[tune](deps): Bump transformers from 4.8.1 to 4.21.2 in /python/requirements/tune

[dependabot]

Bumps transformers from 4.8.1 to 4.21.2.

Release notes

Sourced from transformers's releases.

v4.21.2: Patch release

Fix a regression in the TableQA pipeline: Fix a regression in Trainer checkpoint loading: #18428

# v4.21.1: Patch release

Fix a regression in Trainer checkpoint loading: #18470

v4.21.0: TF XLA text generation - Custom Pipelines - OwlViT, NLLB, MobileViT, Nezha, GroupViT, MVP, CodeGen, UL2

TensorFlow XLA Text Generation

The TensorFlow text generation method can now be wrapped with tf.function and compiled to XLA. You should be able to achieve up to 100x speedup this way. See our blog post and our benchmarks. You can also see XLA generation in action in our example notebooks, particularly for summarization and translation.

import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("t5-small")
model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-small")
Main changes with respect to the original generate workflow: tf.function and pad_to_multiple_of
xla_generate = tf.function(model.generate, jit_compile=True)
tokenization_kwargs = {"pad_to_multiple_of": 32, "padding": True, "return_tensors": "tf"}
The first prompt will be slow (compiling), the others will be very fast!
input_prompts = [
f"translate English to {language}: I have four cats and three dogs."
for language in ["German", "French", "Romanian"]
]
for input_prompt in input_prompts:
tokenized_inputs = tokenizer([input_prompt], **tokenization_kwargs)
generated_text = xla_generate(**tokenized_inputs, max_new_tokens=32)
print(tokenizer.decode(generated_text[0], skip_special_tokens=True))

• Generate: deprecate default max_length by @​gante in #18018
• TF: GPT-J compatible with XLA generation by @​gante in #17986
• TF: T5 can now handle a padded past (i.e. XLA generation) by @​gante in #17969
• TF: XLA beam search + most generation-compatible models are now also XLA-generate-compatible by @​gante in #17857
• TF: generate without tf.TensorArray by @​gante in #17801
• TF: BART compatible with XLA generation by @​gante in #17479

New model additions

OwlViT

The OWL-ViT model (short for Vision Transformer for Open-World Localization) was proposed in Simple Open-Vocabulary Object Detection with Vision Transformers by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby. OWL-ViT is an open-vocabulary object detection network trained on a variety of (image, text) pairs. It can be used to query an image with one or multiple text queries to search for and detect target objects described in text.

• Add OWL-ViT model for zero-shot object detection by @​alaradirik in #17938
• Fix OwlViT tests by @​sgugger in #18253

NLLB

... (truncated)

Commits

• b487096 Patch release: v4.21.2
• c5f7df8 Accept trust_remote_code and ignore it in PreTrainedModel.from_pretrained...
• f0d4968 Patch release: v4.21.1
• dea58d6 Fix load of model checkpoints in the Trainer (#18470)
• a9eee2f Release: v4.21.0
• 0daa202 Fix sacremoses sof dependency for Transofmers XL
• 31b3a12 sentencepiece shouldn't be required for the fast LayoutXLM tokenizer
• 3496ea8 Remove all uses of six (#18318)
• 9e564d0 fix loading from pretrained for sharded model with `torch_dtype="auto" (#18061)
• 36f9859 [EncoderDecoder] Improve docs (#18271)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #98.