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M2M100

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This model was released on 2020-10-21 and added to Hugging Face Transformers on 2021-03-06.

M2M100

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Overview

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.

The abstract from the paper is the following:

Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages. However, much of this work is English-Centric by training only on data which was translated from or to English. While this is supported by large sources of training data, it does not reflect translation needs worldwide. In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Then, we explore how to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters to create high quality models. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT. We open-source our scripts so that others may reproduce the data, evaluation, and final M2M-100 model.

This model was contributed by valhalla.

Usage tips and examples

M2M100 is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation tasks. As the model is multilingual it expects the sequences in a certain format: A special language id token is used as prefix in both the source and target text. The source text format is [lang_code] X [eos], where lang_code is source language id for source text and target language id for target text, with X being the source or target text.

The [M2M100Tokenizer] depends on sentencepiece so be sure to install it before running the examples. To install sentencepiece run pip install sentencepiece.

Supervised Training

python
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer


model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M", device_map="auto")
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="fr")

src_text = "Life is like a box of chocolates."
tgt_text = "La vie est comme une boîte de chocolat."

model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt").to(model.device)

loss = model(**model_inputs).loss  # forward pass

Generation

M2M100 uses the eos_token_id as the decoder_start_token_id for generation with the target language id being forced as the first generated token. To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method. The following example shows how to translate between Hindi to French and Chinese to English using the facebook/m2m100_418M checkpoint.

python
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer


hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।"
chinese_text = "生活就像一盒巧克力。"

model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M", device_map="auto")
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")

# translate Hindi to French
tokenizer.src_lang = "hi"
encoded_hi = tokenizer(hi_text, return_tensors="pt").to(model.device)
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
"La vie est comme une boîte de chocolat."

# translate Chinese to English
tokenizer.src_lang = "zh"
encoded_zh = tokenizer(chinese_text, return_tensors="pt").to(model.device)
generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
"Life is like a box of chocolate."

Resources

M2M100Config

[[autodoc]] M2M100Config

M2M100Tokenizer

[[autodoc]] M2M100Tokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary

M2M100Model

[[autodoc]] M2M100Model - forward

M2M100ForConditionalGeneration

[[autodoc]] M2M100ForConditionalGeneration - forward

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:

bash
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.

python
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer


model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M", attn_implementation="flash_attention_2", device_map="auto").eval()
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")

# translate Hindi to French
hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।"
tokenizer.src_lang = "hi"
encoded_hi = tokenizer(hi_text, return_tensors="pt").to(model.device)
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
"La vie est comme une boîte de chocolat."

Expected speedups

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

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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.

python
from transformers import M2M100ForConditionalGeneration


model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M", attn_implementation="sdpa", device_map="auto")
...

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