokenizer.vocab_size,
+                                                        num_rand,
+                                                        p=probs)
+    return tokenized_ids.tolist(), labels.tolist()
+
+
+class WikiTextMLMDataset(Dataset):
+    """A [Map style dataset](https://pytorch.org/docs/stable/data.html)
+    for iterating over the wikitext dataset. Note that this assumes
+    the dataset can fit in memory. For larger datasets
+    you'd want to shard them and use an iterable dataset (eg: see
+    [Infinibatch](https://github.com/microsoft/infinibatch))
+
+    Args:
+        Dataset (datasets.arrow_dataset.Dataset):
+            The wikitext dataset
+        masking_function (Callable[[str], Tuple[List[int], List[int]]])
+            The masking function. To generate one training instance,
+            the masking function is applied to the `text` of a dataset
+            record
+
+    """
+    def __init__(
+        self,
+        dataset: datasets.arrow_dataset.Dataset,
+        masking_function: Callable[[str], Tuple[List[int], List[int]]],
+    ) -> None:
+        self.dataset = dataset
+        self.masking_function = masking_function
+
+    def __len__(self) -> int:
+        return len(self.dataset)
+
+    def __getitem__(self, idx: int) -> Tuple[List[int], List[int]]:
+        tokens, labels = self.masking_function(self.dataset[idx]["text"])
+        return (tokens, labels)
+
+
+T = TypeVar("T")
+
+
+class InfiniteIterator(object):
+    def __init__(self, iterable: Iterable[T]) -> None:
+        self._iterable = iterable
+        self._iterator = iter(self._iterable)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self) -> T:
+        next_item = None
+        try:
+            next_item = next(self._iterator)
+        except StopIteration:
+            self._iterator = iter(self._iterable)
+            next_item = next(self._iterator)
+        return next_item
+
+
+def create_data_iterator(
+        mask_prob: float,
+        random_replace_prob: float,
+        unmask_replace_prob: float,
+        batch_size: int,
+        max_seq_length: int = 512,
+        tokenizer: str = "roberta-base",
+) -> InfiniteIterator:
+    """Create the dataloader.
+
+    Args:
+        mask_prob (float):
+            Fraction of tokens to mask
+        random_replace_prob (float):
+            Fraction of masked tokens to replace with random token
+        unmask_replace_prob (float):
+            Fraction of masked tokens to replace with the actual token
+        batch_size (int):
+            The batch size of the generated tensors
+        max_seq_length (int, optional):
+            The maximum sequence length for the MLM task. Defaults to 512.
+        tokenizer (str, optional):
+            The tokenizer to use. Defaults to "roberta-base".
+
+    Returns:
+        InfiniteIterator:
+            The torch DataLoader, wrapped in an InfiniteIterator class, to
+            be able to continuously generate samples
+
+    """
+    wikitext_dataset = datasets.load_dataset("wikitext",
+                                             "wikitext-2-v1",
+                                             split="train")
+    wikitext_dataset = wikitext_dataset.filter(
+        lambda record: record["text"] != "").map(
+            lambda record: {"text": record["text"].rstrip("\n")})
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer)
+    masking_function_partial = partial(
+        masking_function,
+        tokenizer=tokenizer,
+        mask_prob=mask_prob,
+        random_replace_prob=random_replace_prob,
+        unmask_replace_prob=unmask_replace_prob,
+        max_length=max_seq_length,
+    )
+    dataset = WikiTextMLMDataset(wikitext_dataset, masking_function_partial)
+    collate_fn_partial = partial(collate_function,
+                                 pad_token_id=tokenizer.pad_token_id)
+    dataloader = DataLoader(dataset,
+                            batch_size=batch_size,
+                            shuffle=True,
+                            collate_fn=collate_fn_partial)
+
+    return InfiniteIterator(dataloader)
+
+
+######################################################################
+############### Model Creation Related Functions #####################
+######################################################################
+
+
+class RobertaLMHeadWithMaskedPredict(RobertaLMHead):
+    def __init__(self,
+                 config: RobertaConfig,
+                 embedding_weight: Optional[torch.Tensor] = None) -> None:
+        super(RobertaLMHeadWithMaskedPredict, self).__init__(config)
+        if embedding_weight is not None:
+            self.decoder.weight = embedding_weight
+
+    def forward(  # pylint: disable=arguments-differ
+        self,
+        features: torch.Tensor,
+        masked_token_indices: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """The current `transformers` library does not provide support
+        for masked_token_indices. This function provides the support, by
+        running the final forward pass only for the masked indices. This saves
+        memory
+
+        Args:
+            features (torch.Tensor):
+                The features to select from. Shape (batch, seq_len, h_dim)
+            masked_token_indices (torch.Tensor, optional):
+                The indices of masked tokens for index select. Defaults to None.
+                Shape: (num_masked_tokens,)
+
+        Returns:
+            torch.Tensor:
+                The index selected features. Shape (num_masked_tokens, h_dim)
+
+        """
+        if masked_token_indices is not None:
+            features = torch.index_select(
+                features.view(-1, features.shape[-1]), 0, masked_token_indices)
+        return super().forward(features)
+
+
+class RobertaMLMModel(RobertaPreTrainedModel):
+    def __init__(self, config: RobertaConfig, encoder: RobertaModel) -> None:
+        super().__init__(config)
+        self.encoder = encoder
+        self.lm_head = RobertaLMHeadWithMaskedPredict(
+            config, self.encoder.embeddings.word_embeddings.weight)
+        self.lm_head.apply(self._init_weights)
+
+    def forward(
+            self,
+            src_tokens: torch.Tensor,
+            attention_mask: torch.Tensor,
+            tgt_tokens: torch.Tensor,
+    ) -> torch.Tensor:
+        """The forward pass for the MLM task
+
+        Args:
+            src_tokens (torch.Tensor):
+                The masked token indices. Shape: (batch, seq_len)
+            attention_mask (torch.Tensor):
+                The attention mask, since the batches are padded
+                to the largest sequence. Shape: (batch, seq_len)
+            tgt_tokens (torch.Tensor):
+                The output tokens (padded with `config.pad_token_id`)
+
+        Returns:
+            torch.Tensor:
+                The MLM loss
+        """
+        # shape: (batch, seq_len, h_dim)
+        sequence_output, *_ = self.encoder(input_ids=src_tokens,
+                                           attention_mask=attention_mask,
+                                           return_dict=False)
+
+        pad_token_id = self.config.pad_token_id
+        # (labels have also been padded with pad_token_id)
+        # filter out all masked labels
+        # shape: (num_masked_tokens,)
+        masked_token_indexes = torch.nonzero(
+            (tgt_tokens != pad_token_id).view(-1)).view(-1)
+        # shape: (num_masked_tokens, vocab_size)
+        prediction_scores = self.lm_head(sequence_output, masked_token_indexes)
+        # shape: (num_masked_tokens,)
+        target = torch.index_select(tgt_tokens.view(-1), 0,
+                                    masked_token_indexes)
+
+        loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
+
+        masked_lm_loss = loss_fct(
+            prediction_scores.view(-1, self.config.vocab_size), target)
+        return masked_lm_loss
+
+
+def create_model(num_layers: int, num_heads: int, ff_dim: int, h_dim: int,
+                 dropout: float) -> RobertaMLMModel:
+    """Create a Bert model with the specified `num_heads`, `ff_dim`,
+    `h_dim` and `dropout`
+
+    Args:
+        num_layers (int):
+            The number of layers
+        num_heads (int):
+            The number of attention heads
+        ff_dim (int):
+            The intermediate hidden size of
+            the feed forward block of the
+            transformer
+        h_dim (int):
+            The hidden dim of the intermediate
+            representations of the transformer
+        dropout (float):
+            The value of dropout to be used.
+            Note that we apply the same dropout
+            to both the attention layers and the
+            FF layers
+
+    Returns:
+        RobertaMLMModel:
+            A Roberta model for MLM task
+
+    """
+    roberta_config_dict = {
+        "attention_probs_dropout_prob": dropout,
+        "bos_token_id": 0,
+        "eos_token_id": 2,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": dropout,
+        "hidden_size": h_dim,
+        "initializer_range": 0.02,
+        "intermediate_size": ff_dim,
+        "layer_norm_eps": 1e-05,
+        "max_position_embeddings": 514,
+        "model_type": "roberta",
+        "num_attention_heads": num_heads,
+        "num_hidden_layers": num_layers,
+        "pad_token_id": 1,
+        "type_vocab_size": 1,
+        "vocab_size": 50265,
+    }
+    roberta_config = RobertaConfig.from_dict(roberta_config_dict)
+    roberta_encoder = RobertaModel(roberta_config)
+    roberta_model = RobertaMLMModel(roberta_config, roberta_encoder)
+    return roberta_model
+
+
+######################################################################
+########### Experiment Management Related Functions ##################
+######################################################################
+
+
+def get_unique_identifier(length: int = 8) -> str:
+    """Create a unique identifier by choosing `length`
+    random characters from list of ascii characters and numbers
+    """
+    alphabet = string.ascii_lowercase + string.digits
+    uuid = "".join(alphabet[ix]
+                   for ix in np.random.choice(len(alphabet), length))
+    return uuid
+
+
+def create_experiment_dir(checkpoint_dir: pathlib.Path,
+                          all_arguments: Dict[str, Any]) -> pathlib.Path:
+    """Create an experiment directory and save all arguments in it.
+    Additionally, also store the githash and gitdiff. Finally create
+    a directory for `Tensorboard` logs. The structure would look something
+    like
+        checkpoint_dir
+            `-experiment-name
+                |- hparams.json
+                |- githash.log
+                |- gitdiff.log
+                `- tb_dir/
+
+    Args:
+        checkpoint_dir (pathlib.Path):
+            The base checkpoint directory
+        all_arguments (Dict[str, Any]):
+            The arguments to save
+
+    Returns:
+        pathlib.Path: The experiment directory
+    """
+    # experiment name follows the following convention
+    # {exp_type}.{YYYY}.{MM}.{DD}.{HH}.{MM}.{SS}.{uuid}
+    current_time = datetime.datetime.now(pytz.timezone("US/Pacific"))
+    expname = "bert_pretrain.{0}.{1}.{2}.{3}.{4}.{5}.{6}".format(
+        current_time.year,
+        current_time.month,
+        current_time.day,
+        current_time.hour,
+        current_time.minute,
+        current_time.second,
+        get_unique_identifier(),
+    )
+    exp_dir = checkpoint_dir / expname
+    if not is_rank_0():
+        return exp_dir
+    exp_dir.mkdir(exist_ok=False)
+    hparams_file = exp_dir / "hparams.json"
+    with hparams_file.open("w") as handle:
+        json.dump(obj=all_arguments, fp=handle, indent=2)
+    # Save the git hash
+    try:
+        gitlog = sh.git.log("-1", format="%H", _tty_out=False, _fg=False)
+        with (exp_dir / "githash.log").open("w") as handle:
+            handle.write(gitlog.stdout.decode("utf-8"))
+    except sh.ErrorReturnCode_128:
+        log_dist(
+            "Seems like the code is not running from"
+            " within a git repo, so hash will"
+            " not be stored. However, it"
+            " is strongly advised to use"
+            " version control.",
+            ranks=[0],
+            level=logging.INFO)
+    # And the git diff
+    try:
+        gitdiff = sh.git.diff(_fg=False, _tty_out=False)
+        with (exp_dir / "gitdiff.log").open("w") as handle:
+            handle.write(gitdiff.stdout.decode("utf-8"))
+    except sh.ErrorReturnCode_129:
+        log_dist(
+            "Seems like the code is not running from"
+            " within a git repo, so diff will"
+            " not be stored. However, it"
+            " is strongly advised to use"
+            " version control.",
+            ranks=[0],
+            level=logging.INFO)
+    # Finally create the Tensorboard Dir
+    tb_dir = exp_dir / "tb_dir"
+    tb_dir.mkdir(exist_ok=False)
+    return exp_dir
+
+
+######################################################################
+################ Checkpoint Related Functions ########################
+######################################################################
+
+
+def load_model_checkpoint(
+    load_checkpoint_dir: pathlib.Path,
+    model: torch.nn.Module,
+    optimizer: torch.optim.Optimizer,
+) -> Tuple[int, torch.nn.Module, torch.optim.Optimizer]:
+    """Loads the optimizer state dict and model state dict from the load_checkpoint_dir
+    into the passed model and optimizer. Searches for the most recent checkpoint to
+    load from
+
+    Args:
+        load_checkpoint_dir (pathlib.Path):
+            The base checkpoint directory to load from
+        model (torch.nn.Module):
+            The model to load the checkpoint weights into
+        optimizer (torch.optim.Optimizer):
+            The optimizer to load the checkpoint weigths into
+
+    Returns:
+        Tuple[int, torch.nn.Module, torch.optim.Optimizer]:
+            The checkpoint step, model with state_dict loaded and
+            optimizer with state_dict loaded
+
+    """
+    log_dist(
+        f"Loading model and optimizer checkpoint from {load_checkpoint_dir}",
+        ranks=[0],
+        level=logging.INFO)
+    checkpoint_files = list(
+        filter(
+            lambda path: re.search(r"iter_(?P<iter_no>\d+)\.pt", path.name) is
+            not None,
+            load_checkpoint_dir.glob("*.pt"),
+        ))
+    assert len(checkpoint_files) > 0, "No checkpoints found in directory"
+    checkpoint_files = sorted(
+        checkpoint_files,
+        key=lambda path: int(
+            re.search(r"iter_(?P<iter_no>\d+)\.pt", path.name).group("iter_no")
+        ),
+    )
+    latest_checkpoint_path = checkpoint_files[-1]
+    checkpoint_step = int(
+        re.search(r"iter_(?P<iter_no>\d+)\.pt",
+                  latest_checkpoint_path.name).group("iter_no"))
+
+    state_dict = torch.load(latest_checkpoint_path)
+    model.load_state_dict(state_dict["model"], strict=True)
+    optimizer.load_state_dict(state_dict["optimizer"])
+    log_dist(
+        f"Loading model and optimizer checkpoints done. Loaded from {latest_checkpoint_path}",
+        ranks=[0],
+        level=logging.INFO)
+    return checkpoint_step, model, optimizer
+
+
+######################################################################
+######################## Driver Functions ############################
+######################################################################
+
+
+def train(
+        checkpoint_dir: str = None,
+        load_checkpoint_dir: str = None,
+        # Dataset Parameters
+        mask_prob: float = 0.15,
+        random_replace_prob: float = 0.1,
+        unmask_replace_prob: float = 0.1,
+        max_seq_length: int = 512,
+        tokenizer: str = "roberta-base",
+        # Model Parameters
+        num_layers: int = 6,
+        num_heads: int = 8,
+        ff_dim: int = 512,
+        h_dim: int = 256,
+        dropout: float = 0.1,
+        # Training Parameters
+        batch_size: int = 8,
+        num_iterations: int = 10000,
+        checkpoint_every: int = 1000,
+        log_every: int = 10,
+        local_rank: int = -1,
+) -> pathlib.Path:
+    """Trains a [Bert style](https://arxiv.org/pdf/1810.04805.pdf)
+    (transformer encoder only) model for MLM Task
+
+    Args:
+        checkpoint_dir (str):
+            The base experiment directory to save experiments to
+        mask_prob (float, optional):
+            The fraction of tokens to mask. Defaults to 0.15.
+        random_replace_prob (float, optional):
+            The fraction of masked tokens to replace with random token.
+            Defaults to 0.1.
+        unmask_replace_prob (float, optional):
+            The fraction of masked tokens to leave unchanged.
+            Defaults to 0.1.
+        max_seq_length (int, optional):
+            The maximum sequence length of the examples. Defaults to 512.
+        tokenizer (str, optional):
+            The tokenizer to use. Defaults to "roberta-base".
+        num_layers (int, optional):
+            The number of layers in the Bert model. Defaults to 6.
+        num_heads (int, optional):
+            Number of attention heads to use. Defaults to 8.
+        ff_dim (int, optional):
+            Size of the intermediate dimension in the FF layer.
+            Defaults to 512.
+        h_dim (int, optional):
+            Size of intermediate representations.
+            Defaults to 256.
+        dropout (float, optional):
+            Amout of Dropout to use. Defaults to 0.1.
+        batch_size (int, optional):
+            The minibatch size. Defaults to 8.
+        num_iterations (int, optional):
+            Total number of iterations to run the model for.
+            Defaults to 10000.
+        checkpoint_every (int, optional):
+            Save checkpoint after these many steps.
+
+            ..note ::
+
+                You want this to be frequent enough that you can
+                resume training in case it crashes, but not so much
+                that you fill up your entire storage !
+
+            Defaults to 1000.
+        log_every (int, optional):
+            Print logs after these many steps. Defaults to 10.
+        local_rank (int, optional):
+            Which GPU to run on (-1 for CPU). Defaults to -1.
+
+    Returns:
+        pathlib.Path: The final experiment directory
+
+    """
+    device = (torch.device("cuda", local_rank) if (local_rank > -1)
+              and torch.cuda.is_available() else torch.device("cpu"))
+    ################################
+    ###### Create Exp. Dir #########
+    ################################
+    if checkpoint_dir is None and load_checkpoint_dir is None:
+        log_dist(
+            "Need to specify one of checkpoint_dir"
+            " or load_checkpoint_dir",
+            ranks=[0],
+            level=logging.ERROR)
+        return
+    if checkpoint_dir is not None and load_checkpoint_dir is not None:
+        log_dist(
+            "Cannot specify both checkpoint_dir"
+            " and load_checkpoint_dir",
+            ranks=[0],
+            level=logging.ERROR)
+        return
+    if checkpoint_dir:
+        log_dist("Creating Experiment Directory",
+                 ranks=[0],
+                 level=logging.INFO)
+        checkpoint_dir = pathlib.Path(checkpoint_dir)
+        checkpoint_dir.mkdir(exist_ok=True)
+        all_arguments = {
+            # Dataset Params
+            "mask_prob": mask_prob,
+            "random_replace_prob": random_replace_prob,
+            "unmask_replace_prob": unmask_replace_prob,
+            "max_seq_length": max_seq_length,
+            "tokenizer": tokenizer,
+            # Model Params
+            "num_layers": num_layers,
+            "num_heads": num_heads,
+            "ff_dim": ff_dim,
+            "h_dim": h_dim,
+            "dropout": dropout,
+            # Training Params
+            "batch_size": batch_size,
+            "num_iterations": num_iterations,
+            "checkpoint_every": checkpoint_every,
+        }
+        exp_dir = create_experiment_dir(checkpoint_dir, all_arguments)
+        log_dist(f"Experiment Directory created at {exp_dir}",
+                 ranks=[0],
+                 level=logging.INFO)
+    else:
+        log_dist("Loading from Experiment Directory",
+                 ranks=[0],
+                 level=logging.INFO)
+        load_checkpoint_dir = pathlib.Path(load_checkpoint_dir)
+        assert load_checkpoint_dir.exists()
+        with (load_checkpoint_dir / "hparams.json").open("r") as handle:
+            hparams = json.load(handle)
+        # Set the hparams
+        # Dataset Params
+        mask_prob = hparams.get("mask_prob", mask_prob)
+        tokenizer = hparams.get("tokenizer", tokenizer)
+        random_replace_prob = hparams.get("random_replace_prob",
+                                          random_replace_prob)
+        unmask_replace_prob = hparams.get("unmask_replace_prob",
+                                          unmask_replace_prob)
+        max_seq_length = hparams.get("max_seq_length", max_seq_length)
+        # Model Params
+        ff_dim = hparams.get("ff_dim", ff_dim)
+        h_dim = hparams.get("h_dim", h_dim)
+        dropout = hparams.get("dropout", dropout)
+        num_layers = hparams.get("num_layers", num_layers)
+        num_heads = hparams.get("num_heads", num_heads)
+        # Training Params
+        batch_size = hparams.get("batch_size", batch_size)
+        _num_iterations = hparams.get("num_iterations", num_iterations)
+        num_iterations = max(num_iterations, _num_iterations)
+        checkpoint_every = hparams.get("checkpoint_every", checkpoint_every)
+        exp_dir = load_checkpoint_dir
+    # Tensorboard writer
+    if is_rank_0():
+        tb_dir = exp_dir / "tb_dir"
+        assert tb_dir.exists()
+        summary_writer = SummaryWriter(log_dir=tb_dir)
+    ################################
+    ###### Create Datasets #########
+    ################################
+    log_dist("Creating Datasets", ranks=[0], level=logging.INFO)
+    data_iterator = create_data_iterator(
+        mask_prob=mask_prob,
+        random_replace_prob=random_replace_prob,
+        unmask_replace_prob=unmask_replace_prob,
+        tokenizer=tokenizer,
+        max_seq_length=max_seq_length,
+        batch_size=batch_size,
+    )
+    log_dist("Dataset Creation Done", ranks=[0], level=logging.INFO)
+    ################################
+    ###### Create Model ############
+    ################################
+    log_dist("Creating Model", ranks=[0], level=logging.INFO)
+    model = create_model(
+        num_layers=num_layers,
+        num_heads=num_heads,
+        ff_dim=ff_dim,
+        h_dim=h_dim,
+        dropout=dropout,
+    )
+    log_dist("Model Creation Done", ranks=[0], level=logging.INFO)
+    ################################
+    ###### DeepSpeed engine ########
+    ################################
+    log_dist("Creating DeepSpeed engine", ranks=[0], level=logging.INFO)
+    ds_config = {
+        "train_micro_batch_size_per_gpu": batch_size,
+        "optimizer": {
+            "type": "Adam",
+            "params": {
+                "lr": 1e-4
+            }
+        },
+        "fp16": {
+            "enabled": True
+        },
+        "zero_optimization": {
+            "stage": 1,
+            "offload_optimizer": {
+                "device": "cpu"
+            }
+        }
+    }
+    model, _, _, _ = deepspeed.initialize(model=model,
+                                          model_parameters=model.parameters(),
+                                          config=ds_config)
+    log_dist("DeepSpeed engine created", ranks=[0], level=logging.INFO)
+    ################################
+    #### Load Model checkpoint #####
+    ################################
+    start_step = 1
+    if load_checkpoint_dir is not None:
+        _, client_state = model.load_checkpoint(load_dir=load_checkpoint_dir)
+        checkpoint_step = client_state['checkpoint_step']
+        start_step = checkpoint_step + 1
+
+    ################################
+    ####### The Training Loop ######
+    ################################
+    log_dist(
+        f"Total number of model parameters: {sum([p.numel() for p in model.parameters()]):,d}",
+        ranks=[0],
+        level=logging.INFO)
+    model.train()
+    losses = []
+    for step, batch in enumerate(data_iterator, start=start_step):
+        if step >= num_iterations:
+            break
+        # Move the tensors to device
+        for key, value in batch.items():
+            batch[key] = value.to(device)
+        # Forward pass
+        loss = model(**batch)
+        # Backward pass
+        model.backward(loss)
+        # Optimizer Step
+        model.step()
+        losses.append(loss.item())
+        if step % log_every == 0:
+            log_dist("Loss: {0:.4f}".format(np.mean(losses)),
+                     ranks=[0],
+                     level=logging.INFO)
+            if is_rank_0():
+                summary_writer.add_scalar(f"Train/loss", np.mean(losses), step)
+        if step % checkpoint_every == 0:
+            model.save_checkpoint(save_dir=exp_dir,
+                                  client_state={'checkpoint_step': step})
+            log_dist("Saved model to {0}".format(exp_dir),
+                     ranks=[0],
+                     level=logging.INFO)
+    # Save the last checkpoint if not saved yet
+    if step % checkpoint_every != 0:
+        model.save_checkpoint(save_dir=exp_dir,
+                              client_state={'checkpoint_step': step})
+        log_dist("Saved model to {0}".format(exp_dir),
+                 ranks=[0],
+                 level=logging.INFO)
+
+    return exp_dir
+
+
+if __name__ == "__main__":
+    torch.manual_seed(42)
+    np.random.seed(0)
+    random.seed(0)
+    fire.Fire(train)

Deepspeed/LICENSE

+    MIT License
+
+    Copyright (c) Microsoft Corporation.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/LICENSE

+The following applies to all files unless otherwise noted:
+
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+--
+
+This repository also contains code from Hugging Face Inc., Google Research,
+and Facebook (from their Fairseq project). Files from these
+organizations have notices at the top of each file. Below are licenses
+used in those files, as indicated.
+
+
+------------- LICENSE FOR huggingface and Google Research code  --------------
+
+
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+                           Version 2.0, January 2004
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+
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Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/MANIFEST.in

+include megatron/data/Makefile
+include megatron/data/helpers.cpp

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/README.md

+This is a snapshot of Megatron v1.1.5 integrated with DeepSpeed's pipeline- and data-parallel training. This 3D parallelism integration
+can train a model with [one trillion parameters](https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/) using as few as 800 NVIDIA V100 GPUs.
+
+See `examples/ds_pretrain_gpt2_pipe.sh` for an entry point to training with 3D parallelism.
+
+See our [pull request](https://github.com/jeffra/DSE/pull/10) for a more detailed view of the integration.
+
+
+[Megatron](https://arxiv.org/pdf/1909.08053.pdf) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research on training large transformer language models at scale. We developed efficient, model-parallel, and multinode training of [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and [BERT](https://arxiv.org/pdf/1810.04805.pdf) using mixed precision.
+
+Using our GPT-2 model we achieve a perplexity of 10.8 on the WikiText-103 dataset (improving SOTA from 15.8) and an accuracy of 66.5% on the LAMBADA datasets. For BERT training, we swapped the position of the layer normalization and the residual connection in the model architecture (similar to GPT-2 architucture), which allowed the models to continue to improve as they were scaled up. Our BERT models with 3.9 billion parameters reaches a loss of 1.16, SQuAD 2.0 F1-score of 91.7, and RACE accuracy of 90.9%.
+
+Our codebase is capable of efficiently training very large (several billion parameter) language models with both model and data parallelism. To demonstrate how the code scales with multiple GPUs we consider the following GPT-2 model sizes. All models use a vocabulary size of 51,200 and a sequence length of 1024.
+
+![Cases](images/cases.png)
+
+The table below details the weak scaling from 1 to 8 GPUs of our model parallelism code in both a DGX-2 and a DGX-A100. Notice that we double the batch size on the DGX-A100 but the iteration time decreases compared to the DGX-2 resulting in a **2.1x** speedup for the end-to-end application.
+
+![Model Parallel Scaling](images/scaling-mp.png)
+
+The following table details how Megatron scales using data parallelism in conjuction with model parallelism in a cluster of DGX-A100s. All of these cases use 128-way data parallelism and the scaling numbers are relative to a single A100 (Case 1B with a 1076ms iteration time).
+
+![Data Parallel Scaling](images/scaling-dp.png)
+
+<a id="contents"></a>
+# Contents
+<!-- MarkdownTOC -->
+
+- [Setup](#setup)
+  - [Downloading Checkpoints](#downloading-checkpoints)
+- [Usage](#usage)
+- [Training](#training)
+  - [Data Preprocessing](#data-preprocessing)
+  - [BERT Pretraining](#bert-pretraining)
+  - [GPT-2 Pretraining](#gpt-2-pretraining)
+  - [Distributed BERT or GPT-2 Pretraining](#distributed-bert-or-gpt-2-pretraining)
+- [REALM Pipeline](#realm)
+- [Evaluation and Tasks](#evaluation-and-tasks)
+  - [GPT-2 Text Generation](#gpt-2-text-generation)
+  - [GPT-2 Evaluation](#gpt-2-evaluation)
+    - [WikiText Perplexity Evaluation](#wikitext-perplexity-evaluation)
+    - [LAMBADA Cloze Accuracy](#lambada-cloze-accuracy)
+  - [BERT Task Evaluation](#bert-task-evaluation)
+    - [RACE Evaluation](#race-evaluation)
+    - [MNLI Evaluation](#mnli-evaluation)
+- [Datasets](#datasets)
+  - [Collecting Wikipedia Training Data](#collecting-wikipedia-training-data)
+  - [Collecting GPT-2 Webtext Data](#collecting-gpt-2-webtext-data)
+
+<!-- /MarkdownTOC -->
+
+<a id="setup"></a>
+# Setup
+We officially support only python 3.6, pytorch 1.5, cuda 10, and nccl 2.6 versions and above.
+
+To use this repo please install the latest supported versions of PyTorch with GPU support and NVIDIA [APEX](https://github.com/NVIDIA/apex#quick-start). We strongly recommend using one of [NGC's recent PyTorch containers](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) (the latest compatible version at time of publication can be pulled with `docker pull nvcr.io/nvidia/pytorch:20.03-py3`). Data preprocessing requires [NLTK](https://www.nltk.org/install.html), though this is not required for training, evaluation or downstream tasks.
+
+To use megatron you can either clone the repo or install it via pip (make sure python3-dev is installed):
+<pre>
+pip install megatron-lm
+</pre>
+
+<a id="downloading-checkpoints"></a>
+## Downloading Checkpoints
+We've provided two pretrained checkpoints for use to evaluate or finetuning downstream tasks. To access these checkpoints, first please [sign up](https://ngc.nvidia.com/signup) for and [setup](https://ngc.nvidia.com/setup/installers/cli) the NVIDIA GPU Cloud (NGC) Registry CLI.
+
+The checkpoints can be downloaded with:
+<pre>
+ngc registry model download-version --dest &#60;output_base_directory&#62; nvidia/&#60;model_name&#62;:&#60;version&#62;
+</pre>
+
+The available models along with `<model_name>:<version>` are below:
+* [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m): megatron\_bert\_345m:v0.0
+* [GPT-2-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m): megatron\_lm\_345m:v0.0
+
+The models require vocabulary files to run. The BERT uncased WordPiece vocab file can be extracted from Google's [pretrained BERT models](https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt). The GPT-2 [vocab file](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json) and [merge table](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt) can be downloaded directly.
+
+Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1)
+
+<a id="usage"></a>
+# Usage
+
+After installation, there are several possible workflows. The most comprehensive is:
+1. Data preprocessing
+2. Pretraining
+3. Finetuning (Optional for zero-shot tasks)
+4. Downstream task evaluation or text generation
+
+However, steps 1 and 2 can be replaced by using one of the pretrained models mentioned above.
+
+We've provided several scripts for pretraining both BERT and GPT-2 in [`examples`](./examples) directory, as well as scripts for both zero-shot and fine-tuned downstream tasks including MNLI, RACE, WikiText103, and LAMBADA evaluation. There is also a script for GPT-2 interactive text generation.
+
+<a id="training"></a>
+# Training
+<a id="data-preprocessing"></a>
+## Data Preprocessing
+We support three file formats for training, but all require preprocessing. First, place your training data in a loose json format, with one json containing a text sample per line. For example:
+<pre>
+{"src": "www.nvidia.com", "text": "The quick brown fox", "type": "Eng", "id": "0", "title": "First Part"}
+{"src": "The Internet", "text": "jumps over the lazy dog", "type": "Eng", "id": "42", "title": "Second Part"}
+</pre>
+
+The name of the `text` field of the json can be changed by using the `--json-key` flag in [`preprocess_data.py`](./tools/preprocess_data.py) The other metadata are optional and are not used in training.
+
+The loose json is then processed into a binary format for training. To convert the json into mmap, cached index file, or the lazy loader format use `preprocess_data.py`. Set the `--dataset-impl` flag to `mmap`, `cached`, or `lazy`, respectively (default is `mmap`). An example script to prepare data for BERT training is:
+<pre>
+python tools/preprocess_data.py \
+       --input my-corpus.json \
+       --output-prefix my-bert \
+       --vocab bert-vocab.txt \
+       --dataset-impl mmap \
+       --tokenizer-type BertWordPieceLowerCase \
+       --split-sentences
+</pre>
+
+The output will be two files named, in this case, `my-bert_text_sentence.bin` and `my-bert_text_sentence.idx`. The `--data-path` specified in later BERT training is the full path and new filename, but without the file extension.
+
+Some minor modifications are required for GPT-2 data preprocessing, namely, the addition of a merge table, an end-of-document token, removal of sentence splitting, and a change to the tokenizer type:
+<pre>
+python tools/preprocess_data.py \
+       --input my-corpus.json \
+       --output-prefix my-gpt2 \
+       --vocab gpt2-vocab.json \
+       --dataset-impl mmap \
+       --tokenizer-type GPT2BPETokenizer \
+       --merge-file gpt2-merges.txt \
+       --append-eod
+</pre>
+
+Here the output files are named `my-gpt2_text_document.bin` and `my-gpt2_text_document.idx`. As before, in GPT-2 training, use the longer name without the extension as `--data-path`.
+
+Further command line arguments are described in the source file [`preprocess_data.py`](./tools/preprocess_data.py).
+
+<a id="bert-pretraining"></a>
+## BERT Pretraining
+`bash examples/pretrain_bert.sh`
+
+This script runs single GPU 345M parameter BERT pretraining. Debugging is the primary use for single GPU training, as the code base and command line arguments are optimized for highly distributed training. Most of the arguments are fairly self-explanatory. By default, the learning rate decays linearly over the training iterations starting at `--lr` to a minimum set by `--min-lr` over `--lr-decay-iters` iterations. The fraction of training iterations used for warmup is set by `--warmup`. While this is single GPU training, the batch size specified by `--batch-size` is per GPU used for data parallelism. The data is partitioned into a 949:50:1 ratio for training/validation/test sets (default is 969:30:1). This partitioning happens on the fly, but is consistent across runs with the same random seed (1234 by default, or specified manually with `--seed`).
+
+The logging, checkpoint-saving, and evaluation intervals are specified. Checkpointing the activations facilitates the training of larger models and/or batches. Note that the `--data-path` now includes the additional `_text_sentence` suffix added in preprocessing, but does not include the file extensions.
+
+<pre>
+CHECKPOINT_PATH=checkpoints/bert_345m
+VOCAB_FILE=bert-vocab.txt
+DATA_PATH=my-bert_text_sentence
+
+BERT_ARGS="--num-layers 24 \
+           --hidden-size 1024 \
+           --num-attention-heads 16 \
+           --seq-length 512 \
+           --max-position-embeddings 512 \
+           --lr 0.0001 \
+           --train-iters 2000000 \
+           --min-lr 0.00001 \
+           --lr-decay-iters 990000 \
+           --warmup 0.01 \
+           --batch-size 8 \
+           --vocab-file $VOCAB_FILE \
+           --split 949,50,1 \
+           --fp16"
+
+OUTPUT_ARGS="--log-interval 10 \
+             --save-interval 500 \
+             --eval-interval 100 \
+             --eval-iters 10 \
+             --checkpoint-activations"
+
+python pretrain_bert.py \
+       $BERT_ARGS \
+       $OUTPUT_ARGS \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH
+</pre>
+
+Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py).
+
+<a id="gpt-2-pretraining"></a>
+## GPT-2 Pretraining
+`bash examples/pretrain_gpt2.sh`
+
+This script runs single GPU 345M parameter GPT-2 pretraining. As mentioned above, single GPU training is primarily intended for debugging purposes, as the code is optimized for distributed training.
+
+It follows largely the same format as the previous BERT script with a few notable differences: the tokenization scheme used is BPE (which requires a merge table and a `json` vocabulary file) instead of WordPiece, the model architecture allows for longer sequences (note that the max position embedding must be greater than or equal to the maximum sequence length), and the `--lr-decay-style` has been set to cosine decay.  Note that the `--data-path` now includes the additional `_text_document` suffix added in preprocessing, but does not include the file extensions.
+
+<pre>
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+DATA_PATH=my-gpt2_text_document
+
+GPT2_ARGS="--num-layers 24 \
+           --hidden-size 1024 \
+           --num-attention-heads 16 \
+           --seq-length 1024 \
+           --max-position-embeddings 1024 \
+           --batch-size 4 \
+           --lr 0.00015 \
+           --train-iters 500000 \
+           --lr-decay-iters 320000 \
+           --lr-decay-style cosine \
+           --vocab-file $VOCAB_FILE \
+           --merge-file $MERGE_FILE \
+           --warmup .01 \
+           --fp16"
+
+OUTPUT_ARGS=&#60;same as those in <a href="#bert-pretraining">BERT pretraining</a> above&#62;
+
+python pretrain_gpt2.py \
+       $GPT2_ARGS \
+       $OUTPUT_ARGS \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+</pre>
+
+Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py).
+
+<a id="distributed-bert-or-gpt-2-pretraining"></a>
+## Distributed BERT or GPT-2 Pretraining
+`bash examples/pretrain_bert_distributed.sh`
+
+`bash examples/pretrain_gpt2_distributed.sh`
+
+These scripts use the PyTorch distributed launcher for distributed training. As such, multinode training can be achieved by properly setting environment variables and using `init_method='env://'` in the launcher. See the official PyTorch [documentation](https://pytorch.org/docs/stable/distributed.html#launch-utility) for further description of these [environment variables](https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization). By default, multinode training uses the [nccl](https://developer.nvidia.com/nccl) distributed backend. A simple set of additional arguments and the use of the PyTorch distributed module with the Python flag `-m torch.distributed.launch`, detailed below, are the only additional requirements to adopt distributed training.
+
+The two tiers of parallelism are data and model parallelism. First, we facilitate two distributed data parallel implementations: a simple one of our own that performs gradient all-reduce at the end of back propagation step, and Torch's distributed data parallel wrapper that overlaps gradient reduction with back propagation computation. To switch between these two options use `--DDP-impl local` or `--DDP-impl torch`, respectively. As expected, Torch distributed data parallelism is more efficient at larger model parallel sizes. For example, for the 8.3 billion parameters model running on 512 GPUs, the scaling increases from 60% to 76% when Torch's distributed data parallel is used. However, the overlapping method requires more memory and for some configurations (e.g., 2.5 billion parameters using 2-way model parallel and 1.2 billion parameters with no model parallel) can make the overall training slower as a result. We empirically found that using a smaller model in those cases improves the training time.
+
+Second, we developed a simple and efficient intra-layer model parallel approach. To use model parallelism, add the `--model-parallel-size` flag to specify the number of GPUs among which to split the model, along with the arguments passed to the distributed launcher as mentioned above. With `WORLD_SIZE` GPUs and `MP_SIZE` model parallel size, `WORLD_SIZE`/`MP_SIZE` GPUs will be used for data parallelism. The default value for `--model-parallel-size` is 1, which will not implement model parallelism.
+
+Other than these minor changes, the distributed training is identical to the training on a single GPU.
+
+Distributed BERT training:
+<pre>
+WORLD_SIZE=8
+MP_SIZE=2
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+CHECKPOINT_PATH=checkpoints/bert_345m
+VOCAB_FILE=bert-vocab.txt
+DATA_PATH=my-bert_text_sentence
+BERT_ARGS=&#60;same as those in <a href="#bert-pretraining">BERT pretraining</a> above&#62;
+OUTPUT_ARGS=&#60;same as those in <a href="#bert-pretraining">BERT pretraining</a> above&#62;
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_bert.py \
+                $BERT_ARGS \
+                $OUTPUT_ARGS \
+                --save $CHECKPOINT_PATH \
+                --load $CHECKPOINT_PATH \
+                --data-path $DATA_PATH \
+                --model-parallel-size $MP_SIZE \
+                --DDP-impl torch
+</pre>
+
+Distributed GPT-2 training:
+<pre>
+WORLD_SIZE=8
+MP_SIZE=2
+
+DISTRIBUTED_ARGS=&#60;same as those directly above&#62;
+
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+DATA_PATH=my-gpt2_text_document
+GPT2_ARGS=&#60;same as those in <a href="#gpt-2-pretraining">GPT-2 pretraining</a> above&#62;
+OUTPUT_ARGS=&#60;same as those in <a href="#bert-pretraining">BERT pretraining</a> above&#62;
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_gpt2.py \
+                $GPT2_ARGS \
+                $OUTPUT_ARGS \
+                --save $CHECKPOINT_PATH \
+                --load $CHECKPOINT_PATH \
+                --data-path $DATA_PATH \
+                --model-parallel-size $MP_SIZE \
+                --DDP-impl torch
+
+</pre>
+
+<a id="realm"></a>
+## REALM Pipeline
+We are working on implementing the [REALM](https://arxiv.org/pdf/2002.08909.pdf) system. The following sections (will) reflect the three stages of training it. For now it's just the ICT code.
+Loosely, they are pretraining the retriever modules, then jointly training the language model and the retriever, and then finetuning a question answering head on the language model with fixed retriever.
+
+### Inverse Cloze Task (ICT) Pretraining
+1. Have a corpus in loose JSON format with the intention of creating a collection of fixed-size blocks of text as the fundamental units of data. For a corpus like Wikipedia, this will mean multiple sentences per block but also multiple blocks per document.
+Run `tools/preprocess_data.py` to construct one or more indexed datasets with the `--split-sentences` argument to make sentences the basic unit. For the original REALM system, we construct two datasets, one with the title of every document, and another with the body.
+Refer to the following script
+<pre>
+python preprocess_data.py \
+    --input /path/to/corpus.json \
+    --json-keys text title \
+    --split-sentences \
+    --tokenizer-type BertWordPieceLowerCase \
+    --vocab-file /path/to/vocab.txt \
+    --output-prefix corpus_indexed \
+    --workers 5  # works well for 10 CPU cores. Scale up accordingly.
+</pre>
+
+2. Use a custom samples mapping function in place of `megatron/data/realm_dataset_utils.get_block_samples_mapping` if required. To do this, you will need to implement a new function in C++ inside of `megatron/data/helpers.cpp`. The samples mapping data structure is used to select the data that will constitute every training sample in advance of the training loop.
+ The samples mapping is responsible for holding all of the required metadata needed to construct the sample from one or more indexed datasets. In REALM, the samples mapping contains the start and end sentence indices, as well as the document index (to find the correct title for a body) and a unique ID for every block.
+3. Pretrain a BERT language model using `pretrain_bert.py`, with the sequence length equal to the block size in token ids. This model should be trained on the same indexed dataset that is used to supply the blocks for the information retrieval task.
+In REALM, this is an uncased bert base model trained with the standard hyperparameters.
+4. Use `pretrain_ict.py` to train an `ICTBertModel` which uses two BERT-based encoders to encode queries and blocks to perform retrieval with.
+The script below trains the ICT model from REALM. It refrences a pretrained BERT model (step 3) in the `--bert-load` argument. The batch size used in the paper is 4096, so this would need to be run with data parallel world size 32.
+<pre>
+python pretrain_ict.py \
+    --num-layers 12 \
+    --num-attention-heads 12 \
+    --hidden-size 768 \
+    --batch-size 128 \
+    --seq-length 256 \
+    --max-position-embeddings 256 \
+    --ict-head-size 128 \
+    --train-iters 100000 \
+    --checkpoint-activations \
+    --bert-load /path/to/pretrained_bert \
+    --load checkpoints \
+    --save checkpoints \
+    --data-path /path/to/indexed_dataset \
+    --titles-data-path /path/to/titles_indexed_dataset \
+    --vocab-file /path/to/vocab.txt \
+    --lr 0.0001 \
+    --num-workers 2 \
+    --lr-decay-style linear \
+    --weight-decay 1e-2 \
+    --clip-grad 1.0 \
+    --warmup .01 \
+    --save-interval 3000 \
+    --query-in-block-prob 0.1 \
+    --fp16
+
+</pre>
+
+### Building an Index of Block Embeddings
+After having trained an ICT model, you can now embed an entire dataset of blocks by creating a `BlockData` structure. After that has been saved, you can load it
+and wrap it with a `FaissMIPSIndex` to do fast similarity search which is key in the learned information retrieval pipeline. The initial index can be built with the following script, meant to be run in an interactive session. It can leverage multiple GPUs on multiple nodes to index large datasets much more quickly.
+
+<pre>
+python tools/create_doc_index.py \
+    --num-layers 12 \
+    --hidden-size 768 \
+    --ict-head-size 128 \
+    --num-attention-heads 12 \
+    --batch-size 128 \
+    --checkpoint-activations \
+    --seq-length 256 \
+    --max-position-embeddings 256 \
+    --ict-load /path/to/pretrained_ict \
+    --data-path /path/to/indexed_dataset \
+    --titles-data-path /path/to/titles_indexed_dataset \
+    --block-data-path embedded_blocks.pkl \
+    --indexer-log-interval 1000 \
+    --indexer-batch-size 128 \
+    --vocab-file /path/to/vocab.txt \
+    --num-workers 2 \
+    --fp16
+</pre>
+
+<a id="evaluation-and-tasks"></a>
+# Evaluation and Tasks
+
+We provide several command line arguments, detailed in the scripts listed below, to handle various zero-shot and fine-tuned downstream tasks. However, you can also finetune your model from a pretrained checkpoint on other corpora as desired. To do so, simply add the `--finetune` flag and adjust the input files and training parameters within the original training script. The iteration count will be reset to zero, and the optimizer and internal state will be reinitialized. If the fine-tuning is interrupted for any reason, be sure to remove the `--finetune` flag before continuing, otherwise the training will start again from the beginning.
+
+Because evaluation requires substantially less memory than training, it may be advantageous to merge a model trained in parallel for use on a single GPU in downstream tasks. The following script accomplishes this.
+
+<pre>
+MODEL_PARALLEL_SIZE=2
+
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m
+
+WORLD_SIZE=$MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
+        --model-type BERT \
+        --model-parallel-size $MODEL_PARALLEL_SIZE \
+        --tokenizer-type BertWordPieceLowerCase \
+        --vocab-file $VOCAB_FILE \
+        --num-layers 24 \
+        --hidden-size 1024 \
+        --num-attention-heads 16 \
+        --seq-length 512 \
+        --max-position-embeddings 512 \
+        --load $CHECKPOINT_PATH
+
+</pre>
+
+Several downstream tasks are described for both GPT-2 and BERT models below. They can be run in distributed and model parallel modes with the same changes used in the training scripts.
+
+<a id="gpt-2-text-generation"></a>
+## GPT-2 Text Generation
+`bash examples/generate_text.sh`
+
+We generate text samples using largely the GPT-2 pretraining script. Few changes need to make, such as we need to provide the path to the pretrained checkpoint, the length of the output samples, whether to generate texts unconditionally (`--num-samples` to denote how many samples to generate) or conditional (need to pass `--sample-input-file <filename>` where each line of the file will be used as the conditional texts). There are few optional parameters to play, e.g. `top-k`, `top-p`, or `greedy` (set top-k and top-p to 0) sampling..
+
+<pre>
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+GPT2_ARGS=&#60;same as those in <a href="#gpt-2-pretraining">GPT-2 pretraining</a> above&#62;
+
+MAX_OUTPUT_SEQUENCE_LENGTH=1024
+TEMPERATURE=1.0
+TOP_P=0.9
+NUMBER_OF_SAMPLES=2
+OUTPUT_FILE=samples.json
+
+python tools/generate_samples_gpt2.py \
+       $GPT2_ARGS \
+       --load $CHECKPOINT_PATH \
+       --out-seq-length $MAX_OUTPUT_SEQUENCE_LENGTH \
+       --temperature $TEMPERATURE \
+       --genfile $OUTPUT_FILE \
+       --num-samples $NUMBER_OF_SAMPLES \
+       --top_p $TOP_P \
+       --recompute
+</pre>
+
+<a id="gpt-2-evaluation"></a>
+## GPT-2 Evaluation
+We include example scripts for GPT-2 evaluation on WikiText perplexity evaluation and LAMBADA Cloze accuracy.
+
+<a id="wikitext-perplexity-evaluation"></a>
+### WikiText Perplexity Evaluation
+For even comparison with prior works, we evaluate perplexity on the word-level [WikiText-103 test dataset](https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip), and appropriately compute perplexity given the change in tokens when using our subword tokenizer.
+
+We use the following command to run WikiText-103 evaluation on a 345M parameter model.
+<pre>
+TASK="WIKITEXT103"
+
+VALID_DATA=&#60;wikitext path&#62;.txt
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+
+COMMON_TASK_ARGS="--num-layers 24 \
+                  --hidden-size 1024 \
+                  --num-attention-heads 16 \
+                  --seq-length 1024 \
+                  --max-position-embeddings 1024 \
+                  --fp16 \
+                  --vocab-file $VOCAB_FILE"
+
+python tasks/main.py \
+       --task $TASK \
+       $COMMON_TASK_ARGS \
+       --valid-data $VALID_DATA \
+       --tokenizer-type GPT2BPETokenizer \
+       --merge-file $MERGE_FILE \
+       --load $CHECKPOINT_PATH \
+       --batch-size 8 \
+       --checkpoint-activations \
+       --log-interval 10 \
+       --no-load-optim \
+       --no-load-rng
+</pre>
+
+
+<a id="lambada-cloze-accuracy"></a>
+### LAMBADA Cloze Accuracy
+To compute LAMBADA cloze accuracy (the accuracy of predicting the last token given the preceeding tokens) we utilize a detokenized, processed version of the [LAMBADA dataset](https://github.com/cybertronai/bflm/blob/master/lambada_test.jsonl).
+
+We use the following command to run LAMBADA evaluation on a 345M parameter model. Note that the `--strict-lambada` flag should be used to require whole word matching. Make that `lambada` is part of the file path.
+
+<pre>
+TASK="LAMBADA"
+
+VALID_DATA=&#60;lambada path&#62;.json
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+COMMON_TASK_ARGS=&#60;same as those in <a href="#wikitext-perplexity-evaluation">WikiText Perplexity Evaluation</a> above&#62;
+
+python tasks/main.py \
+       --task $TASK \
+       $COMMON_TASK_ARGS \
+       --valid-data $VALID_DATA \
+       --tokenizer-type GPT2BPETokenizer \
+       --strict-lambada \
+       --merge-file $MERGE_FILE \
+       --load $CHECKPOINT_PATH \
+       --batch-size 8 \
+       --checkpoint-activations \
+       --log-interval 10 \
+       --no-load-optim \
+       --no-load-rng
+</pre>
+
+Further command line arguments are described in the source file [`main.py`](./tasks/main.py)
+
+<a id="bert-task-evaluation"></a>
+## BERT Task Evaluation
+<a id="race-evaluation"></a>
+### RACE Evaluation
+The following script finetunes the BERT model for evaluation on the [RACE dataset](http://www.cs.cmu.edu/~glai1/data/race/). The `TRAIN_DATA` and `VALID_DATA` directory contain the RACE dataset as separate `.txt` files.
+
+<pre>
+TRAIN_DATA="data/RACE/train/middle"
+VALID_DATA="data/RACE/dev/middle \
+            data/RACE/dev/high"
+VOCAB_FILE=bert-vocab.txt
+PRETRAINED_CHECKPOINT=checkpoints/bert_345m
+CHECKPOINT_PATH=checkpoints/bert_345m_race
+COMMON_TASK_ARGS="--num-layers 24 \
+                  --hidden-size 1024 \
+                  --num-attention-heads 16 \
+                  --seq-length 512 \
+                  --max-position-embeddings 512 \
+                  --fp16 \
+                  --vocab-file $VOCAB_FILE"
+
+COMMON_TASK_ARGS_EXT="--train-data $TRAIN_DATA \
+                      --valid-data $VALID_DATA \
+                      --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
+                      --checkpoint-activations \
+                      --save-interval 10000 \
+                      --save $CHECKPOINT_PATH \
+                      --log-interval 100 \
+                      --eval-interval 1000 \
+                      --eval-iters 10 \
+                      --weight-decay 1.0e-1"
+
+python tasks/main.py \
+       --task RACE \
+       $COMMON_TASK_ARGS \
+       $COMMON_TASK_ARGS_EXT \
+       --tokenizer-type BertWordPieceLowerCase \
+       --epochs 3 \
+       --batch-size 4 \
+       --lr 1.0e-5 \
+       --warmup 0.06
+</pre>
+
+<a id="mnli-evaluation"></a>
+### MNLI Evaluation
+The following script finetunes the BERT model for evaluation with the [MultiNLI sentence pair corpus](https://www.nyu.edu/projects/bowman/multinli/). Because the matching tasks are quite similar, the script can be quickly tweaked to work with the [Quora Question Pairs](https://www.kaggle.com/quora/question-pairs-dataset) (QQP) dataset as well.
+
+<pre>
+
+TRAIN_DATA="data/glue_data/MNLI/train.tsv"
+VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \
+            data/glue_data/MNLI/dev_mismatched.tsv"
+PRETRAINED_CHECKPOINT=checkpoints/bert_345m
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m_mnli
+COMMON_TASK_ARGS=&#60;same as those in <a href="#race-evaluation">RACE Evaluation</a> above&#62;
+COMMON_TASK_ARGS_EXT=&#60;same as those in <a href="#race-evaluation">RACE Evaluation</a> above&#62;
+
+python tasks/main.py \
+       --task MNLI \
+       $COMMON_TASK_ARGS \
+       $COMMON_TASK_ARGS_EXT \
+       --tokenizer-type BertWordPieceLowerCase \
+       --epochs 5 \
+       --batch-size 8 \
+       --lr 5.0e-5 \
+       --warmup 0.065
+</pre>
+
+<a id="datasets"></a>
+# Datasets
+We do not host any datasets for GPT-2 or BERT training, however, we detail their collection so that our results may be reproduced.
+
+<a id="collecting-wikipedia-training-data"></a>
+## Collecting Wikipedia Training Data
+We recommend following the Wikipedia data extraction process specified by Google research: "the recommended pre-processing is to download [the latest dump](https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2), extract the text with [WikiExtractor.py](https://github.com/attardi/wikiextractor), and then apply any necessary cleanup to convert it into plain text."
+
+We recommend using the `--json` argument when using WikiExtractor, which will dump the Wikipedia data into loose json format (one json per line), making it more manageable on the file system and also readily consumable by our codebase. We recommend further preprocessing this json dataset by nltk punctuation standardization. For BERT training, add newlines between sentences during data preprocessing. This is done with the `--split-sentences` flag in `preprocess_data.py` as described [above](#data-preprocessing). (Note that if you'd like to use Wikipedia data for GPT-2 training you should still clean it with nltk/spacy/ftfy, but do not split it into newline separated sentences.)
+
+<a id="collecting-gpt-2-webtext-data"></a>
+## Collecting GPT-2 Webtext Data
+We utilize the publicly available [OpenWebText](https://github.com/eukaryote31/openwebtext) library from [jcpeterson](https://github.com/jcpeterson/openwebtext) and [eukaryote31's](https://github.com/eukaryote31/openwebtext) work to download urls. We then filtered, cleaned, and deduplicated all downloaded content according to the procedure described in our [openwebtext](./tools/openwebtext) directory. For reddit URLs corresponding to content up to October 2018 we arrived at approximately 37GB of content.

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/changes.md

+PRETEND THESE ARE CODE CHANGES

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/ds_config.json

+{
+  "train_batch_size": 256,
+  "train_micro_batch_size_per_gpu": 4,
+  "steps_per_print": 10,
+  "gradient_clipping": 1.0,
+  "fp16": {
+    "enabled": true,
+
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "wall_clock_breakdown": true,
+  "zero_allow_untested_optimizer": false
+}

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/ds_pretrain_gpt2.sh

+#! /bin/bash
+
+GPUS_PER_NODE=8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NNODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
+
+export DLWS_NUM_WORKER=${NNODES}
+export DLWS_NUM_GPU_PER_WORKER=${GPUS_PER_NODE}
+
+DATA_PATH=data/webtext/webtext_text_document
+VOCAB_PATH=data/gpt2-vocab.json
+MERGE_PATH=data/gpt2-merges.txt
+CHECKPOINT_PATH=checkpoints/gpt2_345m_ds
+
+script_path=$(realpath $0)
+script_dir=$(dirname $script_path)
+config_json="$script_dir/ds_zero_stage_2_config.json"
+
+# Megatron Model Parallelism
+mp_size=4
+
+NLAYERS=24
+NHIDDEN=1024
+BATCHSIZE=9
+LOGDIR="tensorboard_data/${NLAYERS}l_${NHIDDEN}h_${NNODES}n_${GPUS_PER_NODE}g_${mp_size}mp_${BATCHSIZE}b_ds4"
+
+#ZeRO Configs
+stage=0
+reduce_scatter=true
+contigious_gradients=true
+rbs=50000000
+agbs=5000000000
+
+#Actication Checkpointing and Contigious Memory
+chkp_layers=1
+PA=true
+PA_CPU=false
+CC=true
+SYNCHRONIZE=true
+PROFILE=false
+
+
+gpt_options=" \
+        --model-parallel-size ${mp_size} \
+        --num-layers $NLAYERS \
+        --hidden-size $NHIDDEN \
+        --num-attention-heads 16 \
+        --seq-length 1024 \
+        --max-position-embeddings 1024 \
+        --batch-size $BATCHSIZE \
+        --train-iters 320000 \
+        --lr-decay-iters 320000 \
+        --save $CHECKPOINT_PATH \
+        --load $CHECKPOINT_PATH \
+        --data-path $DATA_PATH \
+        --vocab-file $VOCAB_PATH \
+        --merge-file $MERGE_PATH \
+        --data-impl mmap \
+        --split 949,50,1 \
+        --distributed-backend nccl \
+        --lr 1.5e-4 \
+        --lr-decay-style cosine \
+        --min-lr 1.0e-5 \
+        --weight-decay 1e-2 \
+        --clip-grad 1.0 \
+        --warmup 0.01 \
+        --checkpoint-activations \
+        --log-interval 100 \
+        --save-interval 10000 \
+        --eval-interval 1000 \
+        --eval-iters 10 \
+        --fp16 \
+        --tensorboard-dir ${LOGDIR}
+"
+  
+ deepspeed_options=" \
+                --deepspeed \
+                --deepspeed_config ${config_json} \
+                --zero-stage ${stage} \
+                --zero-reduce-bucket-size ${rbs} \
+                --zero-allgather-bucket-size ${agbs} 
+            "
+
+if [ "${contigious_gradients}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+                --zero-contigious-gradients"
+fi
+
+if [ "${reduce_scatter}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+                --zero-reduce-scatter"
+fi
+
+chkp_opt=" \
+--checkpoint-activations \
+--checkpoint-num-layers ${chkp_layers}"
+
+if [ "${PA}" = "true" ]; then
+chkp_opt="${chkp_opt} \
+        --partition-activations"
+fi
+
+if [ "${PA_CPU}" = "true" ]; then
+chkp_opt="${chkp_opt} \
+        --checkpoint-in-cpu"
+fi
+
+if [ "${SYNCHRONIZE}" = "true" ]; then
+chkp_opt="${chkp_opt} \
+        --synchronize-each-layer"
+fi
+
+if [ "${CC}" = "true" ]; then
+chkp_opt="${chkp_opt} \
+        --contigious-checkpointing"
+fi
+
+if [ "${PROFILE}" = "true" ]; then
+chkp_opt="${chkp_opt} \
+        --profile-backward"
+fi
+
+full_options="${gpt_options} ${deepspeed_options} ${chkp_opt}"
+
+run_cmd="deepspeed --num_nodes ${DLWS_NUM_WORKER} --num_gpus ${DLWS_NUM_GPU_PER_WORKER} pretrain_gpt2.py $@ ${full_options}"
+echo ${run_cmd}
+eval ${run_cmd}
+
+set +x

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/ds_pretrain_gpt2_pipe.sh

+#! /bin/bash
+
+GPUS_PER_NODE=16
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NNODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
+
+export DLWS_NUM_WORKER=${NNODES}
+export DLWS_NUM_GPU_PER_WORKER=${GPUS_PER_NODE}
+
+DATA_PATH=data/webtext/webtext_text_document
+VOCAB_PATH=data/gpt2-vocab.json
+MERGE_PATH=data/gpt2-merges.txt
+CHECKPOINT_PATH=checkpoints/gpt2_345m_ds
+
+script_path=$(realpath $0)
+script_dir=$(dirname $script_path)
+#config_json="$script_dir/ds_zero_stage_2_config.json"
+config_json="$script_dir/ds_config.json"
+
+# Megatron Model Parallelism
+mp_size=2
+# DeepSpeed Pipeline parallelism
+pp_size=2
+
+NLAYERS=24
+NHIDDEN=1024
+BATCHSIZE=4
+LOGDIR="tensorboard_data/${NLAYERS}l_${NHIDDEN}h_${NNODES}n_${GPUS_PER_NODE}g_${pp_size}pp_${mp_size}mp_${BATCHSIZE}b_ds4"
+
+GAS=16
+
+#ZeRO Configs
+stage=0
+reduce_scatter=true
+contigious_gradients=true
+rbs=50000000
+agbs=5000000000
+
+#Actication Checkpointing and Contigious Memory
+chkp_layers=1
+PA=true
+PA_CPU=false
+CC=true
+SYNCHRONIZE=true
+PROFILE=false
+
+
+gpt_options=" \
+        --model-parallel-size ${mp_size} \
+        --pipe-parallel-size ${pp_size} \
+        --num-layers $NLAYERS \
+        --hidden-size $NHIDDEN \
+        --num-attention-heads 16 \
+        --seq-length 1024 \
+        --max-position-embeddings 1024 \
+        --batch-size $BATCHSIZE \
+        --gas $GAS \
+        --train-iters 320000 \
+        --lr-decay-iters 320000 \
+        --save $CHECKPOINT_PATH \
+        --load $CHECKPOINT_PATH \
+        --data-path $DATA_PATH \
+        --vocab-file $VOCAB_PATH \
+        --merge-file $MERGE_PATH \
+        --data-impl mmap \
+        --split 949,50,1 \
+        --distributed-backend nccl \
+        --lr 1.5e-4 \
+        --lr-decay-style cosine \
+        --min-lr 1.0e-5 \
+        --weight-decay 1e-2 \
+        --clip-grad 1.0 \
+        --warmup 0.01 \
+        --checkpoint-activations \
+        --log-interval 1 \
+        --save-interval 500 \
+        --eval-interval 100 \
+        --eval-iters 10 \
+        --fp16 \
+        --tensorboard-dir ${LOGDIR}
+"
+  
+ deepspeed_options=" \
+                --deepspeed \
+                --deepspeed_config ${config_json} \
+                --zero-stage ${stage} \
+                --zero-reduce-bucket-size ${rbs} \
+                --zero-allgather-bucket-size ${agbs} 
+            "
+
+if [ "${contigious_gradients}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+                --zero-contigious-gradients"
+fi
+
+if [ "${reduce_scatter}" = "true" ]; then
+deepspeed_options="${deepspeed_options} \
+                --zero-reduce-scatter"
+fi
+
+chkp_opt=" \
+--checkpoint-activations \
+--checkpoint-num-layers ${chkp_layers}"
+
+if [ "${PA}" = "true" ]; then
+chkp_opt="${chkp_opt} \
+        --partition-activations"
+fi
+
+if [ "${PA_CPU}" = "true" ]; then
+chkp_opt="${chkp_opt} \
+        --checkpoint-in-cpu"
+fi
+
+if [ "${SYNCHRONIZE}" = "true" ]; then
+chkp_opt="${chkp_opt} \
+        --synchronize-each-layer"
+fi
+
+if [ "${CC}" = "true" ]; then
+chkp_opt="${chkp_opt} \
+        --contigious-checkpointing"
+fi
+
+if [ "${PROFILE}" = "true" ]; then
+chkp_opt="${chkp_opt} \
+        --profile-backward"
+fi
+
+full_options="${gpt_options} ${deepspeed_options} ${chkp_opt}"
+
+run_cmd="deepspeed --num_nodes ${DLWS_NUM_WORKER} --num_gpus ${DLWS_NUM_GPU_PER_WORKER} pretrain_gpt2.py $@ ${full_options}"
+echo ${run_cmd}
+eval ${run_cmd}
+
+set +x

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/ds_zero_stage_2_config.json

+{
+  "train_batch_size": 2048,
+  "gradient_accumulation_steps": 1,
+  "steps_per_print": 1,
+  "zero_optimization": {
+    "stage": 2,
+    "allgather_partitions": true,
+    "reduce_scatter": true,
+    "allgather_bucket_size": 50000000,
+    "reduce_bucket_size": 50000000,
+    "overlap_comm": true
+  },
+  "optimizer": {
+    "type": "Adam",
+    "params": {
+      "lr": 0.00015,
+      "max_grad_norm": 1.0,
+      "betas": [0.9, 0.95]
+    }
+  },
+  "gradient_clipping": 1.0,
+  "fp16": {
+    "enabled": true,
+
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "wall_clock_breakdown": true,
+  "zero_allow_untested_optimizer": false
+}

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/evaluate_zeroshot_gpt2.sh

+#!/bin/bash
+
+WORLD_SIZE=8
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+TASK="LAMBADA"
+
+VALID_DATA=<lambada path>
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+CHECKPOINT=checkpoints/gpt2_345m
+
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+               --task $TASK \
+               --valid-data $VALID_DATA \
+               --tokenizer-type GPT2BPETokenizer \
+               --strict-lambada \
+               --vocab-file $VOCAB_FILE \
+               --merge-file $MERGE_FILE \
+               --load $CHECKPOINT \
+               --model-parallel-size 1 \
+               --num-layers 24 \
+               --hidden-size 1024 \
+               --num-attention-heads 16 \
+               --batch-size 8 \
+               --checkpoint-activations \
+               --seq-length 1024 \
+               --max-position-embeddings 1024 \
+               --log-interval 10 \
+               --fp16 \
+               --no-load-optim \
+               --no-load-rng

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/finetune_mnli_distributed.sh

+#!/bin/bash
+
+WORLD_SIZE=8
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+TRAIN_DATA="data/glue_data/MNLI/train.tsv"
+VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \
+            data/glue_data/MNLI/dev_mismatched.tsv"
+PRETRAINED_CHECKPOINT=checkpoints/bert_345m
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m_mnli
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+               --task MNLI \
+               --seed 1234 \
+               --train-data $TRAIN_DATA \
+               --valid-data $VALID_DATA \
+               --tokenizer-type BertWordPieceLowerCase \
+               --vocab-file $VOCAB_FILE \
+               --epochs 5 \
+               --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
+               --model-parallel-size 1 \
+               --num-layers 24 \
+               --hidden-size 1024 \
+               --num-attention-heads 16 \
+               --batch-size 8 \
+               --checkpoint-activations \
+               --lr 5.0e-5 \
+               --lr-decay-style linear \
+               --warmup 0.065 \
+               --seq-length 512 \
+               --max-position-embeddings 512 \
+               --save-interval 500000 \
+               --save $CHECKPOINT_PATH \
+               --log-interval 10 \
+               --eval-interval 100 \
+               --eval-iters 50 \
+               --weight-decay 1.0e-1 \
+               --fp16

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/finetune_race_distributed.sh

+#!/bin/bash
+
+WORLD_SIZE=8
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+TRAIN_DATA="data/RACE/train/middle"
+VALID_DATA="data/RACE/dev/middle \
+            data/RACE/dev/high"
+VOCAB_FILE=bert-vocab.txt
+PRETRAINED_CHECKPOINT=checkpoints/bert_345m
+CHECKPOINT_PATH=checkpoints/bert_345m_race
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+               --task RACE \
+               --seed 1234 \
+               --train-data $TRAIN_DATA \
+               --valid-data $VALID_DATA \
+               --tokenizer-type BertWordPieceLowerCase \
+               --vocab-file $VOCAB_FILE \
+               --epochs 3 \
+               --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
+               --model-parallel-size 1 \
+               --num-layers 24 \
+               --hidden-size 1024 \
+               --num-attention-heads 16 \
+               --batch-size 4 \
+               --checkpoint-activations \
+               --lr 1.0e-5 \
+               --lr-decay-style linear \
+               --warmup 0.06 \
+               --seq-length 512 \
+               --max-position-embeddings 512 \
+               --save-interval 100000 \
+               --save $CHECKPOINT_PATH \
+               --log-interval 10 \
+               --eval-interval 100 \
+               --eval-iters 50 \
+               --weight-decay 1.0e-1 \
+               --clip-grad 1.0 \
+               --hidden-dropout 0.1 \
+               --attention-dropout 0.1 \
+               --fp16

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/generate_text.sh

+#!/bin/bash
+
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+
+python tools/generate_samples_gpt2.py \
+       --model-parallel-size 1 \
+       --num-layers 24 \
+       --hidden-size 1024 \
+       --load $CHECKPOINT_PATH \
+       --num-attention-heads 16 \
+       --max-position-embeddings 1024 \
+       --tokenizer-type GPT2BPETokenizer \
+       --fp16 \
+       --batch-size 2 \
+       --seq-length 1024 \
+       --out-seq-length 1024 \
+       --temperature 1.0 \
+       --vocab-file $VOCAB_FILE \
+       --merge-file $MERGE_FILE \
+       --genfile unconditional_samples.json \
+       --num-samples 2 \
+       --top_p 0.9 \
+       --recompute

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/merge_mp_bert.sh

+#!/bin/bash
+
+MODEL_PARALLEL_SIZE=2
+
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m
+
+WORLD_SIZE=$MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
+                                --model-type BERT \
+                                --model-parallel-size $MODEL_PARALLEL_SIZE \
+                                --tokenizer-type BertWordPieceLowerCase \
+                                --vocab-file $VOCAB_FILE \
+                                --num-layers 24 \
+                                --hidden-size 1024 \
+                                --num-attention-heads 16 \
+                                --seq-length 512 \
+                                --max-position-embeddings 512 \
+                                --load $CHECKPOINT_PATH

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/pretrain_bert.sh

+#!/bin/bash
+
+RANK=0
+WORLD_SIZE=1
+DATA_PATH=<Specify path and file prefix>_text_sentence
+CHECKPOINT_PATH=<Specify path>
+
+python pretrain_bert.py \
+       --num-layers 24 \
+       --hidden-size 1024 \
+       --num-attention-heads 16 \
+       --batch-size 4 \
+       --seq-length 512 \
+       --max-position-embeddings 512 \
+       --train-iters 2000000 \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+       --vocab-file bert-vocab.txt \
+       --data-impl mmap \
+       --split 949,50,1 \
+       --distributed-backend nccl \
+       --lr 0.0001 \
+       --min-lr 0.00001 \
+       --lr-decay-style linear \
+       --lr-decay-iters 990000 \
+       --weight-decay 1e-2 \
+       --clip-grad 1.0 \
+       --warmup .01 \
+       --log-interval 100 \
+       --save-interval 10000 \
+       --eval-interval 1000 \
+       --eval-iters 10 \
+       --fp16
+

Deepspeed/Megatron-LM-v1.1.5-3D_parallelism/examples/pretrain_bert_distributed.sh

+#!/bin/bash
+
+GPUS_PER_NODE=8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NNODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
+
+DATA_PATH=<Specify path and file prefix>_text_sentence
+CHECKPOINT_PATH=<Specify path>
+
+DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS \
+       pretrain_bert.py \
+       --model-parallel-size 1 \
+       --num-layers 24 \
+       --hidden-size 1024 \
+       --num-attention-heads 16 \
+       --batch-size 4 \
+       --seq-length 512 \
+       --max-position-embeddings 512 \
+       --train-iters 1000000 \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+       --vocab-file bert-vocab.txt \
+       --data-impl mmap \
+       --split 949,50,1 \
+       --distributed-backend nccl \
+       --lr 0.0001 \
+       --lr-decay-style linear \
+       --min-lr 1.0e-5 \
+       --lr-decay-iters 990000 \
+       --weight-decay 1e-2 \
+       --clip-grad 1.0 \
+       --warmup .01 \
+       --log-interval 100 \
+       --save-interval 10000 \
+       --eval-interval 1000 \
+       --eval-iters 10 \
+       --fp16