File size: 3,041 Bytes
bf7656a | 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 | #!/usr/bin/env python
"""
Aether Phase-1 PRE-EXTRACT: run the frozen encoders ONCE, cache their features to disk.
This is the portability enabler — after this, alignment/adapters train on the cached
tensors with NO encoders loaded (fits 6900XT / Kaggle-T4 / Colab-T4 / any small GPU).
Writes /work/slivers_cache/<modality>/<id>.pt (raw encoder feature, pre-projection)
+ /work/slivers_cache/index.jsonl ({id, modality, caption, feat_path, shape})
Encoders live only here (the big box); the cache travels.
Run on the rental MI300 (has all 3 encoders). Validated on a tiny mixed set.
"""
import os, sys, json, torch
sys.path.insert(0,"/work")
import forward as F
dev=F.dev
CACHE="/work/slivers_cache"
def main():
torch.set_grad_enabled(False)
m=F.AetherPhase1().to(dev); m.eval()
os.makedirs(CACHE, exist_ok=True)
idx=[]
torch.manual_seed(42)
# ---- VISION items (image -> caption) ----
os.makedirs(f"{CACHE}/vision", exist_ok=True)
vcaps=["a red brick electrical panel on a wall","a blue ceramic coffee mug on a desk",
"three yellow pencils in a glass jar","a green circuit board with silver traces"]
for i,cap in enumerate(vcaps):
px=torch.randn(1,3,448,448) # (real pipeline: load actual Tier-A image)
feat=m.enc_vision(px).cpu() # (1025,3200) frozen features
p=f"{CACHE}/vision/v{i}.pt"; torch.save(feat,p)
idx.append({"id":f"v{i}","modality":"vision","caption":cap,"feat":p,"shape":list(feat.shape)})
# ---- AUDIO items (speech -> transcript) ----
os.makedirs(f"{CACHE}/audio", exist_ok=True)
acaps=["the meeting starts at nine tomorrow","turn off the main breaker first"]
for i,cap in enumerate(acaps):
mel=torch.randn(400,128); alen=torch.tensor([400])
feat=m.enc_audio(mel,alen).cpu() # (~100,1280)
p=f"{CACHE}/audio/a{i}.pt"; torch.save(feat,p)
idx.append({"id":f"a{i}","modality":"audio","caption":cap,"feat":p,"shape":list(feat.shape)})
# ---- 3D geometry items (mesh -> caption); pre-extract SLAT latent + voxel coords ----
os.makedirs(f"{CACHE}/geom", exist_ok=True)
gcaps=["a low-poly wooden crate","a cylindrical steel conduit fitting"]
for i,cap in enumerate(gcaps):
M=300; vz=torch.randint(0,64,(M,3)); b=torch.zeros(M,1)
coords=torch.cat([b,vz.flip(-1).float()],1); feats=torch.randn(M,1024)
gf,gc=m.enc_geom(coords,feats) # (M',8),(M',4)
gcz=(gc[:,[1,2,3]].float()/gc[:,[1,2,3]].float().max().clamp(min=1)*15).long()
p=f"{CACHE}/geom/g{i}.pt"; torch.save({"feat":gf.cpu(),"xyz":gcz.cpu()},p)
idx.append({"id":f"g{i}","modality":"geom","caption":cap,"feat":p,"shape":list(gf.shape)})
json.dump(idx, open(f"{CACHE}/index.jsonl","w"), indent=2)
print(f"PRE-EXTRACT OK: {len(idx)} items cached to {CACHE}")
for r in idx: print(" ",r["id"],r["modality"],r["shape"],"->",os.path.getsize(r["feat"])//1024,"KB")
if __name__=="__main__":
main()
|