text_classification: registry + configs + prompts + assemble loader for 70 re-collected cells (token embedding frozen)
11d8066 | """Reassemble a full text-classification state_dict from a ParaGen-Bench pool checkpoint and its base checkpoint. | |
| Pool files (ckpt/text_classification/<arch>__<dataset>/<model_id>/collect_XXX.pth) omit the vocabulary token-embedding | |
| matrix, which was frozen during collection and is therefore identical to the base model's. It is stored once, in the | |
| base checkpoint zoo/<dataset>/<arch>/<model_id>.pth. Every pool file lists the omitted keys under "frozen_keys". | |
| from assemble import assemble | |
| sd = assemble("zoo/agnews/bertbase/bertbase_010.pth", | |
| "ckpt/text_classification/bertbase__agnews/bertbase_010/collect_000.pth") | |
| model.load_state_dict(sd, strict=True) | |
| """ | |
| import torch | |
| def assemble(base_ckpt_path, pool_ckpt_path, map_location="cpu"): | |
| """Return the full model state_dict: pool weights + the frozen keys copied from the base's model_state_dict.""" | |
| pool = torch.load(pool_ckpt_path, map_location=map_location, weights_only=False) | |
| base = torch.load(base_ckpt_path, map_location=map_location, weights_only=False) | |
| sd = dict(pool["model_state_dict"]) | |
| base_sd = base["model_state_dict"] | |
| for k in pool["frozen_keys"]: | |
| if k in sd: | |
| raise ValueError(f"frozen key {k!r} is unexpectedly present in the pool file") | |
| if k not in base_sd: | |
| raise KeyError(f"frozen key {k!r} not found in the base checkpoint {base_ckpt_path}") | |
| sd[k] = base_sd[k].to(torch.float32) | |
| return sd | |
| if __name__ == "__main__": | |
| import sys | |
| if len(sys.argv) != 3: | |
| raise SystemExit("usage: python assemble.py <base_ckpt.pth> <collect_XXX.pth>") | |
| sd = assemble(sys.argv[1], sys.argv[2]) | |
| print(f"{len(sd)} tensors, {sum(v.numel() for v in sd.values()):,} values") | |