Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Feat: Add dataset loading from S3, GCS #765

Merged
merged 3 commits into from
Nov 16, 2023
Merged

Feat: Add dataset loading from S3, GCS #765

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
cf01825
Feat: Add dataset loading from S3, GCS
NanoCode012 Oct 22, 2023
2083805
chore: update docs
NanoCode012 Nov 15, 2023
51a530c
chore: add more info on cloud loading
NanoCode012 Nov 16, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 7 additions & 1 deletion README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -426,6 +426,12 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
- path: knowrohit07/know_sql
type: context_qa.load_v2
train_on_split: validation

# loading from s3 or gcs
# s3 creds will be loaded from the system default and gcs only supports public access
dataset:
- path: s3://path_to_ds # Accepts folder with arrow/parquet or file path like above. Supports s3, gcs.
...
```

- loading
Expand Down Expand Up @@ -520,7 +526,7 @@ float16: true

# A list of one or more datasets to finetune the model with
datasets:
# HuggingFace dataset repo | "json" for local dataset, make sure to fill data_files
# HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
- path: vicgalle/alpaca-gpt4
# The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
Expand Down
7 changes: 6 additions & 1 deletion requirements.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@ deepspeed
addict
fire
PyYAML>=6.0
datasets
datasets>=2.14.0
flash-attn>=2.3.0
sentencepiece
wandb
Expand All @@ -33,3 +33,8 @@ art
fschat==0.2.29
gradio
tensorboard

# remote filesystems
s3fs
gcsfs
# adlfs
116 changes: 97 additions & 19 deletions src/axolotl/utils/data.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -170,30 +170,74 @@ def for_d_in_datasets(dataset_configs):
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,
)
ds = load_from_disk(config_dataset.path)
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I don't believe this is equivalent, since the previous load_dataset call also has a data_files component to only load specific files from the path.

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This indeed broke my workflow: I have a directory with txt files inside, the load_dataset knew how to handle that, load_from_disk raises an exception

Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@gordicaleksa , could you please open an Issue along with a sample config? It'll make it easier to remember this.

elif local_path.is_file():
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"
ds_type = get_ds_type(config_dataset)

ds = load_dataset(
ds_type,
name=config_dataset.name,
Expand All @@ -213,6 +257,22 @@ def for_d_in_datasets(dataset_configs):
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(
Expand Down Expand Up @@ -304,6 +364,24 @@ def for_d_in_datasets(dataset_configs):
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,
Expand Down