opened 06:21AM - 27 Apr 23 UTC
closed 03:03PM - 05 Jun 23 UTC
### System Info
- `transformers` version: 4.28.0
- Platform: Linux-5.4.0-122-g…eneric-x86_64-with-glibc2.31
- Python version: 3.9.12
- Huggingface_hub version: 0.13.4
- Safetensors version: not installed
- PyTorch version (GPU?): 1.13.1+cu117 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: yes
### Who can help?
@ArthurZucker @younesbelkada
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)
### Reproduction
I retrained Roberta on my own corpus with the MLM task. I set `model.gradient_checkpointing_enable()` to save memory.
```python
model = RobertaModel.from_pretrained(model_name_or_path,config=config)
model.gradient_checkpointing_enable() # Activate gradient checkpointing
model = Model(model,config,tokenizer,args)
```
My model:
```python
class Model(nn.Module):
def __init__(self, model,config,tokenizer,args):
super(Model, self).__init__()
self.encoder = model
self.config = config
self.tokenizer = tokenizer
self.args = args
self.lm_head = nn.Linear(config.hidden_size,config.vocab_size)
self.lm_head.weight = self.encoder.embeddings.word_embeddings.weight
self.register_buffer(
"bias", torch.tril(torch.ones((args.block_size, args.block_size), dtype=torch.uint8)).view(1, args.block_size, args.block_size)
)
def forward(self, mlm_ids):
...
```
There is an error:
```
RuntimeError: Expected to mark a variable ready only once. This error is caused by one of the following reasons: 1) Use of a module parame
ter outside the `forward` function. Please make sure model parameters are not shared across multiple concurrent forward-backward passes. o
r try to use _set_static_graph() as a workaround if this module graph does not change during training loop.2) Reused parameters in multipl
e reentrant backward passes. For example, if you use multiple `checkpoint` functions to wrap the same part of your model, it would result
in the same set of parameters been used by different reentrant backward passes multiple times, and hence marking a variable ready multiple
times. DDP does not support such use cases in default. You can try to use _set_static_graph() as a workaround if your module graph does n
ot change over iterations.
Parameter at index 195 with name encoder.encoder.layer.11.output.LayerNorm.weight has been marked as ready twice. This means that multiple
autograd engine hooks have fired for this particular parameter during this iteration.
```
If I get rid of this line of code：`model.gradient_checkpointing_enable()`, it is ok. Why?
### Expected behavior
I want to pre-train with `gradient_checkpointing`.