File size: 9,516 Bytes
bc29ee3 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 | #!/usr/bin/env python3
"""Normalize three-model hidden snapshots into one offline probe dataset.
The recorder implementations live in their respective projects. This script
only converts their per-prompt snapshots to a small common CPU format; it does
not run a model or manufacture control examples.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from pathlib import Path
from typing import Any
def _preparse_gpu() -> str:
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument("--gpu", default="0")
args, _ = parser.parse_known_args()
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
return str(args.gpu)
PHYSICAL_GPU = _preparse_gpu()
import numpy as np
import torch
ROLES = {
"self_forcing": {7: "early", 14: "middle", 22: "late", 29: "final"},
"causal_forcing": {7: "early", 14: "middle", 22: "late", 29: "final"},
"hy_worldplay": {13: "early", 26: "middle", 40: "late", 53: "final"},
}
def regular_coords(frames: int = 3, height: int = 30, width: int = 52, max_tokens: int = 240):
total = frames * height * width
if total <= max_tokens:
flat = np.arange(total, dtype=np.int64)
else:
per_frame = max(1, max_tokens // frames)
h_count = min(height, max(1, int(round((per_frame * height / width) ** 0.5))))
w_count = min(width, max(1, per_frame // h_count))
while frames * h_count * w_count > max_tokens and w_count > 1:
w_count -= 1
while frames * h_count * w_count > max_tokens and h_count > 1:
h_count -= 1
hs = np.unique(np.rint(np.linspace(0, height - 1, h_count)).astype(np.int64))
ws = np.unique(np.rint(np.linspace(0, width - 1, w_count)).astype(np.int64))
flat = np.asarray(
[t * height * width + h * width + w for t in range(frames) for h in hs for w in ws],
dtype=np.int64,
)
t = flat // (height * width)
rem = flat % (height * width)
return np.stack([t, rem // width, rem % width], axis=1)
def ensure_stack(values: dict[tuple[int, int], torch.Tensor], layer: int, chunks: int, steps: int):
rows = []
for chunk in range(chunks):
step_rows = []
for step in range(steps):
key = (chunk, step)
if key not in values:
raise ValueError(f"Missing layer={layer} chunk={chunk} step={step}")
step_rows.append(values[key].detach().cpu().to(torch.float16))
rows.append(torch.stack(step_rows, dim=0))
return torch.stack(rows, dim=0).contiguous()
def load_self(path: Path, layers: list[int], chunks: int, steps: int) -> dict[str, Any]:
run = torch.load(path, map_location="cpu", weights_only=False)
features = {}
for layer in layers:
stage = f"block_{layer}_hidden"
values = {}
for key, value in run["records"][stage].items():
c, s = (int(part) for part in key.split(":"))
if c < chunks and s < steps:
values[(c, s)] = value
features[ROLES["self_forcing"][layer]] = ensure_stack(values, layer, chunks, steps)
return {
"prompt_id": int(run["run_index"]),
"prompt": run["prompt"],
"seed": int(run["seed"]),
"model_family": "self_forcing",
"model_variant": "dmd4",
"features": features,
"timesteps": np.asarray([1000.0, 937.5, 833.3333, 625.0], dtype=np.float32),
"coords": regular_coords(),
}
def load_causal(path: Path, layers: list[int], chunks: int, steps: int) -> dict[str, Any]:
run = torch.load(path, map_location="cpu", weights_only=False)
raw = {}
for key, value in run["features"].items():
layer, chunk, step = (int(part) for part in key.split(":"))
if layer in layers and chunk < chunks and step < steps:
raw.setdefault(layer, {})[(chunk, step)] = value
features = {
ROLES["causal_forcing"][layer]: ensure_stack(raw.get(layer, {}), layer, chunks, steps)
for layer in layers
}
return {
"prompt_id": int(run["prompt_id"]),
"prompt": run["prompt"],
"seed": int(run["seed"]),
"model_family": "causal_forcing",
"model_variant": "dmd4",
"features": features,
"timesteps": np.asarray([1000.0, 937.5, 833.3333, 625.0], dtype=np.float32),
"coords": regular_coords(),
}
def load_hy(path: Path, layers: list[int], chunks: int, steps: int) -> dict[str, Any]:
data = np.load(path, allow_pickle=False)
stages = [str(value) for value in data["stages"]]
raw = {}
for index, stage in enumerate(stages):
if not stage.startswith("block_"):
continue
layer = int(stage.split("_")[-1])
chunk = int(data["chunks"][index])
step = int(data["steps"][index])
if layer in layers and chunk < chunks and step < steps:
raw.setdefault(layer, {})[(chunk, step)] = torch.from_numpy(data["features"][index])
features = {
ROLES["hy_worldplay"][layer]: ensure_stack(raw.get(layer, {}), layer, chunks, steps)
for layer in layers
}
return {
"features": features,
"timesteps": np.asarray(data["timesteps"], dtype=np.float32),
"coords": np.asarray(data["coords"], dtype=np.int64),
}
def atomic_save(path: Path, value: dict[str, Any]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
temporary = path.with_suffix(path.suffix + ".tmp")
torch.save(value, temporary)
os.replace(temporary, path)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--self_root", type=Path, required=True)
parser.add_argument("--causal_root", type=Path, required=True)
parser.add_argument("--hy_root", type=Path, required=True)
parser.add_argument("--output_root", type=Path, required=True)
parser.add_argument("--chunks", type=int, default=4)
parser.add_argument("--steps", type=int, default=4)
parser.add_argument("--max_prompts", type=int, default=10)
return parser.parse_args()
def main() -> None:
args = parse_args()
args.output_root.mkdir(parents=True, exist_ok=True)
specs = {
"self_forcing": ([7, 14, 22, 29], args.self_root / "runs", "self"),
"causal_forcing": ([7, 14, 22, 29], args.causal_root / "runs", "causal"),
}
inventory = []
for family, (layers, run_root, prefix) in specs.items():
out_dir = args.output_root / family
out_dir.mkdir(parents=True, exist_ok=True)
for prompt_id in range(args.max_prompts):
if family == "self_forcing":
source = run_root / f"prompt_{prompt_id:02d}.pt"
if not source.exists():
raise FileNotFoundError(source)
item = load_self(source, layers, args.chunks, args.steps)
else:
source = run_root / f"prompt_{prompt_id:04d}" / "feature_snapshots.pt"
if not source.exists():
raise FileNotFoundError(source)
item = load_causal(source, layers, args.chunks, args.steps)
destination = out_dir / f"prompt_{prompt_id:04d}.pt"
atomic_save(destination, item)
inventory.append({
"family": family,
"prompt_id": prompt_id,
"path": str(destination),
"bytes": destination.stat().st_size,
"roles": sorted(item["features"]),
})
hy_files = sorted(args.hy_root.glob("shard_gpu*/runs/prompt_*/forward/final_hidden_snapshots.npz"))
hy_by_prompt = {}
for source in hy_files:
prompt_id = int(source.parts[-3].split("_")[-1])
if prompt_id < args.max_prompts:
hy_by_prompt[prompt_id] = source
out_dir = args.output_root / "hy_worldplay"
out_dir.mkdir(parents=True, exist_ok=True)
for prompt_id in range(args.max_prompts):
source = hy_by_prompt.get(prompt_id)
if source is None:
raise FileNotFoundError(f"HY snapshot for prompt {prompt_id}")
item = load_hy(source, [13, 26, 40, 53], args.chunks, args.steps)
item.update({
"prompt_id": prompt_id,
"model_family": "hy_worldplay",
"model_variant": "ar4",
"seed": 0,
"prompt": f"prompt_{prompt_id:04d}",
})
destination = out_dir / f"prompt_{prompt_id:04d}.pt"
atomic_save(destination, item)
inventory.append({
"family": "hy_worldplay",
"prompt_id": prompt_id,
"path": str(destination),
"bytes": destination.stat().st_size,
"roles": sorted(item["features"]),
})
manifest = {
"dataset_version": 1,
"prompt_ids": list(range(args.max_prompts)),
"chunks": args.chunks,
"steps": args.steps,
"max_tokens": 240,
"roles": ["early", "middle", "late", "final"],
"source_roots": {
"self_forcing": str(args.self_root),
"causal_forcing": str(args.causal_root),
"hy_worldplay": str(args.hy_root),
},
"inventory": inventory,
}
(args.output_root / "manifest.json").write_text(
json.dumps(manifest, indent=2, ensure_ascii=False) + "\n", encoding="utf-8"
)
print(f"[complete] {args.output_root} prompts={args.max_prompts} files={len(inventory)}", flush=True)
if __name__ == "__main__":
main()
|