🚀 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.

🚀 Feature

A way to print to terminal without breaking up the progress bar.

Motivation

A lot of people print stuff to terminal while training/validating/testing, and currently a simple call to print() will break the progress bar. A way to get around this is to set up a custom progress bar, with methods for calling tqdm.write, and passing that as a callback to the trainer. However, this

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

Current pytorch implementation ignores the argument split_f in the function train_batch_ch13 as shown below.

def train_batch_ch13(net, X, y, loss, trainer, devices):
    if isinstance(X, list):
        # Required for BERT Fine-tuning (to be covered later)
        X = [x.to(devices[0]) for x in X]
    else:
        X = X.to(devices[0])
...

Todo: Define the argument `

Is it possible to run this on a (recent) Mac, which does not support CUDA? I would have guessed setting --GPU 0 would not attempt to call CUDA, but it fails.

File "/Users/../Desktop/bopbtl/venv/lib/python3.7/site-packages/torch/cuda/__init__.py", line 61, in _check_driver
    raise AssertionError("Torch not compiled with CUDA enabled")
AssertionError: Torch not compiled with CUDA enable