Using Apex AMP with PyTorch optimizers causes Attribute Error

Hi. I’m facing some issues when I’m trying to use PyTorch optimizers with Apex AMP. My environment is:

  1. OS: Ubuntu 18.04.5
  2. Python: 3.8.5
  3. PyTorch: 1.7.1
  4. CUDA: 11.0
  5. Apex: 0.9.10.dev0
  6. Transformers: 4.3.3

You can reproduce my error with the following code:

from apex import amp
from transformers import AutoModel
from transformers.optimization import AdamW, get_linear_schedule_with_warmup

model = AutoModel.from_pretrained('bert-base-cased')
model = model.cuda()

new_layer = ["extractor", "bilinear"]
optimizer_grouped_parameters = [{"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in new_layer)], },{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in new_layer)], "lr": 1e-4},]
optimizer = AdamW(optimizer_grouped_parameters, lr=1e-5, eps=1e-10)

model, optimizer = amp.initialize(model, optimizer, opt_level="O1", verbosity=0)

scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=4000, num_training_steps=10000)

Commenting out the model, optimizer = amp.initialize line runs the code fine. However, running this script returns the following:

Traceback (most recent call last):

  File "../", line 33, in finetune
    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps)
  File "/home/User/anaconda3/lib/python3.8/site-packages/transformers/", line 98, in get_linear_schedule_with_warmup
    return LambdaLR(optimizer, lr_lambda, last_epoch)
  File "/home/User/anaconda3/lib/python3.8/site-packages/torch/optim/", line 205, in __init__
    super(LambdaLR, self).__init__(optimizer, last_epoch, verbose)
  File "/home/User/anaconda3/lib/python3.8/site-packages/torch/optim/", line 74, in __init__
    self.optimizer.step = with_counter(self.optimizer.step)
  File "/home/User/anaconda3/lib/python3.8/site-packages/torch/optim/", line 56, in with_counter
    instance_ref = weakref.ref(method.__self__)
AttributeError: 'function' object has no attribute '__self__'

What might the issue be, and how could I fix this? Thanks.

We recommend to use the native mixed-precision utility via torch.cuda.amp as described here. New features, such as the compatibility with 3rd party repositories (transformers in this case), won’t land in apex/amp, but in native amp instead.

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