🚀 Feature request

Bart is a seq2seq model, but there might be applications where one would like to use only the pre-trained BartDecoder in an EncoderDecoder setting with a "long" encoder, such as

from transformers import EncoderDecoderModel

model = EncoderDecoderModel("allenai/longformer-large-4096", "facebook/bart-large")

# fine-tune model ...

This is already p

some recordings have low volume so the output can be sometimes really quiet. how about we add a volume bar so we can make the output louder/quieter?

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

• APEX optimization levels are “O1, O2, O3” and not “01, 02, 03”. We should fix this in code + docs.

Make sure it is clear from the docs that the use of Apex anymore and recommend that users use upstream native AMP available since PyTorch 1.6

Bug Report

These tests were run on s390x. s390x is big-endian architecture.

Failure log for helper_test.py

________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________

self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>

    def test_make_tensor(self):  # type: () -> None
    

more details at: allenai/allennlp#2264 (comment)

What would you like to be added: As title

Why is this needed: All pruning schedule except AGPPruner only support level, L1, L2. While there are FPGM, APoZ, MeanActivation and Taylor, it would be much better if we can choose any pruner with any pruning schedule.

**Without this feature, how does current nni