File size: 5,938 Bytes
2a9189f | 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 | """Load the IWM inference-only safetensors release; never loads private data.
Use with the source checkout pinned in manifest.json. No training is dispatched.
"""
import hashlib
import json
from pathlib import Path
import sys
import torch
from safetensors.torch import load_file
SOURCE_COMMIT = "516a71c252ff8f895dcd036bb343c996254c7a76"
BANK = ("feature_mean", "feature_std", "coefficient", "action_mean",
"expert_weight", "expert_mean", "residual_std")
GUIDE = ("probe_mean", "probe_std", "probe_weight", "probe_bias",
"class_centers", "within_std", "relation_keys", "relation_labels", "relation_ids")
EXCLUDED = ("prototypes", "amplitude_limit", "variance") + tuple("guide_" + k for k in GUIDE)
def digest(path):
h = hashlib.sha256()
with Path(path).open("rb") as f:
for block in iter(lambda: f.read(8 * 1024**2), b""):
h.update(block)
return h.hexdigest()
def source_paths(source_root):
root = Path(source_root).resolve()
archive = root / "experiments/videorae3d"
paths = [archive / "cg2_dualcontext", archive / "cg2_ng",
archive / "eeg", archive / "teacher", archive, root / "src", root / "scripts"]
if not all(p.is_dir() for p in paths):
raise ValueError("Use the complete pinned intention-world-model source checkout")
for path in reversed(paths):
if str(path) not in sys.path:
sys.path.insert(0, str(path))
return archive
def prepared_from_state(state, prefix="", include_training=False):
"""Constructor adapter, not a replacement for private training preparation."""
normal = {"mean": state[prefix + "normal_mean"], "std": state[prefix + "normal_std"]}
bank = {k: state[prefix + k] for k in BANK}
if include_training:
bank.update({k: state[prefix + k] for k in ("prototypes", "amplitude_limit", "variance")})
guide = {k: state[prefix + "guide_" + k] for k in GUIDE}
else:
# The archived constructor registers training-only buffers. These empty
# placeholders are deleted before strict loading and never used in forward.
guide = dict(probe_mean=torch.zeros(256), probe_std=torch.ones(256),
probe_weight=torch.zeros(5, 256), probe_bias=torch.zeros(5),
class_centers=torch.zeros(5, 256), within_std=torch.ones(256),
relation_keys=torch.empty(0, 256),
relation_labels=torch.empty(0, dtype=torch.long),
relation_ids=torch.empty(0, dtype=torch.long))
return dict(normalizers={"target": normal}, bank=bank, classguide=guide)
def build_model(config, state, source_root, original=False):
# safetensors stores contiguous values. Preserve archived dense buffer layout
# too: a transposed ridge matrix otherwise selects a different BLAS path.
state = dict(state)
for name, strides in config.get("tensor_layouts", {}).items():
t = state[name]
if len(strides) != t.ndim or any(type(x) is not int or x <= 0 for x in strides):
raise ValueError("Invalid tensor layout")
axes = sorted(zip(t.shape, strides), key=lambda pair: pair[1])
span = 1
for size, stride in axes:
if size > 1:
if stride != span:
raise ValueError("Only dense, non-overlapping layouts are supported")
span *= size
if tuple(t.stride()) != tuple(strides):
state[name] = torch.empty_strided(t.shape, strides, dtype=t.dtype).copy_(t)
source_paths(source_root)
kind = config["architecture"]
if kind == "Teacher3D":
from model import Teacher3D
m = Teacher3D()
else:
if kind not in ("CG2-NG", "NG-D"):
raise ValueError("Unsupported released architecture")
cfg = dict(config["model_config"])
if not (cfg["timing"] == "target" and cfg["method"] == "CG2"
and cfg["variant"] == "CG2-NG" and cfg["representation"] == "tokens"
and cfg["head"] == "continuous"):
raise ValueError("Unexpected model protocol")
prefix = "base." if kind == "NG-D" else ""
prepared = prepared_from_state(state, prefix, include_training=original)
if kind == "NG-D":
assert not cfg["past_aux"] and cfg["separate_motion"]
from dual_models import Controller
else:
from noise_models import Controller
m = Controller(cfg, prepared)
if not original:
base = m.base if kind == "NG-D" else m
for name in EXCLUDED:
if name in base._buffers:
delattr(base, name)
m.load_state_dict(state, strict=True)
return m.eval().requires_grad_(False)
def load_checkpoint(release_root, model_id, source_root, device="cpu", verify=True):
"""Return an eval-only Teacher3D or EEG controller.
Controllers take (z_current[B,1024,64], eeg_raw[B,32,500]) and return
(class_logits[B,5], action[B,1,256]). Zero is raw zero, before normalization.
Use teacher.fdm(z_current, action) for the predicted H1 latent.
Training losses/probes and optimizer-resume state are intentionally absent.
"""
root = Path(release_root).resolve()
manifest = json.loads((root / "manifest.json").read_text())
entry = next((r for r in manifest["models"] if r["id"] == model_id), None)
if entry is None:
raise ValueError("Unknown model_id; see manifest.json")
weights, config = root / entry["weights"], root / entry["config"]
if verify:
for p in (weights, config):
rel = p.relative_to(root).as_posix()
if digest(p) != manifest["files"][rel]["sha256"]:
raise ValueError("Release checksum mismatch: " + rel)
cfg = json.loads(config.read_text())
state = load_file(str(weights), device="cpu")
return build_model(cfg, state, source_root).to(device)
|