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# Copyright (c) Meta Platforms, Inc. and affiliates.
import os
import re
from logging import getLogger
from typing import Any, Generator
import fsspec
import pyarrow as pa
# pyarrow needs the initialization from this import
import pyarrow.dataset # pyright: ignore
import s3fs
from pydantic import BaseModel, ConfigDict
from bytelatent import ByteLatentError
from bytelatent.data.data_types import BltExample
from bytelatent.data.file_util import get_fs
from bytelatent.data.iterators.abstract_iterator import (
PydanticIteratorState,
StatefulIterator,
)
from bytelatent.preprocess.preprocess_entropies import get_id_key, get_text
logger = getLogger(__name__)
class ArrowFileIteratorState(PydanticIteratorState):
model_config = ConfigDict(extra="forbid")
file_path: str | None
row_num: int
num_workers: int
worker_id: int
preprocess_dir: str | None
dataset_files: list[str] | None
entropy_model_name: str | None
arrow_batch_size: int = 100
s3_profile: str | None
filesystem_type: str | None = None
file_format: str
def build(self) -> "ArrowFileIterator":
arrow_file = ArrowFileIterator(
file_path=self.file_path,
worker_id=self.worker_id,
num_workers=self.num_workers,
preprocess_dir=self.preprocess_dir,
entropy_model_name=self.entropy_model_name,
arrow_batch_size=self.arrow_batch_size,
dataset_files=self.dataset_files,
s3_profile=self.s3_profile,
filesystem_type=self.filesystem_type,
file_format=self.file_format,
)
if self.row_num != 0:
arrow_file._set_row_num(self.row_num)
return arrow_file
def shard_sort_key(file: str):
assert isinstance(file, str)
match = re.search(r".+\.shard_([0-9]+)\.arrow", file)
shard_number = int(match.group(1))
return shard_number
def maybe_truncate_string(text: str, max_length: int):
if len(text) <= max_length:
return text
else:
return text[:max_length] + "..."
class ArrowFileIterator(StatefulIterator):
def __init__(
self,
*,
file_path: str | None,
worker_id: int,
num_workers: int,
preprocess_dir: str | None,
entropy_model_name: str | None,
arrow_batch_size: int,
dataset_files: list[str] | None = None,
s3_profile: str | None = None,
filesystem_type: str | None = None,
file_format: str = "arrow",
):
assert 0 <= worker_id < num_workers, (worker_id, num_workers)
if file_path is None and dataset_files is None:
raise ByteLatentError("file_path and dataset_files cannot both be None")
self.row_num = 0
self.iter_id = 0
self.batch_iterator = None
self.batch_to_consume = None
self.dataset = None
self.file_path = file_path
self.worker_id = worker_id
self.num_workers = num_workers
self.preprocess_dir = preprocess_dir
self.entropy_model_name = entropy_model_name
self.arrow_batch_size = arrow_batch_size
self.s3_profile = s3_profile
self.filesystem_type = filesystem_type
self.file_format = file_format
self.fs = None
if self.filesystem_type is not None:
if self.filesystem_type == "file":
self.fs = fsspec.filesystem("file")
elif self.filesystem_type == "s3":
self.fs = fsspec.filesystem("s3", profile=s3_profile)
else:
raise ValueError("Unknown filesystem")
logger.info("Arrow iterator using fs=%s", self.fs)
if dataset_files is None:
assert (
file_path is not None
), "Must specify file_Path if dataset_files is None"
if file_format == "json":
if self.fs is None:
self.fs = get_fs(file_path, s3_profile=s3_profile)
if isinstance(self.fs, s3fs.S3FileSystem):
self.filesystem_type = "s3"
else:
self.filesystem_type = "file"
self.dataset_files = [file_path]
else:
# Prepare arrow shards
jsonl_file = file_path
parts = re.match(
r"(.+)\.chunk\.[0-9]+\.jsonl", os.path.basename(jsonl_file)
)
assert parts is not None
dataset = parts.group(1)
data_dir = os.path.join(preprocess_dir, dataset, entropy_model_name)
data_dir_with_glob = os.path.join(
data_dir, f"{os.path.basename(jsonl_file)}.shard_*.arrow"
)
if self.fs is None:
self.fs = get_fs(data_dir_with_glob, s3_profile=s3_profile)
if isinstance(self.fs, s3fs.S3FileSystem):
self.filesystem_type = "s3"
else:
self.filesystem_type = "file"
shard_files = self.fs.glob(data_dir_with_glob)
for s in shard_files:
complete_file = os.path.join(
data_dir, f"{os.path.basename(s)}.complete"
)
if not self.fs.exists(complete_file):
raise ValueError(f"Missing .complete for input file: {s}")
shard_files = sorted(shard_files, key=shard_sort_key)
if len(shard_files) == 0:
raise ByteLatentError(
f"Zero shard_files found corresponding to: {file_path} using preprocess_dir={preprocess_dir} and entropy_model_name={entropy_model_name}, so the search path is data_dir={data_dir} for matches to {jsonl_file.name}.shard_*.arrow"
)
self.dataset_files = [f for f in shard_files]
else:
self.preprocess_dir = None
self.dataset_files = dataset_files
if dataset_files[0].startswith("s3://"):
for f in dataset_files:
assert f.startswith("s3://")
if self.fs is None:
self.fs = get_fs(dataset_files[0], s3_profile=s3_profile)
if isinstance(self.fs, s3fs.S3FileSystem):
self.filesystem_type = "s3"
else:
self.filesystem_type = "file"
def get_state(self) -> ArrowFileIteratorState:
return ArrowFileIteratorState(
file_path=self.file_path,
row_num=self.row_num,
worker_id=self.worker_id,
num_workers=self.num_workers,
preprocess_dir=self.preprocess_dir,
entropy_model_name=self.entropy_model_name,
arrow_batch_size=self.arrow_batch_size,
dataset_files=self.dataset_files,
s3_profile=self.s3_profile,
filesystem_type=self.filesystem_type,
file_format=self.file_format,
)
def create_iter(
self,
) -> Generator[BltExample, Any, None]:
if self.dataset is None:
if isinstance(self.fs, s3fs.core.S3FileSystem):
filesystem = self.fs
else:
filesystem = None
self.dataset = pa.dataset.dataset(
self.dataset_files, format=self.file_format, filesystem=filesystem
)
self.iter_id += 1
if self.batch_to_consume is not None:
batch_columns: dict[str, list] = self.batch_to_consume
self.batch_to_consume = None
if self.file_format == "arrow":
sample_ids = batch_columns["sample_id"]
texts = batch_columns["text"]
entropies = batch_columns["entropies"]
elif self.file_format == "json":
# This data hasn't been preprocessed to a uniform format,
# so we have to do it now and omit entropies
sample_ids = batch_columns[get_id_key(batch_columns)]
texts = get_text(batch_columns)
entropies = None
else:
raise ValueError(f"Unknown file format: {self.file_format}")
for i in range(len(sample_ids)):
out = BltExample(
sample_id=str(sample_ids[i]),
entropies=entropies[i] if entropies is not None else None,
text=texts[i],
tokens=None,
mask=None,
patch_lengths=None,
)
self.row_num += 1
if (self.row_num - 1) % self.num_workers == self.worker_id:
yield out
self.batch_iterator = self.dataset.to_batches(
batch_size=self.arrow_batch_size,
# We have large files in GBs, no need to readahead
fragment_readahead=1,
# Don't readahead in case batches are huge (e.g., books)
batch_readahead=1,
)
for batch in self.batch_iterator:
batch_columns = batch.to_pydict()
if self.file_format == "arrow":
sample_ids = batch_columns["sample_id"]
texts = batch_columns["text"]
entropies = batch_columns["entropies"]
elif self.file_format == "json":
# This data hasn't been preprocessed to a uniform format,
# so we have to do it now and omit entropies
sample_ids = batch_columns[get_id_key(batch_columns)]
texts = get_text(batch_columns)
entropies = None
else:
raise ValueError(f"Unknown file format: {self.file_format}")
for i in range(len(sample_ids)):
out = BltExample(
sample_id=str(sample_ids[i]),
entropies=entropies[i] if entropies is not None else None,
text=texts[i],
tokens=None,
mask=None,
patch_lengths=None,
)
self.row_num += 1
if (self.row_num - 1) % self.num_workers == self.worker_id:
yield out
def _set_row_num(self, target_row_num: int):
data_str = maybe_truncate_string(str(self.dataset_files), 200)
logger.info(f"Setting arrow position to {target_row_num} for {data_str}")
if target_row_num is None or target_row_num == 0:
self.row_num = 0
self.dataset = None
self.batch_iterator = None
self.batch_to_consume = None
else:
if isinstance(self.fs, s3fs.core.S3FileSystem):
filesystem = self.fs
else:
filesystem = None
self.dataset = pa.dataset.dataset(
self.dataset_files, format="arrow", filesystem=filesystem
)
self.batch_iterator = self.dataset.to_batches(
batch_size=self.arrow_batch_size
)
curr_remaining = target_row_num
for batch in self.batch_iterator:
if len(batch) > curr_remaining:
batch_columns: dict[str, list] = batch.to_pydict()
if self.file_format == "arrow":
leftover_sample_ids = batch_columns["sample_id"][
curr_remaining:
]
leftover_entropies = batch_columns["entropies"][curr_remaining:]
leftover_texts = batch_columns["text"][curr_remaining:]
elif self.file_format == "json":
leftover_sample_ids = batch_columns[get_id_key(batch_columns)][
curr_remaining:
]
leftover_entropies = None
leftover_texts = get_text(batch_columns)[curr_remaining:]
else:
raise ValueError(f"Unknown file format: {self.file_format}")
batch_columns["sample_id"] = leftover_sample_ids
batch_columns["entropies"] = leftover_entropies
batch_columns["text"] = leftover_texts
self.batch_to_consume = batch_columns
break
elif len(batch) == curr_remaining:
# We are exactly at the end of the batch,
# so the next batch is the right spot
break
else:
curr_remaining -= len(batch)
self.row_num = target_row_num
data_str = maybe_truncate_string(str(self.dataset_files), 200)
logger.info(
f"Finished setting arrow position to {target_row_num} for {data_str}"
)
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