576 lines
21 KiB
Python
576 lines
21 KiB
Python
"""Pretrain a lightweight TimesNet autoencoder on training-set exposure."""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import logging
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import math
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import os
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from pathlib import Path
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import numpy as np
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import torch
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel
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from torch.optim import AdamW
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from torch.utils.data import DataLoader, Dataset, DistributedSampler
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from tqdm import tqdm
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from backbones import TimesNetExposureAutoencoder
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from dataset import ExposureCache
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from train_util import (
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configure_torch_for_training,
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create_unique_run_dir,
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load_eid_file,
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resolve_device,
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set_seed,
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setup_logging,
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)
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class ExposureWindowDataset(Dataset):
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def __init__(self, cache: ExposureCache, row_indices: np.ndarray):
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self.cache = cache
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self.row_indices = np.asarray(row_indices, dtype=np.int64)
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def __len__(self) -> int:
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return len(self.row_indices)
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def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
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row = int(self.row_indices[index])
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return {
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"daily": torch.from_numpy(
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np.array(self.cache.daily[row], dtype=np.float32, copy=True)
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),
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"monthly": torch.from_numpy(
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np.array(self.cache.monthly[row], dtype=np.float32, copy=True)
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),
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}
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(
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description="Pretrain a lightweight TimesNet exposure autoencoder"
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)
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parser.add_argument("--exposure_cache_dir", required=True)
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parser.add_argument("--train_eid_file", default="ukb_train_eid.csv")
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parser.add_argument("--val_eid_file", default="ukb_val_eid.csv")
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parser.add_argument(
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"--channel_stats_file",
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default=None,
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help=(
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"Cached channel statistics .npz file. Defaults to "
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"<exposure_cache_dir>/train_channel_stats.npz."
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),
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)
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parser.add_argument(
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"--recompute_channel_stats",
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action="store_true",
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help="Ignore a compatible statistics cache and recompute it.",
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)
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parser.add_argument("--runs_root", default="runs")
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parser.add_argument("--seed", type=int, default=42)
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parser.add_argument("--n_embd", type=int, default=120)
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parser.add_argument("--d_model", type=int, default=64)
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parser.add_argument("--n_layers", type=int, default=2)
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parser.add_argument("--top_k", type=int, default=2)
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parser.add_argument("--n_backbone_blocks", type=int, default=1)
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parser.add_argument("--backbone_kernel_size", type=int, default=5)
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parser.add_argument("--backbone_expansion", type=float, default=2.0)
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parser.add_argument("--dropout", type=float, default=0.0)
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parser.add_argument("--mask_ratio", type=float, default=0.25)
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parser.add_argument("--batch_size", type=int, default=16)
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parser.add_argument("--base_lr", type=float, default=3e-4)
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parser.add_argument("--weight_decay", type=float, default=0.05)
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parser.add_argument("--max_epochs", type=int, default=100)
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parser.add_argument("--warmup_epochs", type=int, default=5)
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parser.add_argument("--patience", type=int, default=12)
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parser.add_argument("--grad_clip", type=float, default=1.0)
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parser.add_argument("--num_workers", type=int, default=4)
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parser.add_argument("--device", default="cuda")
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parser.add_argument("--amp", action=argparse.BooleanOptionalAction, default=True)
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parser.add_argument(
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"--data_parallel",
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action="store_true",
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help="Use torch.nn.DataParallel across multiple CUDA devices.",
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)
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parser.add_argument(
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"--gpu_ids",
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default=None,
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help="Comma-separated CUDA device ids for --data_parallel, e.g. 0,1,2,3.",
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)
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parser.add_argument(
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"--ddp_backend",
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default=None,
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choices=["nccl", "gloo"],
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help="DDP backend. Defaults to nccl on CUDA and gloo otherwise.",
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)
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parser.add_argument(
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"--prefetch_factor",
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type=int,
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default=4,
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help="DataLoader batches prefetched by each worker.",
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)
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args = parser.parse_args()
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if not 0.0 <= args.mask_ratio < 1.0:
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parser.error("--mask_ratio must be in [0, 1)")
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if args.num_workers > 0 and args.prefetch_factor <= 0:
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parser.error("--prefetch_factor must be positive")
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if args.gpu_ids:
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try:
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args.gpu_ids = [
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int(part.strip())
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for part in args.gpu_ids.split(",")
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if part.strip()
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]
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except ValueError as exc:
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parser.error("--gpu_ids must be a comma-separated list of integers")
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if not args.gpu_ids:
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parser.error("--gpu_ids did not contain any valid CUDA device ids")
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args.data_parallel = True
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return args
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def select_rows(cache: ExposureCache, eids: set[int], split: str) -> np.ndarray:
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valid_row = np.asarray(cache.row_index, dtype=np.int64) >= 0
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selected_events = valid_row & np.isin(cache.eids, np.fromiter(eids, np.int64))
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rows = np.unique(
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np.asarray(cache.row_index[selected_events], dtype=np.int64)
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)
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if len(rows) == 0:
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raise ValueError(f"{split} exposure rows are empty after EID filtering")
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return rows
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def maybe_wrap_data_parallel(
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model: TimesNetExposureAutoencoder,
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args: argparse.Namespace,
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device: torch.device,
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logger,
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):
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if not args.data_parallel:
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return model
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if device.type != "cuda":
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raise ValueError("--data_parallel requires --device cuda or cuda:<id>")
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if not torch.cuda.is_available() or torch.cuda.device_count() < 2:
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raise ValueError("--data_parallel requires at least two CUDA devices")
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primary = (
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int(device.index)
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if device.index is not None
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else int(torch.cuda.current_device())
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)
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device_ids = (
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args.gpu_ids
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if args.gpu_ids
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else list(range(torch.cuda.device_count()))
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)
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device_ids = [primary, *[idx for idx in device_ids if idx != primary]]
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if len(device_ids) < 2:
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raise ValueError("--data_parallel needs at least two device ids")
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if any(idx < 0 or idx >= torch.cuda.device_count() for idx in device_ids):
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raise ValueError(f"CUDA device id is out of range: {device_ids}")
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logger.info(f"Using DataParallel on CUDA devices: {device_ids}")
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return torch.nn.DataParallel(
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model, device_ids=device_ids, output_device=primary
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)
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def unwrap_model(model) -> TimesNetExposureAutoencoder:
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if isinstance(model, (torch.nn.DataParallel, DistributedDataParallel)):
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return model.module
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return model
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def init_distributed(
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args: argparse.Namespace,
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) -> tuple[torch.device, int, int, int]:
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world_size = int(os.environ.get("WORLD_SIZE", "1"))
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if world_size == 1:
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return resolve_device(args.device), 0, 0, 1
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if args.data_parallel:
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raise ValueError("--data_parallel cannot be combined with torchrun/DDP")
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local_rank = int(os.environ["LOCAL_RANK"])
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rank = int(os.environ["RANK"])
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if not torch.cuda.is_available():
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raise ValueError("Multi-process exposure training requires CUDA")
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torch.cuda.set_device(local_rank)
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backend = args.ddp_backend or "nccl"
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dist.init_process_group(backend=backend, init_method="env://")
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return torch.device("cuda", local_rank), rank, local_rank, world_size
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def rank_logger(rank: int, run_dir: Path):
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if rank == 0:
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return setup_logging(run_dir)
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logger = logging.getLogger(f"DeepHealth.rank{rank}")
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logger.handlers.clear()
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logger.addHandler(logging.NullHandler())
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return logger
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def distributed_run_dir(
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args: argparse.Namespace, rank: int, world_size: int
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) -> tuple[Path, str]:
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payload: list[str | None] = [None, None]
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if rank == 0:
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run_dir, run_name = create_unique_run_dir(
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lambda stamp: f"exposure_ae_{stamp}", Path(args.runs_root)
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)
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payload = [str(run_dir), run_name]
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if world_size > 1:
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dist.broadcast_object_list(payload, src=0)
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return Path(str(payload[0])), str(payload[1])
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def channel_stats(
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cache: ExposureCache, rows: np.ndarray, chunk_size: int = 256
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) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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results = []
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for source in (cache.daily, cache.monthly):
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sums = np.zeros(source.shape[-1], dtype=np.float64)
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squares = np.zeros_like(sums)
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counts = np.zeros_like(sums)
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for start in tqdm(range(0, len(rows), chunk_size), desc="Channel statistics"):
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values = np.asarray(source[rows[start:start + chunk_size]], dtype=np.float64)
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finite = np.isfinite(values)
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clean = np.where(finite, values, 0.0)
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sums += clean.sum(axis=(0, 1))
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squares += np.square(clean).sum(axis=(0, 1))
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counts += finite.sum(axis=(0, 1))
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mean = sums / np.maximum(counts, 1.0)
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variance = squares / np.maximum(counts, 1.0) - np.square(mean)
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std = np.sqrt(np.maximum(variance, 1e-12))
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results.extend([mean.astype(np.float32), std.astype(np.float32)])
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return tuple(results)
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def eid_set_hash(eids: set[int]) -> str:
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digest = hashlib.sha256()
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for eid in sorted(eids):
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digest.update(f"{eid}\n".encode("ascii"))
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return digest.hexdigest()
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def load_or_compute_channel_stats(
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cache: ExposureCache,
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rows: np.ndarray,
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train_eids: set[int],
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stats_path: Path,
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recompute: bool,
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logger,
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) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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eid_hash = eid_set_hash(train_eids)
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if stats_path.is_file() and not recompute:
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try:
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with np.load(stats_path, allow_pickle=False) as saved:
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compatible = (
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str(saved["train_eid_sha256"].item()) == eid_hash
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and int(saved["cache_event_rows"].item()) == len(cache.eids)
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and int(saved["train_window_rows"].item()) == len(rows)
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)
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if compatible:
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logger.info(f"Loading channel statistics from {stats_path}")
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return (
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saved["daily_mean"].astype(np.float32),
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saved["daily_std"].astype(np.float32),
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saved["monthly_mean"].astype(np.float32),
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saved["monthly_std"].astype(np.float32),
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)
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logger.info("Channel statistics cache is stale; recomputing")
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except (KeyError, OSError, ValueError) as exc:
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logger.warning(
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f"Could not read channel statistics cache ({exc}); recomputing"
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)
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logger.info("Computing channel statistics from training exposure")
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stats = channel_stats(cache, rows)
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stats_path.parent.mkdir(parents=True, exist_ok=True)
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np.savez(
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stats_path,
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daily_mean=stats[0],
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daily_std=stats[1],
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monthly_mean=stats[2],
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monthly_std=stats[3],
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train_eid_sha256=np.asarray(eid_hash),
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cache_event_rows=np.asarray(len(cache.eids), dtype=np.int64),
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train_window_rows=np.asarray(len(rows), dtype=np.int64),
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)
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logger.info(f"Saved channel statistics to {stats_path}")
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return stats
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def masked_mse(
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prediction: torch.Tensor, target: torch.Tensor, mask: torch.Tensor
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) -> torch.Tensor:
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error = (prediction - target).square() * mask
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return error.sum() / mask.sum().clamp_min(1.0)
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def run_epoch(
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model,
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loader: DataLoader,
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device: torch.device,
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stats: tuple[torch.Tensor, ...],
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mask_ratio: float,
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optimizer: AdamW | None,
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scaler: torch.amp.GradScaler,
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grad_clip: float,
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amp_enabled: bool,
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show_progress: bool,
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) -> float:
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training = optimizer is not None
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model.train(training)
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loss_accumulator = torch.zeros(2, device=device, dtype=torch.float64)
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daily_mean, daily_std, monthly_mean, monthly_std = stats
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context = torch.enable_grad if training else torch.no_grad
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with context():
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for batch in tqdm(
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loader,
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desc="train" if training else "val",
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leave=False,
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disable=not show_progress,
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):
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daily = batch["daily"].to(device, non_blocking=True)
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monthly = batch["monthly"].to(device, non_blocking=True)
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daily_observed = torch.isfinite(daily)
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monthly_observed = torch.isfinite(monthly)
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daily = (torch.nan_to_num(daily) - daily_mean) / daily_std
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monthly = (torch.nan_to_num(monthly) - monthly_mean) / monthly_std
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daily = daily * daily_observed
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monthly = monthly * monthly_observed
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if training and mask_ratio > 0:
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daily_input_mask = daily_observed & (
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torch.rand_like(daily) >= mask_ratio
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)
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monthly_input_mask = monthly_observed & (
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torch.rand_like(monthly) >= mask_ratio
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)
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else:
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daily_input_mask = daily_observed
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monthly_input_mask = monthly_observed
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daily_input = daily * daily_input_mask
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monthly_input = monthly * monthly_input_mask
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if training:
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optimizer.zero_grad(set_to_none=True)
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with torch.autocast(
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device_type=device.type, dtype=torch.float16,
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enabled=amp_enabled,
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):
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daily_hat, monthly_hat, _ = model(
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daily_input, monthly_input,
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daily_input_mask, monthly_input_mask,
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)
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loss = (
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masked_mse(daily_hat, daily, daily_observed)
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+ masked_mse(monthly_hat, monthly, monthly_observed)
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)
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if training:
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scaler.scale(loss).backward()
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
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scaler.step(optimizer)
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scaler.update()
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batch_size = daily.size(0)
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loss_accumulator[0] += loss.detach().double() * batch_size
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loss_accumulator[1] += batch_size
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if dist.is_initialized():
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dist.all_reduce(loss_accumulator, op=dist.ReduceOp.SUM)
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return float((loss_accumulator[0] / loss_accumulator[1].clamp_min(1)).item())
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def learning_rate(epoch: int, args: argparse.Namespace) -> float:
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if epoch < args.warmup_epochs:
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return args.base_lr * (epoch + 1) / max(args.warmup_epochs, 1)
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progress = (epoch - args.warmup_epochs) / max(
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args.max_epochs - args.warmup_epochs - 1, 1
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)
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return args.base_lr * 0.5 * (1.0 + math.cos(math.pi * progress))
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def main() -> None:
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args = parse_args()
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device, rank, local_rank, world_size = init_distributed(args)
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set_seed(args.seed + rank)
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configure_torch_for_training(device)
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run_dir, run_name = distributed_run_dir(args, rank, world_size)
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logger = rank_logger(rank, run_dir)
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cache = ExposureCache(args.exposure_cache_dir)
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train_eids = load_eid_file(args.train_eid_file)
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val_eids = load_eid_file(args.val_eid_file)
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if train_eids & val_eids:
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raise ValueError("train and validation EID files must be disjoint")
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train_rows = select_rows(cache, train_eids, "Training")
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val_rows = select_rows(cache, val_eids, "Validation")
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stats_path = (
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Path(args.channel_stats_file)
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if args.channel_stats_file
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else Path(args.exposure_cache_dir) / "train_channel_stats.npz"
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)
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if rank == 0:
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raw_stats = load_or_compute_channel_stats(
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cache,
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train_rows,
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train_eids,
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stats_path,
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args.recompute_channel_stats,
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logger,
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)
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if world_size > 1:
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dist.barrier()
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if rank != 0:
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raw_stats = load_or_compute_channel_stats(
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cache, train_rows, train_eids, stats_path, False, logger
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)
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stats = tuple(
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torch.as_tensor(value, device=device).view(1, 1, -1)
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for value in raw_stats
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)
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if args.batch_size % world_size != 0:
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raise ValueError(
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f"--batch_size={args.batch_size} must be divisible by "
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f"DDP world size {world_size}"
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)
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local_batch_size = args.batch_size // world_size
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loader_kwargs = dict(
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batch_size=local_batch_size,
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num_workers=args.num_workers,
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pin_memory=device.type == "cuda",
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persistent_workers=args.num_workers > 0,
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)
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if args.num_workers > 0:
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loader_kwargs["prefetch_factor"] = args.prefetch_factor
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train_dataset = ExposureWindowDataset(cache, train_rows)
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val_dataset = ExposureWindowDataset(cache, val_rows)
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train_sampler = (
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DistributedSampler(
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train_dataset, num_replicas=world_size, rank=rank,
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shuffle=True, seed=args.seed,
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)
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if world_size > 1 else None
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)
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val_sampler = (
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DistributedSampler(
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val_dataset, num_replicas=world_size, rank=rank, shuffle=False
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)
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if world_size > 1 else None
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)
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train_loader = DataLoader(
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train_dataset, sampler=train_sampler,
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shuffle=train_sampler is None, **loader_kwargs
|
|
)
|
|
val_loader = DataLoader(
|
|
val_dataset, sampler=val_sampler, shuffle=False, **loader_kwargs
|
|
)
|
|
model = TimesNetExposureAutoencoder(
|
|
n_embd=args.n_embd, d_model=args.d_model, n_layers=args.n_layers,
|
|
top_k=args.top_k, n_backbone_blocks=args.n_backbone_blocks,
|
|
backbone_kernel_size=args.backbone_kernel_size,
|
|
backbone_expansion=args.backbone_expansion,
|
|
dropout=args.dropout,
|
|
).to(device)
|
|
if world_size > 1:
|
|
model = DistributedDataParallel(
|
|
model, device_ids=[local_rank], output_device=local_rank
|
|
)
|
|
logger.info(
|
|
f"Using DDP with {world_size} processes; "
|
|
f"global_batch={args.batch_size}, per_gpu_batch={local_batch_size}"
|
|
)
|
|
else:
|
|
model = maybe_wrap_data_parallel(model, args, device, logger)
|
|
optimizer = AdamW(
|
|
model.parameters(), lr=args.base_lr,
|
|
weight_decay=args.weight_decay, betas=(0.9, 0.95),
|
|
)
|
|
amp_enabled = bool(args.amp and device.type == "cuda")
|
|
scaler = torch.amp.GradScaler("cuda", enabled=amp_enabled)
|
|
logger.info(
|
|
f"Run {run_name}: device={device}, train_rows={len(train_rows):,}, "
|
|
f"val_rows={len(val_rows):,}"
|
|
)
|
|
config = vars(args) | {
|
|
"train_rows": len(train_rows),
|
|
"val_rows": len(val_rows),
|
|
"daily_mean": raw_stats[0].tolist(),
|
|
"daily_std": raw_stats[1].tolist(),
|
|
"monthly_mean": raw_stats[2].tolist(),
|
|
"monthly_std": raw_stats[3].tolist(),
|
|
}
|
|
if rank == 0:
|
|
(run_dir / "train_config.json").write_text(
|
|
json.dumps(config, indent=2), encoding="utf-8"
|
|
)
|
|
|
|
best_loss = float("inf")
|
|
stale_epochs = 0
|
|
history = []
|
|
for epoch in range(args.max_epochs):
|
|
if train_sampler is not None:
|
|
train_sampler.set_epoch(epoch)
|
|
lr = learning_rate(epoch, args)
|
|
for group in optimizer.param_groups:
|
|
group["lr"] = lr
|
|
train_loss = run_epoch(
|
|
model, train_loader, device, stats, args.mask_ratio, optimizer,
|
|
scaler, args.grad_clip, amp_enabled, rank == 0,
|
|
)
|
|
val_loss = run_epoch(
|
|
model, val_loader, device, stats, 0.0, None,
|
|
scaler, args.grad_clip, amp_enabled, rank == 0,
|
|
)
|
|
logger.info(
|
|
f"Epoch {epoch + 1:03d} | lr={lr:.3e} | "
|
|
f"train={train_loss:.6f} | val={val_loss:.6f}"
|
|
)
|
|
history.append(
|
|
{"epoch": epoch + 1, "lr": lr,
|
|
"train_loss": train_loss, "val_loss": val_loss}
|
|
)
|
|
if val_loss < best_loss:
|
|
best_loss = val_loss
|
|
stale_epochs = 0
|
|
if rank == 0:
|
|
checkpoint_model = unwrap_model(model)
|
|
torch.save(
|
|
{
|
|
"model_state_dict": checkpoint_model.state_dict(),
|
|
"encoder_state_dict": checkpoint_model.encoder.state_dict(),
|
|
"model_config": {
|
|
key: config[key] for key in (
|
|
"n_embd", "d_model", "n_layers", "top_k",
|
|
"n_backbone_blocks", "backbone_kernel_size",
|
|
"backbone_expansion", "dropout",
|
|
)
|
|
},
|
|
"normalization": {
|
|
"daily_mean": raw_stats[0],
|
|
"daily_std": raw_stats[1],
|
|
"monthly_mean": raw_stats[2],
|
|
"monthly_std": raw_stats[3],
|
|
},
|
|
"epoch": epoch + 1,
|
|
"val_loss": val_loss,
|
|
},
|
|
run_dir / "best.pt",
|
|
)
|
|
else:
|
|
stale_epochs += 1
|
|
if rank == 0:
|
|
(run_dir / "history.json").write_text(
|
|
json.dumps(history, indent=2), encoding="utf-8"
|
|
)
|
|
if stale_epochs >= args.patience:
|
|
logger.info(f"Early stopping after {epoch + 1} epochs")
|
|
break
|
|
logger.info(f"Best validation loss: {best_loss:.6f}")
|
|
logger.info(f"Checkpoint: {run_dir / 'best.pt'}")
|
|
if dist.is_initialized():
|
|
dist.destroy_process_group()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|