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"""Module containing data utilities"""
import functools
import hashlib
import logging
from pathlib import Path
from typing import Dict, List, Tuple, Union
import torch
from datasets import (
Dataset,
DatasetDict,
concatenate_datasets,
load_dataset,
load_from_disk,
)
from huggingface_hub import hf_hub_download
from transformers import PreTrainedTokenizerBase
from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
from axolotl.datasets import ConstantLengthDataset, TokenizedPromptDataset
from axolotl.prompt_strategies import load
from axolotl.prompt_tokenizers import (
AlpacaMultipleChoicePromptTokenizingStrategy,
AlpacaPromptTokenizingStrategy,
AlpacaReflectionPTStrategy,
GPTeacherPromptTokenizingStrategy,
JeopardyPromptTokenizingStrategy,
OpenAssistantPromptTokenizingStrategy,
SummarizeTLDRPromptTokenizingStrategy,
)
from axolotl.prompters import (
AlpacaPrompter,
GPTeacherPrompter,
JeopardyPrompter,
MultipleChoiceConcisePrompter,
MultipleChoiceExplainPrompter,
Prompter,
ReflectAlpacaPrompter,
SummarizeTLDRPrompter,
UnsupportedPrompter,
)
from axolotl.utils.dict import DictDefault
from axolotl.utils.distributed import is_main_process, zero_first
from axolotl.utils.trainer import (
calculate_total_num_steps,
process_datasets_for_packing,
)
LOG = logging.getLogger("axolotl")
def md5(to_hash: str, encoding: str = "utf-8") -> str:
try:
return hashlib.md5(to_hash.encode(encoding), usedforsecurity=False).hexdigest()
except TypeError:
return hashlib.md5(to_hash.encode(encoding)).hexdigest() # nosec
def prepare_dataset(cfg, tokenizer):
prompters = []
if not cfg.pretraining_dataset:
with zero_first(is_main_process()):
train_dataset, eval_dataset, prompters = load_prepare_datasets(
tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
)
else:
train_dataset = load_pretraining_dataset(
cfg.pretraining_dataset,
tokenizer,
max_tokens=cfg.sequence_len,
seed=cfg.seed or 42,
)
# https://discuss.huggingface.co/t/how-to-use-huggingface-trainer-streaming-datasets-without-wrapping-it-with-torchdatas-iterablewrapper/25230
train_dataset = train_dataset.with_format("torch")
eval_dataset = None
return train_dataset, eval_dataset, cfg.max_steps, prompters
with zero_first(is_main_process()):
train_dataset, eval_dataset = process_datasets_for_packing(
cfg, train_dataset, eval_dataset, tokenizer
)
if eval_dataset and cfg.sample_packing and cfg.eval_sample_packing is not False:
total_eval_steps = calculate_total_num_steps(cfg, eval_dataset, update=False)
if total_eval_steps == 0:
raise ValueError(
"eval dataset split is too small for sample_packing. You should set `eval_sample_packing: False`. "
)
if cfg.max_steps:
total_num_steps = min(
calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
)
LOG.info(f"Maximum number of steps set at {total_num_steps}")
else:
total_num_steps = calculate_total_num_steps(cfg, train_dataset)
return train_dataset, eval_dataset, total_num_steps, prompters
def load_tokenized_prepared_datasets(
tokenizer, cfg, default_dataset_prepared_path
) -> Tuple[DatasetDict, List[Prompter]]:
tokenizer_name = tokenizer.__class__.__name__
ds_hash = str(
md5(
(
str(cfg.sequence_len)
+ "@"
+ "|".join(
sorted(
[
f"{d.path}:{d.type}:{d.shards}:{d.conversation}"
for d in cfg.datasets
]
)
)
+ "|"
+ tokenizer_name
)
)
)
prepared_ds_path = (
Path(cfg.dataset_prepared_path) / ds_hash
if cfg.dataset_prepared_path
else Path(default_dataset_prepared_path) / ds_hash
)
dataset = None
prompters = []
use_auth_token = cfg.hf_use_auth_token
try:
if cfg.push_dataset_to_hub:
dataset = load_dataset(
f"{cfg.push_dataset_to_hub}/{ds_hash}",
token=use_auth_token,
)
dataset = dataset["train"]
except Exception: # pylint: disable=broad-except # nosec
pass
if dataset:
...
elif cfg.dataset_prepared_path and any(prepared_ds_path.glob("*")):
LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
dataset = load_from_disk(str(prepared_ds_path))
LOG.info("Prepared dataset loaded from disk...")
else:
LOG.info(f"Unable to find prepared dataset in {prepared_ds_path}")
LOG.info("Loading raw datasets...")
if cfg.seed:
seed = cfg.seed
else:
LOG.info("No seed provided, using default seed of 42")
seed = 42
datasets = []
def for_d_in_datasets(dataset_configs):
for dataset in dataset_configs:
if dataset.name and isinstance(dataset.name, list):
for name in dataset.name:
yield DictDefault({**dataset, "name": name})
else:
yield dataset
# pylint: disable=invalid-name
for config_dataset in for_d_in_datasets(cfg.datasets):
ds: Union[Dataset, DatasetDict] = None
ds_from_hub = False
try:
load_dataset(
config_dataset.path,
name=config_dataset.name,
streaming=True,
token=use_auth_token,
)
ds_from_hub = True
except (FileNotFoundError, ConnectionError):
pass
ds_from_cloud = False
storage_options = {}
remote_file_system = None
if config_dataset.path.startswith("s3://"):
try:
import aiobotocore.session # type: ignore
import s3fs # type: ignore
except ImportError as exc:
raise ImportError(
"s3:// paths require aiobotocore and s3fs to be installed"
) from exc
# Takes credentials from ~/.aws/credentials for default profile
s3_session = aiobotocore.session.AioSession(profile="default")
storage_options = {"session": s3_session}
remote_file_system = s3fs.S3FileSystem(**storage_options)
elif config_dataset.path.startswith(
"gs://"
) or config_dataset.path.startswith("gcs://"):
try:
import gcsfs # type: ignore
except ImportError as exc:
raise ImportError(
"gs:// or gcs:// paths require gcsfs to be installed"
) from exc
# gcsfs will use default credentials from the environment else anon
# https://gcsfs.readthedocs.io/en/latest/#credentials
storage_options = {"token": None}
remote_file_system = gcsfs.GCSFileSystem(**storage_options)
# TODO: Figure out how to get auth creds passed
# elif config_dataset.path.startswith("adl://") or config_dataset.path.startswith("abfs://"):
# try:
# import adlfs
# except ImportError as exc:
# raise ImportError(
# "adl:// or abfs:// paths require adlfs to be installed"
# ) from exc
# # Gen 1
# storage_options = {
# "tenant_id": TENANT_ID,
# "client_id": CLIENT_ID,
# "client_secret": CLIENT_SECRET,
# }
# # Gen 2
# storage_options = {
# "account_name": ACCOUNT_NAME,
# "account_key": ACCOUNT_KEY,
# }
# remote_file_system = adlfs.AzureBlobFileSystem(**storage_options)
try:
if remote_file_system and remote_file_system.exists(
config_dataset.path
):
ds_from_cloud = True
except (FileNotFoundError, ConnectionError):
pass
# prefer local dataset, even if hub exists
local_path = Path(config_dataset.path)
if local_path.exists():
if local_path.is_dir():
# TODO dirs with arrow or parquet files could be loaded with `load_from_disk`
ds = load_dataset(
config_dataset.path,
name=config_dataset.name,
data_files=config_dataset.data_files,
streaming=False,
split=None,
)
elif local_path.is_file():
ds_type = get_ds_type(config_dataset)
ds = load_dataset(
ds_type,
name=config_dataset.name,
data_files=config_dataset.path,
streaming=False,
split=None,
)
else:
raise ValueError(
"unhandled dataset load: local path exists, but is neither a directory or a file"
)
elif ds_from_hub:
ds = load_dataset(
config_dataset.path,
name=config_dataset.name,
streaming=False,
data_files=config_dataset.data_files,
token=use_auth_token,
)
elif ds_from_cloud and remote_file_system:
if remote_file_system.isdir(config_dataset.path):
ds = load_from_disk(
config_dataset.path,
storage_options=storage_options,
)
elif remote_file_system.isfile(config_dataset.path):
ds_type = get_ds_type(config_dataset)
ds = load_dataset(
ds_type,
name=config_dataset.name,
data_files=config_dataset.path,
streaming=False,
split=None,
storage_options=storage_options,
)
else:
if isinstance(config_dataset.data_files, str):
fp = hf_hub_download(
repo_id=config_dataset.path,
repo_type="dataset",
filename=config_dataset.data_files,
)
elif isinstance(config_dataset.data_files, list):
fp = []
for file in config_dataset.data_files:
fp.append(
hf_hub_download(
repo_id=config_dataset.path,
repo_type="dataset",
filename=file,
)
)
else:
raise ValueError(
"data_files must be either a string or list of strings"
)
ds = load_dataset(
"json",
name=config_dataset.name,
data_files=fp,
streaming=False,
split=None,
)
if not ds:
raise ValueError("unhandled dataset load")
# support for using a subset of the data
if config_dataset.shards:
if "train" in ds:
ds = ds.shuffle(seed=seed)["train"].shard(
num_shards=config_dataset.shards, index=0
)
else:
ds = ds.shuffle(seed=seed).shard(
num_shards=config_dataset.shards, index=0
)
d_base_type = d_prompt_style = None
d_type = config_dataset.type
if isinstance(d_type, str):
d_type_split = d_type.split(":")
d_base_type = d_type_split[0]
d_prompt_style = d_type_split[1] if len(d_type_split) > 1 else None
if "train" in ds:
ds = ds["train"]
elif (
isinstance(ds, DatasetDict)
and config_dataset.train_on_split
and config_dataset.train_on_split in ds
):
ds = ds[config_dataset.train_on_split]
elif isinstance(ds, DatasetDict):
raise ValueError(
f"no train split found for dataset {config_dataset.path}, you may specify a split with 'train_on_split: `"
)
dataset_wrapper, dataset_prompter = get_dataset_wrapper(
config_dataset=config_dataset,
dataset=ds,
tokenizer=tokenizer,
cfg=cfg,
d_base_type=d_base_type,
d_prompt_style=d_prompt_style,
)
datasets.append(dataset_wrapper)
prompters.append(dataset_prompter)
LOG.info("merging datasets")
dataset = concatenate_datasets(datasets)
if len(datasets) > 1:
LOG.info("shuffle merged datasets")
dataset = dataset.shuffle(seed=seed)
if cfg.local_rank == 0:
LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
dataset.save_to_disk(prepared_ds_path)
if cfg.push_dataset_to_hub:
LOG.info(
f"Saving merged prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
)
dataset.push_to_hub(
f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
)
return dataset, prompters
def get_ds_type(config_dataset: DictDefault):
"""
Get the dataset type from the path if it's not specified
"""
ds_type = "json"
if config_dataset.ds_type:
ds_type = config_dataset.ds_type
elif ".parquet" in config_dataset.path:
ds_type = "parquet"
elif ".arrow" in config_dataset.path:
ds_type = "arrow"
elif ".csv" in config_dataset.path:
ds_type = "csv"
elif ".txt" in config_dataset.path:
ds_type = "text"
return ds_type
def load_prepare_datasets(
tokenizer: PreTrainedTokenizerBase,
cfg,
default_dataset_prepared_path,
) -> Tuple[Dataset, Dataset, List[Prompter]]:
max_packed_sequence_len = (
cfg.max_packed_sequence_len if cfg.max_packed_sequence_len else cfg.sequence_len
)
max_packed_sequence_len = min(
max_packed_sequence_len, cfg.sequence_len
) # make sure we don't accidentally set it larger than sequence_len
tokenizer_name = tokenizer.__class__.__name__
prompters: List[Prompter] = []
if cfg.max_packed_sequence_len is not None:
# see if we can go ahead and load the stacked dataset
seed = f"@{str(cfg.seed)}" if cfg.seed else ""
ds_hash = str(
md5(
(
str(cfg.sequence_len)
+ "@"
+ str(max_packed_sequence_len)
+ seed
+ "|".join(
sorted([f"{d.path}:{d.type}:{d.shards}" for d in cfg.datasets])
)
+ "|"
+ tokenizer_name
)
)
)
prepared_ds_path = (
Path(cfg.dataset_prepared_path) / ds_hash
if cfg.dataset_prepared_path
else Path(default_dataset_prepared_path) / ds_hash
)
dataset = None
use_auth_token = cfg.hf_use_auth_token
try:
if cfg.push_dataset_to_hub:
LOG.info(
f"Checking for packed prepared dataset from hub... {cfg.push_dataset_to_hub}/{ds_hash}"
)
dataset = load_dataset(
f"{cfg.push_dataset_to_hub}/{ds_hash}",
token=use_auth_token,
)
dataset = dataset["train"]
except Exception: # pylint: disable=broad-except # nosec
pass
if dataset:
...
elif cfg.dataset_prepared_path and any(prepared_ds_path.glob("*")):
LOG.info(
f"Loading prepared packed dataset from disk at {prepared_ds_path}..."
)
dataset = load_from_disk(str(prepared_ds_path))
LOG.info("Prepared packed dataset loaded from disk...")
if cfg.push_dataset_to_hub:
LOG.info(
f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
)
dataset.push_to_hub(
f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
)
else:
dataset, prompters = load_tokenized_prepared_datasets(
tokenizer, cfg, default_dataset_prepared_path
)
if cfg.seed:
dataset = dataset.shuffle(seed=cfg.seed)
constant_len_dataset = ConstantLengthDataset(
tokenizer,
[dataset],
seq_length=max_packed_sequence_len,
)
LOG.info(f"packing master dataset to len: {cfg.max_packed_sequence_len}")
dataset = Dataset.from_list(list(constant_len_dataset))
# filter out bad data
# TODO convert to dataset.filter(...)
dataset = Dataset.from_list(
[
d
for d in dataset
if len(d["input_ids"]) <= cfg.sequence_len
and len(d["input_ids"]) > 0
and len(d["input_ids"]) == len(d["attention_mask"])
and len(d["input_ids"]) == len(d["labels"])
]
)
if cfg.local_rank == 0:
LOG.info(
f"Saving packed prepared dataset to disk... {prepared_ds_path}"
)
dataset.save_to_disk(prepared_ds_path)
if cfg.push_dataset_to_hub:
LOG.info(
f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
)
dataset.push_to_hub(
f"{cfg.push_dataset_to_hub}/{ds_hash}",
private=True,
)
else:
dataset, prompters = load_tokenized_prepared_datasets(
tokenizer, cfg, default_dataset_prepared_path
)
if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None:
LOG.info(
f"Using index #{cfg.dataset_shard_idx} of {cfg.dataset_shard_num} shards"
)
dataset = dataset.shard(
num_shards=cfg.dataset_shard_num,
index=cfg.dataset_shard_idx,
)
if cfg.val_set_size:
# ensure we end up with the same fingerprint by doing rank0 first and being able to cache
to_hash_train = (
dataset._fingerprint # pylint: disable=protected-access
+ "|"
+ str(cfg.val_set_size)
+ "|"
+ "train"
+ "|"
+ str(cfg.seed or 42)
)
to_hash_test = (
dataset._fingerprint # pylint: disable=protected-access
+ "|"
+ str(cfg.val_set_size)
+ "|"
+ "test"
+ "|"
+ str(cfg.seed or 42)
)
train_fingerprint = md5(to_hash_train)
test_fingerprint = md5(to_hash_test)
dataset = dataset.train_test_split(
test_size=cfg.val_set_size,
shuffle=False,
seed=cfg.seed or 42,
train_new_fingerprint=train_fingerprint,
test_new_fingerprint=test_fingerprint,
)
train_dataset = dataset["train"]
eval_dataset = dataset["test"]
else:
train_dataset = dataset
eval_dataset = None
return train_dataset, eval_dataset, prompters
def get_dataset_wrapper(
config_dataset, dataset, tokenizer, cfg, d_base_type, d_prompt_style
):
dataset_wrapper = None
dataset_prompter = None
if (
"input_ids" in dataset.features
and "attention_mask" in dataset.features
and "labels" in dataset.features
):
# dataset is already tokenized, just drop it straight in
dataset_prompter = UnsupportedPrompter()
dataset_wrapper = dataset
elif isinstance(config_dataset.type, DictDefault):
ds_strategy = load(
"user_defined", tokenizer, cfg, config_dataset.type.to_dict()
)
dataset_prompter = UnsupportedPrompter()
dataset_wrapper = TokenizedPromptDataset(
ds_strategy, dataset, process_count=cfg.dataset_processes
)
elif ds_strategy := load(config_dataset.type, tokenizer, cfg, config_dataset):
dataset_prompter = UnsupportedPrompter()
dataset_wrapper = TokenizedPromptDataset(
ds_strategy, dataset, process_count=cfg.dataset_processes
)
elif d_base_type == "alpaca":
dataset_prompter = AlpacaPrompter(d_prompt_style)
ds_strategy = AlpacaPromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy, dataset, process_count=cfg.dataset_processes
)
dataset_wrapper = ds_wrapper
elif d_base_type == "explainchoice":
dataset_prompter = MultipleChoiceExplainPrompter(d_prompt_style)
ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy, dataset, process_count=cfg.dataset_processes
)
dataset_wrapper = ds_wrapper
elif d_base_type == "concisechoice":
dataset_prompter = MultipleChoiceConcisePrompter(d_prompt_style)
ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy, dataset, process_count=cfg.dataset_processes
)
dataset_wrapper = ds_wrapper
elif d_base_type == "summarizetldr":
dataset_prompter = SummarizeTLDRPrompter(d_prompt_style)
ds_strategy = SummarizeTLDRPromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy, dataset, process_count=cfg.dataset_processes
)
dataset_wrapper = ds_wrapper
elif d_base_type == "jeopardy":
dataset_prompter = JeopardyPrompter(d_prompt_style)
ds_strategy = JeopardyPromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy, dataset, process_count=cfg.dataset_processes
)
dataset_wrapper = ds_wrapper
elif d_base_type == "oasst":
dataset_prompter = AlpacaPrompter(d_prompt_style)
ds_strategy = OpenAssistantPromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy, dataset, process_count=cfg.dataset_processes
)
dataset_wrapper = ds_wrapper
elif d_base_type == "gpteacher":
dataset_prompter = GPTeacherPrompter(d_prompt_style)
ds_strategy = GPTeacherPromptTokenizingStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy, dataset, process_count=cfg.dataset_processes
)
dataset_wrapper = ds_wrapper
elif d_base_type == "reflection":
dataset_prompter = ReflectAlpacaPrompter(d_prompt_style)
ds_strategy = AlpacaReflectionPTStrategy(
dataset_prompter,
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(
ds_strategy, dataset, process_count=cfg.dataset_processes
)
dataset_wrapper = ds_wrapper
else:
suffix = ""
if ":load_" in config_dataset.type:
suffix = f" Did you mean {config_dataset.type.replace(':load_', '.load_')}?"
LOG.error(
f"unhandled prompt tokenization strategy: {config_dataset.type}. {suffix}"
)
raise ValueError(
f"unhandled prompt tokenization strategy: {config_dataset.type} {suffix}"
)
return dataset_wrapper, dataset_prompter
def encode_pretraining(
tokenizer: PreTrainedTokenizerBase, max_tokens: int, examples: List[str]
) -> Dict[str, List]:
res = tokenizer(
examples,
truncation=True,
max_length=max_tokens - 2,
add_special_tokens=True,
)
# Convert to PyTorch tensors
input_ids = [torch.tensor(seq) for seq in res["input_ids"]]
attention_mask = [torch.tensor(seq) for seq in res["attention_mask"]]
new_input_ids = []
new_attention_mask = []
# Append EOS and PAD tokens to input_ids, and correct attention_mask
for i, _ in enumerate(input_ids):
input_ids[i] = torch.cat(
(
input_ids[i],
torch.tensor([tokenizer.eos_token_id, tokenizer.pad_token_id]),
),
dim=0,
)
attention_mask[i] = torch.cat((attention_mask[i], torch.tensor([1, 0])), dim=0)
# Concatenate tokens so that their lengths are less than max_tokens
buffer_input_ids = torch.tensor([], dtype=torch.long)
buffer_attention_mask = torch.tensor([], dtype=torch.long)
for ids, mask in zip(input_ids, attention_mask):
if buffer_input_ids.numel() == max_tokens:
new_input_ids.append(buffer_input_ids)
new_attention_mask.append(buffer_attention_mask)
buffer_input_ids = torch.tensor([], dtype=torch.long)
buffer_attention_mask = torch.tensor([], dtype=torch.long)
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
elif buffer_input_ids.numel() + ids.numel() <= max_tokens:
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
else:
buffer_input_ids = torch.cat(
(
buffer_input_ids,
torch.full(
(max_tokens - buffer_input_ids.numel(),),
tokenizer.pad_token_id,
dtype=torch.long,
),
),
dim=0,
)
buffer_attention_mask = torch.cat(
(
buffer_attention_mask,
torch.full(
(max_tokens - buffer_attention_mask.numel(),),
0,
dtype=torch.long,
),
),
dim=0,
)
new_input_ids.append(buffer_input_ids)
new_attention_mask.append(buffer_attention_mask)
buffer_input_ids = torch.tensor([], dtype=torch.long)
buffer_attention_mask = torch.tensor([], dtype=torch.long)
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
if buffer_input_ids.numel() > 0: # for any leftover tokens
while buffer_input_ids.numel() < max_tokens: # make all sequences equal in size
buffer_input_ids = torch.cat(
(
buffer_input_ids,
torch.full(
(max_tokens - buffer_input_ids.numel(),),
tokenizer.pad_token_id,
dtype=torch.long,
),
),
dim=0,
)
buffer_attention_mask = torch.cat(
(
buffer_attention_mask,
torch.full(
(max_tokens - buffer_attention_mask.numel(),),
0,
dtype=torch.long,
),
),
dim=0,
)
new_input_ids.append(buffer_input_ids)
new_attention_mask.append(buffer_attention_mask)
ret = {
"input_ids": [seq.tolist() for seq in new_input_ids],
"labels": [seq.tolist() for seq in new_input_ids],
"attention_mask": [seq.tolist() for seq in new_attention_mask],
}
LOG.debug(len(ret["input_ids"]))
return ret
def load_pretraining_dataset(path, tokenizer, max_tokens=2048, seed=42):
encode = functools.partial(encode_pretraining, tokenizer, max_tokens)
dataset = load_dataset(path, streaming=True, split="train")
dataset = dataset.shuffle(seed=seed, buffer_size=10_000)
dataset = dataset.map(
encode,
batched=True,
input_columns="text",
# remove all the existing columns after mapping since they end up having
# a different length than the encoded/tokenized column
remove_columns=dataset.features.keys(),
)
return dataset
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