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import json
import time
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
import torch
from transformers import Trainer
from .manifest import build_manifest_from_dir
from .vocab import build_gene_vocab_from_manifest, build_meta_vocab_from_manifest
logger = logging.getLogger("scRep_train")
# =============================================================================
# Distributed helpers
# =============================================================================
def dist_is_initialized() -> bool:
return torch.distributed.is_available() and torch.distributed.is_initialized()
def dist_rank_world() -> Tuple[int, int]:
if dist_is_initialized():
return torch.distributed.get_rank(), torch.distributed.get_world_size()
rank = int(os.environ.get("RANK", "0"))
world = int(os.environ.get("WORLD_SIZE", "1"))
return rank, world
def dist_barrier() -> None:
if dist_is_initialized():
torch.distributed.barrier()
def is_world_process_zero() -> bool:
rank, _ = dist_rank_world()
return rank == 0
def wait_for_file(path: str, timeout: float = 300.0, poll: float = 0.5) -> None:
"""Wait until a file exists and is readable."""
p = Path(path)
start = time.time()
last_err = None
while time.time() - start < timeout:
if p.exists():
try:
with open(p, "r") as f:
_ = f.read(1)
return
except Exception as e:
last_err = e
time.sleep(poll)
raise TimeoutError(f"Timed out waiting for file: {path}. Last error: {last_err}")
# =============================================================================
# JSON helpers
# =============================================================================
def load_json(path: str) -> Any:
with open(path, "r") as f:
return json.load(f)
def save_json(obj: Any, path: str) -> None:
os.makedirs(os.path.dirname(path), exist_ok=True)
tmp_path = path + ".tmp"
with open(tmp_path, "w") as f:
json.dump(obj, f, ensure_ascii=False, indent=2)
f.flush()
os.fsync(f.fileno())
os.replace(tmp_path, path)
# =============================================================================
# FSDP / Trainer helpers
# =============================================================================
def parse_fsdp_list(fsdp_str: Optional[str]) -> Optional[List[str]]:
s = (fsdp_str or "").strip()
if not s:
return None
s = s.replace(",", " ")
parts = [p.strip() for p in s.split() if p.strip()]
return parts if parts else None
def load_fsdp_config(path: Optional[str]) -> Optional[Dict[str, Any]]:
if not path:
return None
if not os.path.exists(path):
raise FileNotFoundError(f"fsdp_config not found: {path}")
return load_json(path)
def normalize_report_to(report_to: str):
return [] if report_to == "none" else report_to
class TrainerBatchWrapperCollator:
"""Wrap the collated batch as {"batch": ...} for Trainer."""
def __init__(self, base_collator):
self.base = base_collator
def __call__(self, examples):
return {"batch": self.base(examples)}
# =============================================================================
# Asset helpers
# =============================================================================
def gene_vocab_to_map(gene_vocab: Dict[str, Any]) -> Dict[str, int]:
gene_offset = int(gene_vocab.get("gene_offset", 2))
gene_names = list(gene_vocab["gene_names"])
return {str(name): i + gene_offset for i, name in enumerate(gene_names)}
def _normalize_meta_keys(meta_keys: str) -> List[str]:
return [k.strip() for k in meta_keys.split(",") if k.strip()]
def _asset_metadata(args) -> Dict[str, Any]:
return {
"data_dir": os.path.abspath(args.data_dir),
"manifest_json": os.path.abspath(args.manifest_json) if args.manifest_json else "",
"gene_vocab_json": os.path.abspath(args.gene_vocab_json) if getattr(args, "gene_vocab_json", "") else "",
"use_raw": bool(args.use_raw),
"backed": args.backed,
"meta_keys": _normalize_meta_keys(args.meta_keys),
}
def prepare_scratch_assets(args, output_dir: str) -> Tuple[Any, Dict[str, Any], Dict[str, Any], Dict[str, int], Dict[str, Dict[str, int]], Dict[str, int]]:
is_main = is_world_process_zero()
manifest_path = os.path.join(output_dir, "manifest.json")
gene_vocab_path = os.path.join(output_dir, "gene_vocab.json")
meta_vocab_path = os.path.join(output_dir, "meta_vocab.json")
metadata_path = os.path.join(output_dir, "asset_metadata.json")
expected_metadata = _asset_metadata(args)
cache_complete = all(
os.path.exists(path)
for path in (manifest_path, gene_vocab_path, meta_vocab_path, metadata_path)
)
metadata_matches = False
cache_reason = "missing cached asset files"
if cache_complete:
cached_metadata = load_json(metadata_path)
metadata_matches = cached_metadata == expected_metadata
if not metadata_matches:
cache_reason = "cached asset metadata does not match current dataset/config"
if cache_complete and metadata_matches:
logger.info("loading cached scratch assets from %s", output_dir)
manifest = load_json(manifest_path)
gene_vocab = load_json(gene_vocab_path)
meta_vocab = load_json(meta_vocab_path)
gene_name_to_id = gene_vocab_to_map(gene_vocab)
meta_maps = meta_vocab["meta_maps"]
meta_vocab_sizes = meta_vocab["meta_vocab_sizes"]
logger.info(
"loaded cached scratch assets: manifest=%s datasets, genes=%s, meta_keys=%s",
len(manifest),
gene_vocab.get("gene_vocab_size", 0),
list(meta_vocab_sizes.keys()),
)
return manifest, gene_vocab, meta_vocab, gene_name_to_id, meta_maps, meta_vocab_sizes
if is_main:
logger.info("scratch assets cache miss in %s: %s", output_dir, cache_reason)
os.makedirs(output_dir, exist_ok=True)
if args.manifest_json and os.path.exists(args.manifest_json):
logger.info("building manifest from explicit manifest_json=%s", args.manifest_json)
manifest = load_json(args.manifest_json)
save_json(manifest, manifest_path)
else:
logger.info("building manifest from data_dir=%s", args.data_dir)
manifest_start = time.time()
manifest = build_manifest_from_dir(
args.data_dir,
backed=args.backed,
use_raw=bool(args.use_raw),
)
if len(manifest) == 0:
raise RuntimeError(
"manifest is empty when building from `data_dir`. "
"This usually means your h5ad files were filtered out.\n\n"
"Common causes:\n"
"1) `--use_raw` is enabled but your .h5ad has no `adata.raw`.\n"
"2) Wrong `data_dir` / file pattern (no .h5ad found).\n"
"3) Required obs/meta columns mismatch (meta_keys).\n\n"
f"args.use_raw={args.use_raw}, data_dir={args.data_dir}"
)
save_json(manifest, manifest_path)
logger.info("manifest saved to %s in %.1fs", manifest_path, time.time() - manifest_start)
meta_keys = _normalize_meta_keys(args.meta_keys)
if getattr(args, "gene_vocab_json", ""):
logger.info("loading fixed gene vocab from %s", args.gene_vocab_json)
gene_vocab = load_json(args.gene_vocab_json)
gene_names = list(gene_vocab["gene_names"])
gene_name_to_id = gene_vocab_to_map(gene_vocab)
logger.info("fixed gene vocab loaded with %s genes", len(gene_names))
else:
logger.info("building gene vocab from %s manifest entries", len(manifest))
gene_start = time.time()
gene_name_to_id, gene_names = build_gene_vocab_from_manifest(
manifest,
use_raw=args.use_raw,
backed=args.backed,
gene_offset=2,
sort_paths=True,
)
logger.info("gene vocab built with %s genes in %.1fs", len(gene_names), time.time() - gene_start)
logger.info("building meta vocab for keys=%s", meta_keys)
meta_start = time.time()
meta_maps, meta_vocab_sizes = build_meta_vocab_from_manifest(
manifest,
keys=meta_keys,
backed=args.backed,
sort_paths=True,
)
logger.info("meta vocab built in %.1fs", time.time() - meta_start)
if not getattr(args, "gene_vocab_json", ""):
gene_vocab = {
"gene_offset": 2,
"gene_names": gene_names,
"gene_vocab_size": max(gene_name_to_id.values()) + 1,
}
meta_vocab = {
"meta_vocab_sizes": meta_vocab_sizes,
"meta_maps": meta_maps,
}
save_json(gene_vocab, gene_vocab_path)
save_json(meta_vocab, meta_vocab_path)
save_json(expected_metadata, metadata_path)
logger.info("scratch assets saved to %s", output_dir)
else:
logger.info("waiting for scratch assets to be prepared in %s", output_dir)
wait_for_file(metadata_path)
manifest = load_json(manifest_path)
gene_vocab = load_json(gene_vocab_path)
meta_vocab = load_json(meta_vocab_path)
gene_name_to_id = gene_vocab_to_map(gene_vocab)
meta_maps = meta_vocab["meta_maps"]
meta_vocab_sizes = meta_vocab["meta_vocab_sizes"]
logger.info("loaded scratch assets prepared by rank0 from %s", output_dir)
return manifest, gene_vocab, meta_vocab, gene_name_to_id, meta_maps, meta_vocab_sizes
def save_bundle(
trainer: Trainer,
model,
save_dir: str,
*,
gene_vocab: Dict[str, Any],
meta_vocab: Dict[str, Any],
manifest: Any,
) -> None:
"""
Save a final bundle that is directly loadable by ``CellContrastivePredictModel.from_pretrained``.
Under FSDP we explicitly ask Accelerate for a gathered state dict, then save it
through the unwrapped model. All ranks participate in ``get_state_dict``; only
world-process-zero writes files.
"""
os.makedirs(save_dir, exist_ok=True)
if hasattr(trainer, "accelerator") and trainer.accelerator is not None:
unwrapped_model = trainer.accelerator.unwrap_model(model)
state_dict = trainer.accelerator.get_state_dict(trainer.model)
if trainer.is_world_process_zero():
unwrapped_model.save_pretrained(
save_dir,
save_safetensors=bool(trainer.args.save_safetensors),
state_dict=state_dict,
gene_vocab=gene_vocab,
meta_vocab=meta_vocab,
manifest=manifest,
extra_files={"training_args.json": trainer.args.to_dict()},
)
else:
if not trainer.is_world_process_zero():
return
model.save_pretrained(
save_dir,
save_safetensors=bool(trainer.args.save_safetensors),
gene_vocab=gene_vocab,
meta_vocab=meta_vocab,
manifest=manifest,
extra_files={"training_args.json": trainer.args.to_dict()},
)
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