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from collections.abc import Sequence
import json
import math
from pathlib import Path
import random
import sqlite3
from .atomic import atomic_output
from .inputs import validate_text
from .provenance import fingerprint, sha256
AUDIT_FIELDS = ("source_id", "group_id", "recording_id")
MAX_BLOCK_BYTES = 64 * 1024 * 1024
MAX_BLOCK_ROWS = 2048
INDEX_VERSION = 2
def validate_row(row, location):
validate_text(row.get("question"), row.get("criteria"))
if not isinstance(row.get("state"), dict):
raise ValueError(f"{location} needs a state object")
target = row.get("target")
if isinstance(target, bool) or not isinstance(target, int) or not 0 <= target < len(row["criteria"]):
raise ValueError(f"{location} has an invalid target index")
for name in AUDIT_FIELDS:
if not isinstance(row.get(name), str) or not row[name].strip():
raise ValueError(f"{location} requires audit field {name}")
class _Rows(Sequence):
def __init__(self, dataset):
self.dataset = dataset
def __len__(self):
return self.dataset.count
def __iter__(self):
for block in self.dataset._blocks():
yield from self.dataset._read_block(block)
def __getitem__(self, index):
if isinstance(index, slice):
return [self[i] for i in range(*index.indices(len(self)))]
if index < 0:
index += len(self)
if not 0 <= index < len(self):
raise IndexError(index)
block = self.dataset.db.execute(
'SELECT * FROM blocks WHERE row_start<=? ORDER BY row_start DESC LIMIT 1', (index,)).fetchone()
return self.dataset._read_block(block)[index-block["row_start"]]
class ChoiceDataset:
"""Keep waveform rows on disk; hold at most one bounded block and one batch."""
def __init__(self, path):
self.path = Path(path).resolve()
self.row_indices = None
self.manifest_snapshot = None
if self.path.is_dir():
self.files = sorted(self.path.rglob("*.parquet"))
index = self.path / ".paya-index.sqlite3"
elif self.path.suffix == ".json":
manifest = json.loads(self.path.read_text(encoding="utf-8"))
base = Path(manifest.get("path_base", self.path.parent))
self.files = [(base / p).resolve() for p in manifest["files"]]
info = self.path.stat()
self.manifest_snapshot = (info.st_size, info.st_mtime_ns)
if "row_indices" in manifest:
self.row_indices = {}
for name, groups in manifest['row_indices'].items():
source = str((base/name).resolve())
if Path(source) not in self.files or not isinstance(groups,dict):
raise ValueError('Row selection must refer to a declared source file')
self.row_indices[source] = {}
for group, values in groups.items():
if str(int(group)) != group or int(group)<0 or not isinstance(values,list):
raise ValueError('Invalid row-group selection')
if any(isinstance(v,bool) or not isinstance(v,int) or v<0 for v in values) or len(set(values))!=len(values):
raise ValueError('Row positions must be distinct nonnegative integers')
self.row_indices[source][int(group)] = values
index = self.path.with_suffix(".paya-index.sqlite3")
else:
self.files = [self.path]
index = self.path.with_name(self.path.name + ".paya-index.sqlite3")
if not self.files or len(set(self.files)) != len(self.files):
raise ValueError("Dataset must name nonempty, distinct input files")
if any(p.suffix not in (".jsonl", ".parquet") for p in self.files):
raise ValueError("Dataset files must be JSONL or Parquet")
if self.row_indices is not None and any(p.suffix != '.parquet' for p in self.files):
raise ValueError('Row selections require bounded Parquet sources')
self.snapshots = [(str(p), p.stat().st_size, p.stat().st_mtime_ns) for p in self.files]
self.file_stats = {name: (size, modified) for name, size, modified in self.snapshots}
identities = [(str(p), sha256(p)) for p in self.files]
self.sha256 = identities[0][1] if len(identities) == 1 else fingerprint(identities)
if self.row_indices is not None:
self.sha256 = fingerprint({'files':identities,'row_indices':self.row_indices})
index = index.with_name(index.stem + "." + self.sha256[:16] + index.suffix)
binding = json.dumps({"version": INDEX_VERSION, "files": identities,
**({'row_indices':self.row_indices} if self.row_indices is not None else {})}, sort_keys=True)
valid = False
if index.exists():
try:
with sqlite3.connect(index) as check:
valid = check.execute("SELECT value FROM metadata WHERE key='binding'").fetchone() == (binding,)
except sqlite3.DatabaseError:
valid = False
if not valid:
self._build_index(index, binding)
self.index_path = index
self.db = sqlite3.connect(index)
self.db.row_factory = sqlite3.Row
self.db.execute("PRAGMA cache_size=-8192")
self.db.execute("PRAGMA temp_store=FILE")
self.count, self.block_count = self.db.execute('SELECT SUM(nrows),COUNT(*) FROM blocks').fetchone()
if not self.count:
raise ValueError("Dataset is empty")
self.rows = _Rows(self)
def assert_unchanged(self):
if self.manifest_snapshot is not None:
info = self.path.stat()
if (info.st_size,info.st_mtime_ns) != self.manifest_snapshot:
raise ValueError('Dataset manifest changed during training')
current = [(str(p), p.stat().st_size, p.stat().st_mtime_ns) for p in self.files]
if current != self.snapshots:
raise ValueError("Dataset changed during training")
def _build_index(self, index, binding):
with atomic_output(index) as temporary:
db = sqlite3.connect(temporary.name)
try:
db.executescript('''
PRAGMA cache_size=-8192;
PRAGMA temp_store=FILE;
CREATE TABLE metadata(key TEXT PRIMARY KEY,value TEXT);
CREATE TABLE blocks(id INTEGER PRIMARY KEY,filename TEXT,kind TEXT,position INTEGER,
nrows INTEGER,row_start INTEGER,byte_length INTEGER);
CREATE INDEX block_start ON blocks(row_start);
CREATE TABLE audit(field TEXT,value TEXT,PRIMARY KEY(field,value)) WITHOUT ROWID;
''')
offset = 0
def add(path, kind, position, rows, byte_length=0):
nonlocal offset
for i, row in enumerate(rows):
validate_row(row, f"{path.name} row {offset+i+1}")
db.execute('INSERT INTO blocks(filename,kind,position,nrows,row_start,byte_length) VALUES(?,?,?,?,?,?)',
(str(path),kind,position,len(rows),offset,byte_length))
db.executemany('INSERT OR IGNORE INTO audit VALUES(?,?)',
((field,row[field]) for row in rows for field in AUDIT_FIELDS))
offset += len(rows)
for path in self.files:
if path.suffix == ".parquet":
import pyarrow.parquet as pq
pf = pq.ParquetFile(path)
if "state" not in pf.schema_arrow.names:
raise ValueError("Parquet dataset requires state")
columns = ["question","criteria","target",*AUDIT_FIELDS]
selected = self.row_indices.get(str(path),{}) if self.row_indices is not None else None
if selected is not None and any(g>=pf.metadata.num_row_groups for g in selected):
raise ValueError('Selected row group does not exist')
for group in range(pf.metadata.num_row_groups):
if selected is not None and not selected.get(group):
continue
info = pf.metadata.row_group(group)
widths = {"BOOLEAN": 1, "INT32": 4, "INT64": 8, "INT96": 12, "FLOAT": 4, "DOUBLE": 8}
numeric_bytes = sum(info.column(i).num_values * widths.get(info.column(i).physical_type,0)
for i in range(info.num_columns))
if info.num_rows > MAX_BLOCK_ROWS or max(info.total_byte_size,numeric_bytes) > MAX_BLOCK_BYTES:
raise ValueError("Parquet row group exceeds memory budget; repack with 256 rows per group")
table = pf.read_row_group(group,columns=columns)
if selected is not None:
if max(selected[group]) >= len(table):
raise ValueError('Selected row position is out of bounds')
table = table.take(selected[group])
rows = table.to_pylist()
for row in rows:
row["state"] = {}
if rows:
add(path,"parquet",group,rows)
else:
with path.open("rb") as source:
rows=[]; start=source.tell()
while True:
position=source.tell()
line=source.readline(MAX_BLOCK_BYTES+1)
if len(line)>MAX_BLOCK_BYTES:
raise ValueError("JSONL row exceeds memory budget")
if not line:
if rows: add(path,"jsonl",start,rows,source.tell()-start)
break
if rows and (len(rows)>=256 or source.tell()-start>MAX_BLOCK_BYTES):
add(path,"jsonl",start,rows,position-start)
rows=[];start=position
if line.strip():
rows.append(json.loads(line))
if not offset:
raise ValueError("Dataset is empty")
db.execute("INSERT INTO metadata VALUES('binding',?)",(binding,))
db.commit()
finally:
db.close()
def _blocks(self, seed=None):
if seed is None:
yield from self.db.execute('SELECT * FROM blocks ORDER BY id')
return
rng = random.Random(seed)
a = rng.randrange(1,self.block_count+1)
while math.gcd(a,self.block_count) != 1:
a = rng.randrange(1,self.block_count+1)
b = rng.randrange(self.block_count)
for i in range(self.block_count):
identifier = (a*i+b)%self.block_count+1
yield self.db.execute('SELECT * FROM blocks WHERE id=?',(identifier,)).fetchone()
def _read_block(self, block):
info=Path(block['filename']).stat()
if (info.st_size,info.st_mtime_ns) != self.file_stats[block['filename']]:
raise ValueError("Dataset changed during training")
if block["kind"] == "parquet":
import pyarrow.parquet as pq
table = pq.ParquetFile(block["filename"]).read_row_group(block["position"])
if self.row_indices is not None:
table = table.take(self.row_indices[block['filename']][block['position']])
rows = table.to_pylist()
else:
with open(block["filename"],"rb") as source:
source.seek(block["position"])
rows = [json.loads(line) for line in source.read(block["byte_length"]).splitlines() if line.strip()]
if len(rows) != block["nrows"]:
raise ValueError("Dataset block changed")
return rows
def batches(self, batch_size, seed=None, start_batch=0):
if isinstance(batch_size,bool) or not isinstance(batch_size,int) or batch_size<1:
raise ValueError("batch_size must be a positive integer")
if isinstance(start_batch,bool) or not isinstance(start_batch,int) or start_batch<0:
raise ValueError("start_batch must be a nonnegative integer")
self.assert_unchanged()
skip=start_batch*batch_size
pending=[]
for block in self._blocks(seed):
if skip>=block['nrows']:
skip-=block['nrows']
continue
values=self._read_block(block)
order=list(range(len(values)))
if seed is not None:
random.Random(f'{seed}:block:{block["id"]}').shuffle(order)
for position in order[skip:]:
row=values[position].copy()
if seed is not None:
permutation=list(range(len(row['criteria'])))
random.Random(f'{seed}:block:{block["id"]}:row:{position}').shuffle(permutation)
row['criteria']=[row['criteria'][i] for i in permutation]
row['target']=permutation.index(row['target'])
pending.append(row)
if len(pending)==batch_size:
yield pending
pending=[]
skip=0
if pending:
yield pending
def assert_disjoint(*datasets):
for i, left in enumerate(datasets):
for right in datasets[i+1:]:
left.db.execute('ATTACH DATABASE ? AS other',(str(right.index_path),))
try:
overlap=left.db.execute('SELECT a.field FROM audit a JOIN other.audit b '
'ON a.field=b.field AND a.value=b.value LIMIT 1').fetchone()
if overlap:
raise ValueError(f'Dataset splits overlap on {overlap[0]}')
finally:
left.db.execute('DETACH DATABASE other')
def make_batch(model, rows):
return model.make_batch([r["state"] for r in rows], [r["question"] for r in rows],
[r["criteria"] for r in rows])
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