NLLB

Updated tokenizer behavior

DISCLAIMER: The default behaviour for the tokenizer was fixed and thus changed in April 2023. The previous version adds [self.eos_token_id, self.cur_lang_code] at the end of the token sequence for both target and source tokenization. This is wrong as the NLLB paper mentions (page 48, 6.1.1. Model Architecture) :

Note that we prefix the source sequence with the source language, as opposed to the target language as previously done in several works (Arivazhagan et al., 2019; Johnson et al., 2017). This is primarily because we prioritize optimizing zero-shot performance of our model on any pair of 200 languages at a minor cost to supervised performance.

Previous behaviour:

>>> from transformers import NllbTokenizer

>>> tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
>>> tokenizer("How was your day?").input_ids
[13374, 1398, 4260, 4039, 248130, 2, 256047]

>>> # 2: '</s>'
>>> # 256047 : 'eng_Latn'

New behaviour

>>> from transformers import NllbTokenizer

>>> tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
>>> tokenizer("How was your day?").input_ids
[256047, 13374, 1398, 4260, 4039, 248130, 2]

Enabling the old behaviour can be done as follows:

>>> from transformers import NllbTokenizer

>>> tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M", legacy_behaviour=True)

For more details, feel free to check the linked PR and Issue.

Overview

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.

The abstract of the paper is the following:

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by first contextualizing the need for low-resource language translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed at narrowing the performance gap between low and high-resource languages. More specifically, we developed a conditional compute model based on Sparsely Gated Mixture of Experts that is trained on data obtained with novel and effective data mining techniques tailored for low-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.

This implementation contains the dense models available on release.

The sparse model NLLB-MoE (Mixture of Expert) is now available! More details here

This model was contributed by Lysandre. The authors’ code can be found here.

Generating with NLLB

While generating the target text set the forced_bos_token_id to the target language id. The following example shows how to translate English to French using the facebook/nllb-200-distilled-600M model.

Note that we’re using the BCP-47 code for French fra_Latn. See here for the list of all BCP-47 in the Flores 200 dataset.

>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")

>>> article = "UN Chief says there is no military solution in Syria"
>>> inputs = tokenizer(article, return_tensors="pt")

>>> translated_tokens = model.generate(
...     **inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("fra_Latn"), max_length=30
... )
>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
Le chef de l'ONU dit qu'il n'y a pas de solution militaire en Syrie

Generating from any other language than English

English (eng_Latn) is set as the default language from which to translate. In order to specify that you’d like to translate from a different language, you should specify the BCP-47 code in the src_lang keyword argument of the tokenizer initialization.

See example below for a translation from romanian to german:

>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained(
...     "facebook/nllb-200-distilled-600M", token=True, src_lang="ron_Latn"
... )
>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M", token=True)

>>> article = "Şeful ONU spune că nu există o soluţie militară în Siria"
>>> inputs = tokenizer(article, return_tensors="pt")

>>> translated_tokens = model.generate(
...     **inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("deu_Latn"), max_length=30
... )
>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
UN-Chef sagt, es gibt keine militärische Lösung in Syrien

Resources

• Translation task guide
• Summarization task guide

NllbTokenizer

>>> from transformers import NllbTokenizer

>>> tokenizer = NllbTokenizer.from_pretrained(
...     "facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="fra_Latn"
... )
>>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
>>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie."
>>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt")

NllbTokenizerFast

>>> from transformers import NllbTokenizerFast

>>> tokenizer = NllbTokenizerFast.from_pretrained(
...     "facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="fra_Latn"
... )
>>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
>>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie."
>>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt")

Using Flash Attention 2

Flash Attention 2 is a faster, optimized version of the attention scores computation which relies on cuda kernels.

Installation

First, check whether your hardware is compatible with Flash Attention 2. The latest list of compatible hardware can be found in the official documentation.

Next, install the latest version of Flash Attention 2:

pip install -U flash-attn --no-build-isolation

Usage

To load a model using Flash Attention 2, we can pass the argument attn_implementation="flash_attention_2" to .from_pretrained. You can use either torch.float16 or torch.bfloat16 precision.

>>> import torch
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M", torch_dtype=torch.float16, attn_implementation="flash_attention_2").to("cuda").eval()
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")

>>> article = "Şeful ONU spune că nu există o soluţie militară în Siria"
>>> inputs = tokenizer(article, return_tensors="pt").to("cuda")

>>> translated_tokens = model.generate(
...     **inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("deu_Latn"), max_length=30
... )
>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
"UN-Chef sagt, es gibt keine militärische Lösung in Syrien"

Expected speedups

Below is an expected speedup diagram that compares pure inference time between the native implementation and the Flash Attention 2.

Using Scaled Dot Product Attention (SDPA)

PyTorch includes a native scaled dot-product attention (SDPA) operator as part of torch.nn.functional. This function encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the official documentation or the GPU Inference page for more information.

SDPA is used by default for torch>=2.1.1 when an implementation is available, but you may also set attn_implementation="sdpa" in from_pretrained() to explicitly request SDPA to be used.

from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M", torch_dtype=torch.float16, attn_implementation="sdpa")
...

For the best speedups, we recommend loading the model in half-precision (e.g. torch.float16 or torch.bfloat16).