Bump transformers from 4.5.0 to 4.21.1

[dependabot[bot]]

Bumps transformers from 4.5.0 to 4.21.1.

Release notes

Sourced from transformers's releases.

# 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

The NLLB model was presented in No Language Left Behind: Scaling Human-Centered Machine Translation by Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang. No Language Left Behind (NLLB) is a model capable of delivering high-quality translations directly between any pair of 200+ languages — including low-resource languages like Asturian, Luganda, Urdu and more.

... (truncated)

Commits

• 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)
• 3c45faa [DETR] Improve code examples (#18262)
• ee67e7a patch for smddp import (#18244)
• Additional commits viewable in compare view

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