from __future__ import annotations
import os
from argparse import Namespace
from functools import partial
from typing import Any, Dict, List, Literal, Optional, Union
import pyarrow
from loguru import logger
from data_juicer import cuda_device_count
from data_juicer.core.data import DJDataset
from data_juicer.core.data.schema import Schema
from data_juicer.ops import Deduplicator, Filter, Mapper
from data_juicer.ops.base_op import TAGGING_OPS
from data_juicer.utils.constant import Fields
from data_juicer.utils.lazy_loader import LazyLoader
from data_juicer.utils.process_utils import calculate_np
ray = LazyLoader('ray')
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def get_abs_path(path, dataset_dir):
full_path = os.path.abspath(os.path.join(dataset_dir, path))
if os.path.exists(full_path):
return full_path
else:
return path
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def convert_to_absolute_paths(samples, dataset_dir, path_keys):
samples = samples.to_pydict()
for key in path_keys:
for idx in range(len(samples[key])):
paths = samples[key][idx]
if isinstance(paths, str):
samples[key][idx] = get_abs_path(paths, dataset_dir)
elif isinstance(paths, list):
samples[key][idx] = [
get_abs_path(item, dataset_dir) for item in paths
]
return pyarrow.Table.from_pydict(samples)
# TODO: check path for nestdataset
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def set_dataset_to_absolute_path(dataset, dataset_path, cfg):
"""
Set all the path in input data to absolute path.
Checks dataset_dir and project_dir for valid paths.
"""
path_keys = []
columns = dataset.columns()
for key in [cfg.video_key, cfg.image_key, cfg.audio_key]:
if key in columns:
path_keys.append(key)
if len(path_keys) > 0:
dataset_dir = os.path.dirname(dataset_path)
logger.error(f'dataset_dir: {dataset_dir}')
dataset = dataset.map_batches(partial(convert_to_absolute_paths,
dataset_dir=dataset_dir,
path_keys=path_keys),
batch_format='pyarrow',
zero_copy_batch=True)
return dataset
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def preprocess_dataset(dataset: ray.data.Dataset, dataset_path,
cfg) -> ray.data.Dataset:
if dataset_path:
dataset = set_dataset_to_absolute_path(dataset, dataset_path, cfg)
return dataset
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def get_num_gpus(op, op_proc):
if not op.use_cuda():
return 0
proc_per_gpu = op_proc / cuda_device_count()
return 1.0 / proc_per_gpu
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def filter_batch(batch, filter_func):
mask = pyarrow.array(filter_func(batch.to_pydict()))
return batch.filter(mask)
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class RayDataset(DJDataset):
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def __init__(self,
dataset: ray.data.Dataset,
dataset_path: str = None,
cfg: Optional[Namespace] = None) -> None:
self.data = preprocess_dataset(dataset, dataset_path, cfg)
self.num_proc = getattr(cfg, 'np', getattr(cfg, 'num_proc',
None)) if cfg else None
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def schema(self) -> Schema:
"""Get dataset schema.
Returns:
Schema: Dataset schema containing column names and types
"""
if self.data is None or self.data.columns() is None:
raise ValueError('Dataset is empty or not initialized')
# Get schema from Ray dataset
_schema = self.data.schema()
# convert schema to proper list and dict
column_types = {
k: Schema.map_ray_type_to_python(v)
for k, v in zip(_schema.names, _schema.types)
}
return Schema(column_types=column_types, columns=column_types.keys())
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def get(self, k: int) -> List[Dict[str, Any]]:
"""Get k rows from the dataset."""
if k < 0:
raise ValueError(f'k must be non-negative, got {k}')
if k == 0:
return []
k = min(k, self.data.count())
return list(self.data.limit(k).take())
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def get_column(self, column: str, k: Optional[int] = None) -> List[Any]:
"""Get column values from Ray dataset.
Args:
column: Name of the column to retrieve
k: Optional number of rows to return. If None, returns all rows
Returns:
List of values from the specified column
Raises:
KeyError: If column doesn't exist
ValueError: If k is negative
"""
if (self.data is None or self.data.columns() is None
or column not in self.data.columns()):
raise KeyError(f"Column '{column}' not found in dataset")
if k is not None:
if k < 0:
raise ValueError(f'k must be non-negative, got {k}')
if k == 0:
return []
k = min(k, self.data.count())
return [row[column] for row in self.data.limit(k).take()]
return [row[column] for row in self.data.take()]
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def process(self,
operators,
*,
exporter=None,
checkpointer=None,
tracer=None) -> DJDataset:
if operators is None:
return self
if not isinstance(operators, list):
operators = [operators]
for op in operators:
self._run_single_op(op)
return self
def _run_single_op(self, op):
op_proc = calculate_np(op._name, op.mem_required, op.cpu_required,
self.num_proc, op.use_cuda())
num_gpus = get_num_gpus(op, op_proc)
if (op._name in TAGGING_OPS.modules
and Fields.meta not in self.data.columns()):
def process_batch_arrow(table: pyarrow.Table):
new_column_data = [{} for _ in range(len(table))]
new_table = table.append_column(Fields.meta, [new_column_data])
return new_table
self.data = self.data.map_batches(process_batch_arrow,
batch_format='pyarrow')
try:
batch_size = getattr(op, 'batch_size',
1) if op.is_batched_op() else 1
if isinstance(op, Mapper):
if op.use_cuda():
op_kwargs = op._op_cfg[op._name]
self.data = self.data.map_batches(
op.__class__,
fn_args=None,
fn_kwargs=None,
fn_constructor_args=None,
fn_constructor_kwargs=op_kwargs,
batch_size=batch_size,
num_gpus=num_gpus,
concurrency=op_proc,
batch_format='pyarrow')
else:
self.data = self.data.map_batches(op.process,
batch_size=batch_size,
batch_format='pyarrow',
num_gpus=num_gpus)
elif isinstance(op, Filter):
columns = self.data.columns()
if Fields.stats not in columns:
def process_batch_arrow(table: pyarrow.Table):
new_column_data = [{} for _ in range(len(table))]
new_talbe = table.append_column(
Fields.stats, [new_column_data])
return new_talbe
self.data = self.data.map_batches(process_batch_arrow,
batch_format='pyarrow')
if op.use_cuda():
op_kwargs = op._op_cfg[op._name]
self.data = self.data.map_batches(
op.__class__,
fn_args=None,
fn_kwargs=None,
fn_constructor_args=None,
fn_constructor_kwargs=op_kwargs,
batch_size=batch_size,
num_gpus=num_gpus,
concurrency=op_proc,
batch_format='pyarrow')
else:
self.data = self.data.map_batches(op.compute_stats,
batch_size=batch_size,
batch_format='pyarrow',
num_gpus=num_gpus)
if op.stats_export_path is not None:
self.data.write_json(op.stats_export_path,
force_ascii=False)
if op.is_batched_op():
self.data = self.data.map_batches(partial(
filter_batch, filter_func=op.process),
batch_format='pyarrow',
batch_size=batch_size,
num_gpus=num_gpus,
zero_copy_batch=True)
else:
self.data = self.data.filter(op.process)
elif isinstance(op, Deduplicator):
self.data = op.run(self.data)
else:
logger.error(
'Ray executor only support Filter and Mapper OPs for now')
raise NotImplementedError
except: # noqa: E722
logger.error(f'An error occurred during Op [{op._name}].')
import traceback
traceback.print_exc()
exit(1)
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@classmethod
def read_json(cls, paths: Union[str, List[str]]) -> RayDataset:
# Note: a temp solution for reading json stream
# TODO: replace with ray.data.read_json_stream once it is available
import pyarrow.json as js
try:
js.open_json
return read_json_stream(paths)
except AttributeError:
return ray.data.read_json(paths)
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class JSONStreamDatasource(ray.data.read_api.JSONDatasource):
"""
A temp Datasource for reading json stream.
Note:
Depends on a customized `pyarrow` with `open_json` method.
"""
def _read_stream(self, f: 'pyarrow.NativeFile', path: str):
from pyarrow.json import open_json
try:
reader = open_json(
f,
read_options=self.read_options,
**self.arrow_json_args,
)
schema = None
while True:
try:
batch = reader.read_next_batch()
table = pyarrow.Table.from_batches([batch], schema=schema)
if schema is None:
schema = table.schema
yield table
except StopIteration:
return
except pyarrow.lib.ArrowInvalid as e:
raise ValueError(f'Failed to read JSON file: {path}.') from e
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def read_json_stream(
paths: Union[str, List[str]],
*,
filesystem: Optional['pyarrow.fs.FileSystem'] = None,
parallelism: int = -1,
ray_remote_args: Dict[str, Any] = None,
arrow_open_stream_args: Optional[Dict[str, Any]] = None,
meta_provider=None,
partition_filter=None,
partitioning=ray.data.read_api.Partitioning('hive'),
include_paths: bool = False,
ignore_missing_paths: bool = False,
shuffle: Union[Literal['files'], None] = None,
file_extensions: Optional[List[str]] = ['json', 'jsonl'],
concurrency: Optional[int] = None,
override_num_blocks: Optional[int] = None,
**arrow_json_args,
) -> ray.data.Dataset:
if meta_provider is None:
meta_provider = ray.data.read_api.DefaultFileMetadataProvider()
datasource = JSONStreamDatasource(
paths,
arrow_json_args=arrow_json_args,
filesystem=filesystem,
open_stream_args=arrow_open_stream_args,
meta_provider=meta_provider,
partition_filter=partition_filter,
partitioning=partitioning,
ignore_missing_paths=ignore_missing_paths,
shuffle=shuffle,
include_paths=include_paths,
file_extensions=file_extensions,
)
return ray.data.read_datasource(
datasource,
parallelism=parallelism,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
override_num_blocks=override_num_blocks,
)