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import os
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import torch
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import argparse
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import glob
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parser = argparse.ArgumentParser(description='Pruning')
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parser.add_argument('--ckpt', type=str, default=None, help='path to model ckpt')
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args = parser.parse_args()
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ckpt = args.ckpt
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def prune_it(p, keep_only_ema=False):
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print(f"prunin' in path: {p}")
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size_initial = os.path.getsize(p)
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nsd = dict()
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sd = torch.load(p, map_location="cpu")
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print(sd.keys())
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for k in sd.keys():
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if k != "optimizer_states":
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nsd[k] = sd[k]
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else:
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print(f"removing optimizer states for path {p}")
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if "global_step" in sd:
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print(f"This is global step {sd['global_step']}.")
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if keep_only_ema:
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sd = nsd["state_dict"].copy()
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ema_keys = {k: "model_ema." + k[6:].replace(".", ".") for k in sd.keys() if k.startswith("model.")}
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new_sd = dict()
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for k in sd:
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if k in ema_keys:
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new_sd[k] = sd[ema_keys[k]].half()
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elif not k.startswith("model_ema.") or k in ["model_ema.num_updates", "model_ema.decay"]:
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new_sd[k] = sd[k].half()
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assert len(new_sd) == len(sd) - len(ema_keys)
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nsd["state_dict"] = new_sd
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else:
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sd = nsd['state_dict'].copy()
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new_sd = dict()
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for k in sd:
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new_sd[k] = sd[k].half()
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nsd['state_dict'] = new_sd
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fn = f"{os.path.splitext(p)[0]}-pruned.ckpt" if not keep_only_ema else f"{os.path.splitext(p)[0]}-ema-pruned.ckpt"
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print(f"saving pruned checkpoint at: {fn}")
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torch.save(nsd, fn)
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newsize = os.path.getsize(fn)
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MSG = f"New ckpt size: {newsize*1e-9:.2f} GB. " + \
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f"Saved {(size_initial - newsize)*1e-9:.2f} GB by removing optimizer states"
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if keep_only_ema:
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MSG += " and non-EMA weights"
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print(MSG)
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if __name__ == "__main__":
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prune_it(ckpt) |
