CODEWITHSUNDEEP
pythonpytorchgpt-2openai-api
kewlzilla
kewlzilla

Reputation: 11

AttributeError: 'GPT2Model' object has no attribute 'gradient_checkpointing'

I am trying to load a GPT2 fine tuned model in flask initially. The model is being loaded during the init functions using:

app.modelgpt2 = torch.load('models/model_gpt2.pt', map_location=torch.device('cpu'))
app.modelgpt2tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

But while performing the prediction task as followed in the snippet below:

from flask import current_app
input_ids = current_app.modelgpt2tokenizer.encode("sample sentence here", return_tensors='pt')
sample_outputs = current_app.modelgpt2.generate(input_ids,
                                                do_sample=True,
                                                top_k=50,
                                                min_length=30,
                                                max_length=300,
                                                top_p=0.95,
                                                temperature=0.7,
                                                num_return_sequences=1)

It throws the following error as mentioned in the question: AttributeError: 'GPT2Model' object has no attribute 'gradient_checkpointing'

The error trace is listed starting from the model.generate function: File "/venv/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context return func(*args, **kwargs)

File "/venv/lib/python3.8/site-packages/transformers/generation_utils.py", line 1017, in generate return self.sample(

File "/venv/lib/python3.8/site-packages/transformers/generation_utils.py", line 1531, in sample outputs = self(

File "/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs)

File "/venv/lib/python3.8/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 1044, in forward transformer_outputs = self.transformer(

File "/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs)

File "/venv/lib/python3.8/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 861, in forward print(self.gradient_checkpointing)

File "/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1177, in getattr raise AttributeError("'{}' object has no attribute '{}'".format(

AttributeError: 'GPT2Model' object has no attribute 'gradient_checkpointing'

Checked with modeling_gpt2.py, by default self.gradient_checkpointing is set False in the constructor of the class.

Upvotes: 1

Views: 3294

Answers (2)

Nuwan Lasantha
Nuwan Lasantha

Reputation: 1

From HF:

Form the error message, it looks like you used torch.save to save your whole model (and not the weights), which is not recommended at all because when the model changes (like it did between 4.10 and 4.11) you then can’t reload it directly with torch.load.

Our advice is to always use save_pretrained/from_pretrained to save/load your models or if it’s not possible, to save the weights (model.state_dict) with torch.save and then reload them with model.load_state_dict, as this will works across different versions of the models.

Upvotes: 0

kewlzilla
kewlzilla

Reputation: 11

This issue is found to be occurring only if the framework is run using venv or deployment frameworks like uWSGI or gunicorn. It is resolved when transformers version 4.10.0 is used instead of the latest package.

Upvotes: 0

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