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import os
import yaml
import torch
from transformers import AlbertConfig, AlbertModel
class CustomAlbert(AlbertModel):
def forward(self, *args, **kwargs):
# Call the original forward method
outputs = super().forward(*args, **kwargs)
# Only return the last_hidden_state
return outputs.last_hidden_state
def load_plbert(log_dir):
config_path = os.path.join(log_dir, "config.yml")
plbert_config = yaml.safe_load(open(config_path))
albert_base_configuration = AlbertConfig(**plbert_config["model_params"])
bert = CustomAlbert(albert_base_configuration)
files = os.listdir(log_dir)
ckpts = []
for f in os.listdir(log_dir):
if f.startswith("step_"):
ckpts.append(f)
iters = [
int(f.split("_")[-1].split(".")[0])
for f in ckpts
if os.path.isfile(os.path.join(log_dir, f))
]
iters = sorted(iters)[-1]
checkpoint = torch.load(log_dir + "/step_" + str(iters) + ".t7", map_location="cpu")
state_dict = checkpoint["net"]
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
if name.startswith("encoder."):
name = name[8:] # remove `encoder.`
new_state_dict[name] = v
del new_state_dict["embeddings.position_ids"]
bert.load_state_dict(new_state_dict, strict=False)
return bert
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