Environment info

• transformers version: 4.11.3
• Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.2.5
• Python version: 3.8.12
• PyTorch version (GPU?): 1.9.1+cu102 (False)
• Tensorflow version (GPU?): not install

Dear all,

I hope to find you well. The current codebase is totally closed and it is impossible to retrieve all the metrics computed. Allow all metrics to be returned inside dataset.evaluate. For instance, currently, we cannot get the map per class that is printed inside [eval_map](https://github.com/open-mmlab/mmdetection/blob/414c62cb12d1b0ebf6151a697a32b84adc269ca4/mmdet/core/evaluati

🐛 Bug

Tensorboard logging metrics from different GPUs when using DataParallel training.

To Reproduce

Use any arbitrary toy model with DP as an accelerator and in training_step(), include the below line of code:

self.log(mode + '_loss', sum(losses), on_epoch=False, on_step=True, prog_bar=True, logger=True, sync_dist=True)

In the trainer, specify tensorboard logger with

Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

Bug Report

Describe the bug

An ONNX model with QuantizeLinear (opset 13) passes shape inference when it has no input for zero_point, but fails if the input is the empty string. Trailing optional inputs should be able to be expressed in either way.

onnx version 1.10.1

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.

Discussed in microsoft/nni#4070

[2021-08-14 10:13:41] INFO (NNIDataStore) Datastore initialization done
[2021-08-14 10:13:41] INFO (RestServer) RestServer start
[2021-08-14 10:13:41] INFO (RestServer) RestServer base port is 8080
[2021-08-14 10:13:41] I

🙌🙌🙌🙌🙌