huggingface/transformers#12276 introduced a new --log_level feature, which now allows users to set their desired log level via CLI or TrainingArguments.

run_translation.py was used as a "model" for other examples.

Now we need to replicate this to all other Trainer-based examples under examples/pytorch/, the 3 changes are

1. importing datasets
2. using `training

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Discussed in PyTorchLightning/pytorch-lightning#8042

class WarmupLRScheduler(torch.optim.lr_scheduler._LRScheduler):
    """
    Warmup learning rate until `total_steps`

    Args:
    

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

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

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.

Hi
Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq