Spaces:
Sleeping
Sleeping
File size: 5,244 Bytes
bda104d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | import copy
import math
import os
from typing import Any, Dict, List, Optional
import lightning as L
import rootutils
import torch
import torch.nn.functional as F
from dora.hydra import hydra_main
from omegaconf import DictConfig
rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
from steerable_retrieval.callbacks.energy import get_dictionary_from_lightningsae, load_state_dict_any # noqa: E402
from steerable_retrieval.experiments.common import ( # noqa: E402
ensure_dir,
finish_wandb,
instantiate_model_and_load,
maybe_start_wandb,
resolve_model_from_source,
save_config,
save_json,
)
from steerable_retrieval.utils import RankedLogger, extras, register_resolvers # noqa: E402
log = RankedLogger(__name__, rank_zero_only=True)
register_resolvers()
def _stability_hungarian(a: torch.Tensor, b: torch.Tensor) -> float:
from scipy.optimize import linear_sum_assignment
sim = (a @ b.t()).detach().cpu().numpy()
row_idx, col_idx = linear_sum_assignment(-sim)
return float(sim[row_idx, col_idx].mean())
def _k_values(n_concepts: int, n_curve_points: int) -> List[int]:
if n_concepts <= 1:
return [1]
out = [1]
for i in range(max(2, n_curve_points)):
frac = i / max(n_curve_points - 1, 1)
k = int(round(math.exp(math.log(1) * (1 - frac) + math.log(n_concepts) * frac)))
out.append(max(1, min(n_concepts, k)))
out.append(n_concepts)
return sorted(set(out))
def run_stability(cfg: DictConfig) -> Dict[str, Any]:
if cfg.get("seed") is not None:
L.seed_everything(int(cfg.seed), workers=True)
source_run_id = resolve_model_from_source(cfg)
output_dir = ensure_dir(str(cfg.output_dir))
save_config(cfg, output_dir)
device = torch.device(cfg.get("device", "cuda:0") if torch.cuda.is_available() else "cpu")
model, ckpt_path = instantiate_model_and_load(cfg, device=device)
d0 = F.normalize(get_dictionary_from_lightningsae(model).to(device), dim=-1)
n_concepts = int(d0.shape[0])
checkpoints = list(cfg.stability.checkpoint_paths)
if not checkpoints:
raise ValueError("stability.checkpoint_paths cannot be empty.")
run = maybe_start_wandb(
cfg=cfg,
job_type="stability_processing",
name=str(cfg.get("run_name", "stability_processing")),
output_dir=output_dir,
)
sorted_indices = torch.arange(n_concepts, device=device)
k_values = _k_values(n_concepts=n_concepts, n_curve_points=int(cfg.stability.n_curve_points))
topk = cfg.stability.get("topk_by_energy")
per_checkpoint = []
all_stabilities = []
all_topk = []
curves = []
for path in checkpoints:
comparison = copy.deepcopy(model).to("cpu")
comparison.load_state_dict(load_state_dict_any(path, map_location="cpu"), strict=False)
comparison = comparison.to(device).eval()
dk = F.normalize(get_dictionary_from_lightningsae(comparison).to(device), dim=-1)
stab_full = _stability_hungarian(d0, dk)
all_stabilities.append(stab_full)
stab_topk = None
if topk is not None:
k = min(int(topk), n_concepts)
top_idx = sorted_indices[:k]
stab_topk = _stability_hungarian(d0[top_idx], dk[top_idx])
all_topk.append(stab_topk)
curve_vals = []
for k in k_values:
top_idx = sorted_indices[:k]
curve_vals.append(_stability_hungarian(d0[top_idx], dk[top_idx]))
curves.append(curve_vals)
per_checkpoint.append(
{
"checkpoint_path": str(path),
"stability_full": stab_full,
"stability_topk": stab_topk,
}
)
mean_curve = [sum(vals) / len(vals) for vals in zip(*curves)]
summary = {
"experiment": "stability_processing",
"config_source_wandb_run_id": source_run_id,
"source_checkpoint": ckpt_path,
"n_concepts": n_concepts,
"per_checkpoint": per_checkpoint,
"mean_stability_full": float(sum(all_stabilities) / len(all_stabilities)),
"mean_stability_topk": float(sum(all_topk) / len(all_topk)) if all_topk else None,
"k_values": k_values,
"mean_stability_curve": mean_curve,
}
save_json(os.path.join(output_dir, "summary.json"), summary)
if run is not None:
import wandb
table = wandb.Table(columns=["checkpoint_path", "stability_full", "stability_topk"])
for item in per_checkpoint:
table.add_data(item["checkpoint_path"], item["stability_full"], item["stability_topk"])
wandb.log({"stability/per_checkpoint": table})
wandb.log({"stability/mean_full": summary["mean_stability_full"]})
if summary["mean_stability_topk"] is not None:
wandb.log({"stability/mean_topk": summary["mean_stability_topk"]})
finish_wandb(run)
return summary
@hydra_main(version_base="1.3", config_path="../../configs/experiment", config_name="stability.yaml")
def main(cfg: DictConfig) -> Optional[Dict[str, Any]]:
os.environ["TOKENIZERS_PARALLELISM"] = "false"
extras(cfg)
return run_stability(cfg)
if __name__ == "__main__":
main()
|