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stas00 commented Jun 12, 2021

Let's use this Issue to track performance issues and enhancement requests, so it's easier to prioritize the work.

This is for pytorch transformers

Also I will tag it as a Second Good Issue in case someone is ready for a challenging but rewarding experience of figuring things out. If you do want to take the challenge comment in the corresponding Issue/PR that resonates with you so other

hellock commented Jun 7, 2020

We keep this issue open to collect feature requests from users and hear your voice. Our monthly release plan is also available here.

You can either:

  1. Suggest a new feature by leaving a comment.
  2. Vote for a feature request with 👍 or be against with 👎. (Remember that developers are busy and cannot respond to all feature requests, so vote for your most favorable one!)
  3. Tell us that
carmocca commented Jun 10, 2021

🐛 Bug

If accumulate_grad_batches is enabled, we don't call on_after_backward until we step the optimizers

This means on_after_backward is acting like on_before_optimizer_step.

So we should add that and always run `on_after_b

askhade commented May 27, 2021

Bug Report

Is the issue related to model conversion? No

Describe the bug

DynamicQuantizeLinear function op does not have shape inference function defined. In absence of shape inference, function body is used to get the shape inference for the function op and although it works as a fallback option it hurts perf.

Expected behavior

Add shape inference function for DynamicQuan

danieldeutsch commented Jun 2, 2021

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.


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