[tune](deps): Bump transformers from 4.9.1 to 4.11.2 in /python/requirements/tune

[dependabot]

Bumps transformers from 4.9.1 to 4.11.2.

Release notes

Sourced from transformers's releases.

v4.11.2: Patch release

Fix the Trainer API on TPU:

• Fix gather for TPU #13813

v4.11.1: Patch release

Patch release with a few bug fixes:

• [Wav2Vec2] Better error message (#13777)
• Fix LayoutLM ONNX test error (#13710)
• Fix warning for gradient_checkpointing (#13767)
• Implement len in IterableDatasetShard (#13780)
• Fix length of IterableDatasetShard and add test (#13792)

v4.11.0: GPT-J, Speech2Text2, FNet, Pipeline GPU utilization, dynamic model code loading

GPT-J

Three new models are released as part of the GPT-J implementation: GPTJModel, GPTJForCausalLM, GPTJForSequenceClassification, in PyTorch.

The GPT-J model was released in the kingoflolz/mesh-transformer-jax repository by Ben Wang and Aran Komatsuzaki. It is a GPT-2-like causal language model trained on the Pile dataset.

It was contributed by @​StellaAthena, @​kurumuz, @​EricHallahan, and @​leogao2.

• GPT-J-6B #13022 (@​StellaAthena)

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=gptj

SpeechEncoderDecoder & Speech2Text2

One new model is released as part of the Speech2Text2 implementation: Speech2Text2ForCausalLM, in PyTorch.

The Speech2Text2 model is used together with Wav2Vec2 for Speech Translation models proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.

Speech2Text2 is a decoder-only transformer model that can be used with any speech encoder-only, such as Wav2Vec2 or HuBERT for Speech-to-Text tasks. Please refer to the SpeechEncoderDecoder class on how to combine Speech2Text2 with any speech encoder-only model.

• Add SpeechEncoderDecoder & Speech2Text2 #13186 (@​patrickvonplaten)

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?other=speech2text2

FNet

Eight new models are released as part of the FNet implementation: FNetModel, FNetForPreTraining, FNetForMaskedLM, FNetForNextSentencePrediction, FNetForSequenceClassification, FNetForMultipleChoice, FNetForTokenClassification, FNetForQuestionAnswering, in PyTorch.

The FNet model was proposed in FNet: Mixing Tokens with Fourier Transforms by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon. The model replaces the self-attention layer in a BERT model with a fourier transform which returns only the real parts of the transform. The model is significantly faster than the BERT model because it has fewer parameters and is more memory efficient. The model achieves about 92-97% accuracy of BERT counterparts on GLUE benchmark, and trains much faster than the BERT model.

• Add FNet #13045 (@​gchhablani)

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?other=fnet

... (truncated)

Commits

• 7655f11 Release v4.11.2
• 6b87918 Fix gather for TPU (#13813)
• 54f9d62 Release v4.11.1
• 22d3156 Fix length of IterableDatasetShard and add test (#13792)
• 9bb3d33 Implement len in IterableDatasetShard (#13780)
• a05400e Fix warning for gradient_checkpointing (#13767)
• 1008324 Fix LayoutLM ONNX test error (#13710)
• 11144a3 up (#13777)
• dc193c9 Release: v4.11.0
• 1c96500 Fix gather for SageMaker model parallel
• Additional commits viewable in compare view

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[dependabot]

Superseded by #69.