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from __future__ import annotations
import hashlib
import os
import time
from collections import defaultdict
from pathlib import Path
from typing import Iterable
import datasets
import pyarrow as pa
import pyarrow.compute as pc
import pyarrow.parquet as pq
from datasets import Dataset
from datasets.arrow_writer import ArrowWriter
from colpali_engine.data.dataset import ColPaliEngineDataset
from paths import forbid_venv_path
ALLOWED_SOURCES = (
"colpali_train_set",
"VisRAG-Ret-Train-Synthetic-data",
"VisRAG-Ret-Train-In-domain-data",
"vdr-multilingual-train",
"tatdqa_train",
"tabfquad_train_set",
)
_STREAM_BATCH_ROWS = 256
def _set_task_ids_from_sources(
dataset: ColPaliEngineDataset,
sources: pa.Array | pa.ChunkedArray,
) -> None:
if isinstance(sources, pa.ChunkedArray):
sources = sources.combine_chunks()
encoded = pc.dictionary_encode(sources)
dataset.task_ids = encoded.indices.to_numpy(zero_copy_only=False)
dataset.task_names = encoded.dictionary.to_pylist()
counts = pc.value_counts(encoded.indices).to_pylist()
summary = {
dataset.task_names[int(item["values"])]: int(item["counts"])
for item in counts
}
print(f"[data] task-consistent groups: {summary}")
def _attach_task_ids(
dataset: ColPaliEngineDataset,
queries_ds: Dataset,
corpus_ds: Dataset,
docid_to_idx: dict[int, int] | None,
) -> None:
if "source" not in corpus_ds.column_names:
dataset.task_ids = [0] * len(dataset)
dataset.task_names = ["all"]
return
positive_column = queries_ds.data.column("pos_target")
if docid_to_idx is None:
row_indices = positive_column
else:
row_indices = pa.array(
[docid_to_idx[int(doc_id)] for doc_id in positive_column.to_pylist()],
type=pa.int64(),
)
sources = pc.take(corpus_ds.data.column("source"), row_indices)
_set_task_ids_from_sources(dataset, sources)
def _default_dataset_cache_dir() -> Path:
override = os.environ.get("HF_DATASETS_CACHE") or os.environ.get("EVIE_DATASETS_CACHE")
if override:
return Path(override)
return Path.home() / ".cache" / "huggingface" / "datasets"
def _export_parts(data_root: Path) -> list[str]:
parts = sorted(Path(data_root).glob("part_*.parquet"))
if not parts:
raise FileNotFoundError(f"No part_*.parquet under {data_root}")
return [str(p) for p in parts]
def _cache_dir() -> Path:
d = Path(os.environ.get("HF_DATASETS_CACHE") or _default_dataset_cache_dir())
forbid_venv_path(d, "HF_DATASETS_CACHE")
d.mkdir(parents=True, exist_ok=True)
return d
def _fingerprint(parts: list[str]) -> str:
h = hashlib.sha1()
for p in parts:
st = Path(p).stat()
h.update(Path(p).name.encode())
h.update(str(st.st_size).encode())
h.update(str(int(st.st_mtime)).encode())
return h.hexdigest()[:16]
def _prepare_streamed_arrow(parts: list[str]) -> Path:
"""Rank 0 writes a shared Arrow cache; other ranks memory-map it.
Avoid Dataset.filter here: concurrent DDP ranks would race on the same
cache-*.arrow files.
"""
final = _cache_dir() / f"export_stream_{_fingerprint(parts)}.arrow"
done = Path(str(final) + ".done")
error = Path(str(final) + ".error")
rank = int(os.environ.get("RANK", "0"))
if done.exists() and final.exists():
return final
if rank == 0:
tmp = Path(str(final) + f".{os.getpid()}.tmp")
error.unlink(missing_ok=True)
try:
writer = ArrowWriter(path=str(tmp))
total = 0
t0 = time.time()
for i, p in enumerate(parts):
for rb in pq.ParquetFile(p).iter_batches(batch_size=_STREAM_BATCH_ROWS):
writer.write_table(pa.Table.from_batches([rb]))
total += rb.num_rows
print(f"[data] shard {i + 1}/{len(parts)}: {total:,} rows, {time.time() - t0:.0f}s", flush=True)
writer.finalize()
os.replace(tmp, final)
done.touch()
print(f"[data] arrow cache {final} ({total:,} rows)", flush=True)
return final
except Exception as exc:
tmp.unlink(missing_ok=True)
error.write_text(repr(exc), encoding="utf-8")
raise
waited = 0
timeout = int(os.environ.get("EVIE_DATA_CACHE_TIMEOUT", "7200"))
while not (done.exists() and final.exists()):
if error.exists():
raise RuntimeError(f"rank0 failed to build Arrow cache: {error.read_text()}")
time.sleep(5)
waited += 5
if waited >= timeout:
raise TimeoutError(f"timed out waiting for Arrow cache after {timeout}s")
return final
def build_train_dataset(
data_root: str | Path,
sources: Iterable[str] = (),
max_samples_per_source: int = 0,
) -> ColPaliEngineDataset:
root = Path(data_root)
parts = _export_parts(root)
arrow = _prepare_streamed_arrow(parts)
ds = Dataset.from_file(str(arrow))
selected = tuple(dict.fromkeys(sources))
if selected and set(selected) != set(ALLOWED_SOURCES):
keep = set(selected)
ds = ds.filter(lambda s: s in keep, input_columns="source")
if max_samples_per_source:
counts = defaultdict(int)
indices = []
for i, source in enumerate(ds["source"]):
if counts[source] < max_samples_per_source:
indices.append(i)
counts[source] += 1
ds = ds.select(indices)
ds = ds.cast_column("image", datasets.Image())
tag = ",".join(selected) if selected else "all"
print(f"[data] {len(ds):,} pairs from {root.name} ({tag})")
wrapped = ColPaliEngineDataset(ds, pos_target_column_name="image")
if "source" in ds.column_names:
_set_task_ids_from_sources(wrapped, ds.data.column("source"))
return wrapped
def build_hardneg_dataset(
hardneg_root: str | Path,
data_root: str | Path,
num_negatives: int = 2,
max_samples: int = 0,
use_negatives: bool = True,
queries_subdir: str = "",
) -> ColPaliEngineDataset:
from datasets import load_from_disk
from colpali_engine.data.dataset import Corpus
root = Path(hardneg_root)
subdir = queries_subdir or os.environ.get("HARDNEG_SUBDIR", "judged")
queries_ds = load_from_disk(str(root / subdir))
print(f"[data] hardneg subdir: {subdir}")
corpus_path = root / "corpus"
if corpus_path.is_dir():
corpus_ds = load_from_disk(str(corpus_path))
corpus_origin = str(corpus_path)
else:
parts = _export_parts(Path(data_root))
arrow = _prepare_streamed_arrow(parts)
corpus_ds = Dataset.from_file(str(arrow)).cast_column("image", datasets.Image())
corpus_origin = f"export {Path(data_root).name}"
if max_samples and len(queries_ds) > max_samples:
queries_ds = queries_ds.shuffle(seed=42).select(range(max_samples))
docid_to_idx = None
if "doc_id" in corpus_ds.column_names:
docid_to_idx = {int(corpus_ds[i]["doc_id"]): i for i in range(len(corpus_ds))}
corpus = Corpus(corpus_data=corpus_ds, docid_to_idx_mapping=docid_to_idx, doc_column_name="image")
rename = {}
if "positive_doc_id" in queries_ds.column_names:
rename["positive_doc_id"] = "pos_target"
if "negative_doc_ids" in queries_ds.column_names:
rename["negative_doc_ids"] = "neg_target"
for old, new in rename.items():
if old in queries_ds.column_names and new not in queries_ds.column_names:
queries_ds = queries_ds.rename_column(old, new)
if "pos_target" not in queries_ds.column_names:
raise ValueError("hardneg queries need positive_doc_id / pos_target")
min_negs = max(1, int(num_negatives)) if use_negatives else 1
if "neg_target" in queries_ds.column_names:
before = len(queries_ds)
lengths = pc.list_value_length(queries_ds.data.column("neg_target"))
valid = pc.fill_null(pc.greater_equal(lengths, min_negs), False)
n_valid = int(pc.sum(pc.cast(valid, pa.int64())).as_py())
if n_valid != before:
queries_ds = queries_ds.select(pc.indices_nonzero(valid).to_pylist())
print(
f"[data] hardneg {before} -> {len(queries_ds)} (>= {min_negs} negs); "
f"corpus={len(corpus_ds)} from {corpus_origin}"
)
if use_negatives:
if "neg_target" not in queries_ds.column_names:
raise ValueError("hardneg queries need negative_doc_ids / neg_target")
wrapped = ColPaliEngineDataset(
queries_ds,
corpus=corpus,
query_column_name="query",
pos_target_column_name="pos_target",
neg_target_column_name="neg_target",
num_negatives=num_negatives,
)
_attach_task_ids(wrapped, queries_ds, corpus_ds, docid_to_idx)
return wrapped
print(f"[data] hardneg in-batch only: queries={len(queries_ds)}")
wrapped = ColPaliEngineDataset(
queries_ds,
corpus=corpus,
query_column_name="query",
pos_target_column_name="pos_target",
neg_target_column_name=None,
num_negatives=0,
)
_attach_task_ids(wrapped, queries_ds, corpus_ds, docid_to_idx)
return wrapped
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