Environment info

• transformers version: 4.11.2
• Platform: Linux-5.4.0-66-generic-x86_64-with-glibc2.10
• Python version: 3.8.8
• PyTorch version (GPU?): 1.9.1+cu102 (True)
• Tensorflow version (GPU?): not installed (NA)
• F

Proposed refactoring or deprecation

Current names:

pytorch_lightning/plugins/precision/
├── apex_amp.py
├── deepspeed_precision.py
├── double.py
├── fully_sharded_native_amp.py
├── ipu_precision.py
├── mixed.py
├── native_amp.py
├── precision_plugin.py
├── sharded_native_amp.py
├── tpu.py
└── tpu_bfloat.py

Motivation

Had to choose this when working on

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