70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
# use tranformers as usual.
|
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
|
|
t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-large")
|
|
t5_tokenizer = AutoTokenizer.from_pretrained("t5-large")
|
|
# A running example
|
|
inputs_ids = t5_tokenizer.encode("Is Harry Poter wrtten by JKrowling", return_tensors="pt")
|
|
t5_tokenizer.decode(t5.generate(inputs_ids)[0])
|
|
# >>> '<pad><extra_id_0>? Is it Harry Potter?</s>'
|
|
|
|
|
|
# use existing delta models
|
|
from opendelta import AutoDeltaModel, AutoDeltaConfig
|
|
# use existing delta models from DeltaCenter
|
|
delta = AutoDeltaModel.from_finetuned("thunlp/Spelling_Correction_T5_LRAdapter_demo", backbone_model=t5)
|
|
# freeze the whole backbone model except the delta models.
|
|
delta.freeze_module()
|
|
# visualize the change
|
|
delta.log()
|
|
|
|
|
|
t5_tokenizer.decode(t5.generate(inputs_ids)[0])
|
|
# >>> <pad> Is Harry Potter written by JK Rowling?</s>
|
|
|
|
|
|
# Now save merely the delta models, not the whole backbone model, to tmp/
|
|
delta.save_finetuned(".tmp")
|
|
import os; os.listdir(".tmp")
|
|
# >>> The state dict size is 1.443 MB
|
|
# >>> We encourage users to push their final and public models to delta center to share them with the community!
|
|
|
|
|
|
# reload the model from local url and add it to pre-trained T5.
|
|
t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-large")
|
|
delta1 = AutoDeltaModel.from_finetuned(".tmp", backbone_model=t5)
|
|
import shutil; shutil.rmtree(".tmp") # don't forget to remove the tmp files.
|
|
t5_tokenizer.decode(t5.generate(inputs_ids)[0])
|
|
# >>> <pad> Is Harry Potter written by JK Rowling?</s>
|
|
|
|
# detach the delta models, the model returns to the unmodified status.
|
|
delta1.detach()
|
|
t5_tokenizer.decode(t5.generate(inputs_ids)[0])
|
|
# >>> '<pad><extra_id_0>? Is it Harry Potter?</s>'
|
|
|
|
# use default configuration for cunstomized wrapped models which have PLMs inside. This is a common need for users.
|
|
import torch.nn as nn
|
|
class WrappedModel(nn.Module):
|
|
def __init__(self, inner_model):
|
|
super().__init__()
|
|
self.inner = inner_model
|
|
def forward(self, *args, **kwargs):
|
|
return self.inner(*args, **kwargs)
|
|
|
|
wrapped_model = WrappedModel(WrappedModel(t5))
|
|
|
|
# say we use LoRA
|
|
delta_config = AutoDeltaConfig.from_dict({"delta_type":"lora"})
|
|
delta2 = AutoDeltaModel.from_config(delta_config, backbone_model=wrapped_model)
|
|
delta2.log()
|
|
# >>> root
|
|
# -- inner
|
|
# -- inner
|
|
# ...
|
|
# ... lora_A:[8,1024], lora_B:[1024,8]
|
|
delta2.detach()
|
|
|
|
# use a not default configuration
|
|
# say we add lora to the last four layer of the decoder of t5, with lora rank=5
|
|
delta_config3 = AutoDeltaConfig.from_dict({"delta_type":"lora", "modified_modules":["[r]decoder.*((20)|(21)|(22)|(23)).*DenseReluDense\.wi"], "lora_r":5})
|
|
delta3 = AutoDeltaModel.from_config(delta_config3, backbone_model=wrapped_model)
|
|
delta3.log() |