🐛 Bug

Information

Model I am using (Bert, XLNet ...): student-4L-312

Language I am using the model on (English, Chinese ...):English

The problem arises when using:

• [x] the official example scripts: (give details below)
• [ ] my own modified scripts: (give details below)

The tasks I am working on is:

• [x] an official GLUE/SQUaD task: (give the name)
• [ ] my own task or dataset: (give details below)

To reproduce

Steps to reproduce the behavior:

1. run the command below python examples/fastformers/run_superglue.py --model_type bert --model_name_or_path ../model/fastformers/student_model/student-4L-312 --task_name BoolQ --output_dir ./out --do_eval --data_dir ../dataset/fastformers/BoolQ --per_instance_eval_batch_size 1 --do_lower_case --max_seq_length 512 --use_onnxrt --no_cuda

error message:

Traceback (most recent call last):
  File "examples/fastformers/run_superglue.py", line 1901, in <module>
    main()
  File "examples/fastformers/run_superglue.py", line 1840, in main
    from onnxruntime import ExecutionMode, InferenceSession, SessionOptions
  File "/home/username/.local/lib/python3.6/site-packages/onnxruntime/__init__.py", line 13, in <module>
    from onnxruntime.capi._pybind_state import get_all_providers, get_available_providers, get_device, set_seed, \
ImportError: cannot import name 'get_all_providers'

Expected behavior

Environment info

• transformers version: transformers (2.11.0)
• Platform: ubuntu16.04
• Python version: 3.6
• PyTorch version (GPU?): torch (1.7.0+cpu)
• Tensorflow version (GPU?): null
• Using GPU in script?: No
• Using distributed or parallel set-up in script?: No
• onnxruntime (1.4.0)

Hello!

I was reading your paper and was looking in the HF repo and found https://github.com/huggingface/transformers/issues/8083 where it appeared that you were discussing adding your functionality to their library, however that never happened, so I am curious if you discovered something that prohibited adding this functionality. Thanks!

❓ Questions & Help

I am trying to convert the Roberta-large model to Fastformers. I am facing this issue with data files after preprocessing

Details

runcate_sequences assert len(ids) > num_tokens_to_remove AssertionError

what did lead me to this error A link to original question on Stack Overflow:

model is bert_base When i do prune, set num_heads to 8. Before prune, shape of key(value) weight is [768, 768] After prune, shape of key(value) weight became [512, 768]

so, saved weights can't be loaded by transformers

Fastformers is already impressive as is but a new paper has just been released: ZORB And it aims to become an alternative to... Backpropagation (you read this right)

Such a breakthrough allow ~300X additional speedup to fastformers (even more in theory) while only diminishing accuracy of a few percents on many cases and actually apparently outperforming BP with a lower error count? Anyway it's a huge performance breakthrough and should be popularized by fastformers and huggingface.

https://paperswithcode.com/paper/zorb-a-derivative-free-backpropagation Notes: Extensive testing of the variable accuracy loss would be welcome. Some activations functions needs to be adapted? (e.g Mish?)

How to optimize an already fine-tuned model from Hugging Face?

Congratulations on the work, it looks amazing 😊

Details

If there is an already fine-tuned model from Hugging Face for, let's say, generating question-answer pairs such as valhalla/t5-base-qa-qg-hl, how could it be further optimized for inference using your method? I'm a bit lost

Thank you in advance!

The README mentions Intel cpus are required because of the necessity of AVX256 support.

First of all AMD cpu supports AVX 256 since a Long time (jaguar which predate zen). True AVX 256 support (not only being compatible but being twice as fast as AVX 128) came one year ago with ZEN 2 cpus.

Zen 3 cpus are now being released and are the fastest cpus in the world and any Deep learning researchers should have them and be compatible with them, period.

Hello and so happy to see you use Pytorch-Lightning! :tada: Just wondering if you already heard about quite the new Pytorch Lightning (PL) ecosystem CI where we would like to invite you to... You can check out our blog post about it: Stay Ahead of Breaking Changes with the New Lightning Ecosystem CI :zap: As you use PL framework for your cool project, we would like to enhance your experience and offer you safe updates to our future releases. At this moment, you run tests with a particular PL version, but it may accidentally happen that the next version will be incompatible with your project... :confused: We do not intend to change anything on our project side, but still here we have a solution - ecosystem CI with testing both - your and our latest development head we can find it very early and prevent releasing eventually bad version... :+1:

What is needed to do?

• have some tests, including PL integration
• add config to ecosystem CI - https://github.com/PyTorchLightning/ecosystem-ci

What will you get?

• scheduled nightly testing configured for development/stable versions
• slack notification if something went wrong to investigate
• testing also on multi-GPU machine as our gift to you :rabbit:

cc: @borda

❓ Questions & Help

Details

Hello again @ykim362,

I'm trying to reproduce your distillation results from Section 2 of the FastFormers paper and I have a few questions I was hoping you could help with:

1. Did you use the weights provided in the TinyBERT repo (link) or those provided by Huawei in the HuggingFace model hub (link)?
2. Did you use General_TinyBERT(Nlayer-Ddim) or General_TinyBERT_v2(Nlayer-Ddim)?
3. I noticed that the Huawei models on the HuggingFace hub do not appear to be compatible with the Transformers library, so e.g. I get errors like the following:

>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("huawei-noah/TinyBERT_General_4L_312D")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/lewtun/git/transformers/src/transformers/models/auto/tokenization_auto.py", line 345, in from_pretrained
    config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
  File "/Users/lewtun/git/transformers/src/transformers/models/auto/configuration_auto.py", line 360, in from_pretrained
    raise ValueError(
ValueError: Unrecognized model in huawei-noah/TinyBERT_General_4L_312D. Should have a `model_type` key in its config.json, or contain one of the following strings in its name: retribert, mt5, t5, mobilebert, distilbert, albert, bert-generation, camembert, xlm-roberta, pegasus, marian, mbart, mpnet, bart, blenderbot, reformer, longformer, roberta, deberta, flaubert, fsmt, squeezebert, bert, openai-gpt, gpt2, transfo-xl, xlnet, xlm-prophetnet, prophetnet, xlm, ctrl, electra, encoder-decoder, funnel, lxmert, dpr, layoutlm, rag, tapas

Did you have to do something special to load TinyBERT in your FastFormers experiments? Looking at your source code (link) it seems you use the standard from_pretrained methods of the Transformers library, so I'm curious whether you encountered the same problem. 4. Did you use the data augmentation technique from TinyBERT (i.e. combine BERT with GloVe word embeddings) in your experiments? Looking at your codebase, I could not see this being used, but just want to double-check since it appears to play an important role in the TinyBERT paper. 5. Finally, what values of state_loss_ratio and att_loss_ratio did you use to generate the distilled model in Table 3 of your paper?

For reference, I am not working directly from the fastformers repo, so have the following dependencies:

- `transformers` version: 4.0.0-rc-1
- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.6.0 (True)
- Tensorflow version (GPU?): 2.3.0 (True)
- Using GPU in script?: (True)
- Using distributed or parallel set-up in script?: None

Thank you!

❓ Questions & Help

Details

Hello,

First of all, thank you very much for open-sourcing this research - I expect it will have a large impact on helping bring Transformers to production!

I have a question about the results in Table 3 of your paper.

Is the distilled model with (4L, 312) obtained from task-agnostic or task-specific distillation? In Section 2 you state

Since we are experimenting with various NLU tasks, the capacity of the optimal student model that preserves accuracy may vary with varying level of task’s difficulty. Therefore, we experiment with distilling various sized student models; then, we pick the smaller model among the distilled models that can offer higher accuracy than the original BERT model for each task.

and I could not tell from the codebase which approach you used to generate the numbers on Table 3.

Thank you!

🐛 Bug

Information

Model I am using (Bert, XLNet ...): BERT

Language I am using the model on (English, Chinese ...): English

The problem arises when using:

• [x] the official example scripts: (give details below)

The tasks I am working on is:

• [x] an official GLUE/SQUaD task: (give the name)

To reproduce

Steps to reproduce the behavior:

1. run the command below

python examples\fastformers\run_superglue.py --model_type bert --model_name_or_path ..\model\fastformers\teacher_model\teacher-bert-base  --task_name BoolQ --output_dir .\out --do_eval  --data_dir ..\dataset\fastformers\BoolQ --per_instance_eval_batch_size 1 --use_fixed_seq_length --do_lower_case --max_seq_length 512 --no_cuda

Expected behavior

get error below:

FileNotFoundError: [Errno 2] No such file or directory: '..\dataset\fastformers\BoolQ\tensors_dev_..\model\fastformers\teacher_model\teacher-bert-base_512_boolq_True'

Environment info

• transformers version: 2.11.0
• Platform: windows
• Python version: 3.6
• PyTorch version (GPU?): 1.7.0+cpu
• Tensorflow version (GPU?): None
• Using GPU in script?: No
• Using distributed or parallel set-up in script?: No