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[tune](deps): Bump transformers from 4.3.2 to 4.4.1 in /python/requirements #3

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@dependabot dependabot bot commented on behalf of github Mar 18, 2021

Bumps transformers from 4.3.2 to 4.4.1.

Release notes

Sourced from transformers's releases.

v4.4.0: S2T, M2M100, I-BERT, mBART-50, DeBERTa-v2, XLSR-Wav2Vec2

SpeechToText

Two new models are released as part of the S2T implementation: Speech2TextModel and Speech2TextForConditionalGeneration, in PyTorch.

Speech2Text is a speech model that accepts a float tensor of log-mel filter-bank features extracted from the speech signal. It’s a transformer-based seq2seq model, so the transcripts/translations are generated autoregressively.

The Speech2Text model was proposed in fairseq S2T: Fast Speech-to-Text Modeling with fairseq by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.

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

M2M100

Two new models are released as part of the M2M100 implementation: M2M100Model and M2M100ForConditionalGeneration, in PyTorch.

M2M100 is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation tasks.

The M2M100 model was proposed in Beyond English-Centric Multilingual Machine Translation by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=m2m_100

I-BERT

Six new models are released as part of the I-BERT implementation: IBertModel, IBertForMaskedLM, IBertForSequenceClassification, IBertForMultipleChoice, IBertForTokenClassification and IBertForQuestionAnswering, in PyTorch.

I-BERT is a quantized version of RoBERTa running inference up to four times faster.

The I-BERT framework in PyTorch allows to identify the best parameters for quantization. Once the model is exported in a framework that supports int8 execution (such as TensorRT), a speedup of up to 4x is visible, with no loss in performance thanks to the parameter search.

The I-BERT model was proposed in I-BERT: Integer-only BERT Quantization by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney and Kurt Keutzer.

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

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

mBART-50

MBart-50 is created using the original mbart-large-cc25 checkpoint by extending its embedding layers with randomly initialized vectors for an extra set of 25 language tokens and then pretrained on 50 languages.

The MBart model was presented in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.

... (truncated)

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@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Mar 18, 2021
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dependabot bot commented on behalf of github Mar 20, 2021

Superseded by #5.

@dependabot dependabot bot closed this Mar 20, 2021
@dependabot dependabot bot deleted the dependabot/pip/python/requirements/transformers-4.4.1 branch March 20, 2021 07:03
vakker pushed a commit that referenced this pull request Apr 26, 2022
…ray-project#23821)

This PR refactors `LazyBlockList` in service of out-of-band serialization (see [mono-PR](ray-project#22616)) and is a precursor to an execution plan refactor (PR #2) and adding the actual out-of-band serialization APIs (PR #3). The following is included in this refactor:
1. `ReadTask`s are now a first-class concept, replacing calls;
2. read stage progress tracking is consolidated into `LazyBlockList._get_blocks_with_metadta()` and more of the read task complexity, e.g. the read remote function, was pushed into `LazyBlockList` to make `ray.data.read_datasource()` simpler;
3. we are a bit smarter with how we progressively launch tasks and fetch and cache metadata, including fetching the metadata for read tasks in `.iter_blocks_with_metadata()` instead of relying on the pre-read task metadata (which will be less accurate), and we also fix some small bugs in the lazy ramp-up around progressive metadata fetching.

(1) is the most important item for supporting out-of-band serialization and fundamentally changes the `LazyBlockList` data model. This is required since we need to be able to reference the underlying read tasks when rewriting read stages during optimization and when serializing the lineage of the Dataset. See the [mono-PR](ray-project#22616) for more context.

Other changes:
1. Changed stats actor to a global named actor singleton in order to obviate the need for serializing the actor handle with the Dataset stats; without this, we were encountering serialization failures.
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