48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
import os
|
|
import torch
|
|
import fire
|
|
|
|
|
|
def prune_it(p, keep_only_ema=False):
|
|
print(f"prunin' in path: {p}")
|
|
size_initial = os.path.getsize(p)
|
|
nsd = dict()
|
|
sd = torch.load(p, map_location="cpu")
|
|
print(sd.keys())
|
|
for k in sd.keys():
|
|
if k != "optimizer_states":
|
|
nsd[k] = sd[k]
|
|
else:
|
|
print(f"removing optimizer states for path {p}")
|
|
if "global_step" in sd:
|
|
print(f"This is global step {sd['global_step']}.")
|
|
if keep_only_ema:
|
|
sd = nsd["state_dict"].copy()
|
|
# infer ema keys
|
|
ema_keys = {k: "model_ema." + k[6:].replace(".", "") for k in sd.keys() if k.startswith("model.")}
|
|
new_sd = dict()
|
|
|
|
for k in sd:
|
|
if k in ema_keys:
|
|
new_sd[k] = sd[ema_keys[k]]
|
|
elif not k.startswith("model_ema.") or k in ["model_ema.num_updates", "model_ema.decay"]:
|
|
new_sd[k] = sd[k]
|
|
|
|
assert len(new_sd) == len(sd) - len(ema_keys)
|
|
nsd["state_dict"] = new_sd
|
|
|
|
fn = f"{os.path.splitext(p)[0]}-pruned.ckpt" if not keep_only_ema else f"{os.path.splitext(p)[0]}-ema-pruned.ckpt"
|
|
print(f"saving pruned checkpoint at: {fn}")
|
|
torch.save(nsd, fn)
|
|
newsize = os.path.getsize(fn)
|
|
MSG = f"New ckpt size: {newsize*1e-9:.2f} GB. " + \
|
|
f"Saved {(size_initial - newsize)*1e-9:.2f} GB by removing optimizer states"
|
|
if keep_only_ema:
|
|
MSG += " and non-EMA weights"
|
|
print(MSG)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
fire.Fire(prune_it)
|
|
print("done.")
|