
 
NLLB-MOE
	
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.
Tips:
M2M100ForConditionalGeneration is the base model for both NLLB and NLLB MoE
The NLLB-MoE is very similar to the NLLB model, but it’s feed forward layer is based on the implementation of SwitchTransformers.
The tokenizer is the same as the NLLB models.
This model was contributed by Arthur Zucker.
The original code can be found here.
Implementation differences with SwitchTransformers
	
The biggest difference is the way the tokens are routed. NLLB-MoE uses a `top-2-gate` which means that for each input, only the top two experts are selected based on the 
highest predicted probabilities from the gating network, and the remaining experts are ignored. In `SwitchTransformers`, only the top-1 probabilities are computed, 
which means that tokens have less probability of being forwarded. Moreover, if a token is not routed to any expert, `SwitchTransformers` still adds its unmodified hidden 
states (kind of like a residual connection) while they are masked in `NLLB`'s top-2 routing mechanism.
Generating with NLLB-MoE
	
The avalable checkpoints requires around 350GB of storage. Make sure to use `accelerate` if you do not have enough RAM on your machine.
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.
	Copied
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b")
 model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-moe-54b")
 article = "Previously, Ring's CEO, Jamie Siminoff, remarked the company started when his doorbell wasn't audible from his shop in his garage."
 inputs = tokenizer(article, return_tensors="pt")
 translated_tokens = model.generate(
...     **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["fra_Latn"], max_length=50
... )
 tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
"Auparavant, le PDG de Ring, Jamie Siminoff, a fait remarquer que la société avait commencé lorsque sa sonnette n'était pas audible depuis son magasin dans son garage."
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:
	Copied
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b", src_lang="ron_Latn")
 model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-moe-54b")
 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.lang_code_to_id["deu_Latn"], max_length=30
... )
 tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
Documentation resources
	
Translation task guide
Summarization task guide
NllbMoeConfig
	
class transformers.NllbMoeConfig
<
source
>
(
vocab_size = 128112
max_position_embeddings = 1024
encoder_layers = 12
encoder_ffn_dim = 4096
encoder_attention_heads = 16
decoder_layers = 12
decoder_ffn_dim = 4096
decoder_attention_heads = 16
encoder_layerdrop = 0.05
decoder_layerdrop = 0.05
use_cache = True
is_encoder_decoder = True
activation_function = 'relu'
d_model = 1024
dropout = 0.1
attention_dropout = 0.1
activation_dropout = 0.0
init_std = 0.02
decoder_start_token_id = 2
scale_embedding = True
router_bias = False
router_dtype = 'float32'
router_ignore_padding_tokens = False
num_experts = 128
expert_capacity = 64
encoder_sparse_step = 4
decoder_sparse_step = 4
router_z_loss_coef = 0.001
router_aux_loss_coef = 0.001
second_expert_policy = 'all'
normalize_router_prob_before_dropping = False
batch_prioritized_routing = False
moe_eval_capacity_token_fraction = 1.0
moe_token_dropout = 0.2
pad_token_id = 1
bos_token_id = 0
eos_token_id = 2
output_router_logits = False
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 50265) —
Vocabulary size of the NllbMoe model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling NllbMoeModel or
d_model (int, optional, defaults to 1024) —
Dimensionality of the layers and the pooler layer.
encoder_layers (int, optional, defaults to 12) —
Number of encoder layers.
decoder_layers (int, optional, defaults to 12) —
Number of decoder layers.
encoder_attention_heads (int, optional, defaults to 16) —
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (int, optional, defaults to 16) —
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (int, optional, defaults to 4096) —
Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
encoder_ffn_dim (int, optional, defaults to 4096) —
Dimensionality of the “intermediate” (often named feed-forward) layer in encoder.
activation_function (str or function, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "silu" and "gelu_new" are supported.
dropout (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.
activation_dropout (float, optional, defaults to 0.0) —
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (float, optional, defaults to 0.0) —
The dropout ratio for classifier.
max_position_embeddings (int, optional, defaults to 1024) —
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (float, optional, defaults to 0.0) —
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (float, optional, defaults to 0.0) —
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
second_expert_policy ( str, optional, default to "all") —
The policy used for the sampling the probability of being sampled to a second expert for each token.
normalize_router_prob_before_dropping (bool, optional, defaults to True) —
Whether or not to normalize the router probabilities before applying a mask based on the experts capacity
(capacity dropping).
batch_prioritized_routing (bool, optional, defaults to True) —
Whether or not to orders the tokens by their router probabilities before capacity dropping. This means that
the tokens that have the highest probabilities will be routed before other tokens that might be further in
the sequence.
moe_eval_capacity_token_fraction (float, optional, defaults to 1.0) —
Fraction of tokens as capacity during validation, if set to negative, uses the same as training. Should be
in range: (0.0, 1.0].
num_experts (int, optional, defaults to 128) —
Number of experts for each NllbMoeSparseMlp layer.
expert_capacity (int, optional, defaults to 64) —
Number of tokens that can be stored in each expert.
encoder_sparse_step (int, optional, defaults to 4) —
Frequency of the sparse layers in the encoder. 4 means that one out of 4 layers will be sparse.
decoder_sparse_step (int, optional, defaults to 4) —
Frequency of the sparse layers in the decoder. 4 means that one out of 4 layers will be sparse.
router_dtype (str, optional, default to "float32") —
The dtype used for the routers. It is preferable to keep the dtype to "float32" as specified in the
selective precision discussion in the paper.
router_ignore_padding_tokens (bool, optional, defaults to False) —
Whether to ignore padding tokens when routing. if False, the padding tokens are not routed to any
experts.
router_bias (bool, optional, defaults to False) —
Whether or not the classifier of the router should have a bias.
moe_token_dropout (float, optional, defualt ot 0.2) —
Masking rate for MoE expert output masking (EOM), which is implemented via a Dropout2d on the expert
outputs.
output_router_logits (bool, optional, defaults to False) —
Whether or not to return the router logits. Only set to True to get the auxiliary loss when training.
use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models).
This is the configuration class to store the configuration of a NllbMoeModel. It is used to instantiate an
NLLB-MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the NLLB-MoE
facebook/nllb-moe-54b architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import NllbMoeModel, NllbMoeConfig
 # Initializing a NllbMoe facebook/nllb-moe-54b style configuration
 configuration = NllbMoeConfig()
 # Initializing a model from the facebook/nllb-moe-54b style configuration
 model = NllbMoeModel(configuration)
 # Accessing the model configuration
 configuration = model.config
NllbMoeTop2Router
	
class transformers.NllbMoeTop2Router
<
source
>
(
config: NllbMoeConfig
)
Router using tokens choose top-2 experts assignment.
This router uses the same mechanism as in NLLB-MoE from the fairseq repository. Items are sorted by router_probs
and then routed to their choice of expert until the expert’s expert_capacity is reached. There is no guarantee
that each token is processed by an expert, or that each expert receives at least one token.
The router combining weights are also returned to make sure that the states that are not updated will be masked.
route_tokens
<
source
>
(
router_logits: Tensor
input_dtype: dtype = torch.float32
padding_mask: typing.Optional[torch.LongTensor] = None
)
Computes the dispatch_mask and the dispatch_weights for each experts. The masks are adapted to the expert
capacity.
forward
<
source
>
(
hidden_states: Tensor
padding_mask: typing.Optional[torch.LongTensor] = None
)
→
top_1_mask (torch.Tensor of shape (batch_size, sequence_length))
Parameters 
hidden_states (torch.Tensor) —
(batch_size, sequence_length, hidden_dim) from which router probabilities are computed.
Returns
top_1_mask (torch.Tensor of shape (batch_size, sequence_length))
Index tensor of shape [batch_size, sequence_length] corresponding to the expert selected for each token
using the top1 probabilities of the router.
router_probabilities (torch.Tensor of shape (batch_size, sequence_length, nump_experts)):
Tensor of shape (batch_size, sequence_length, num_experts) corresponding to the probabilities for each
token and expert. Used for routing tokens to experts.
router_logits (torch.Tensor of shape (batch_size, sequence_length))):
Logits tensor of shape (batch_size, sequence_length, num_experts) corresponding to raw router logits.
This is used later for computing router z-loss.
The hidden states are reshaped to simplify the computation of the router probabilities (combining weights for
each experts.)
NllbMoeSparseMLP
	
class transformers.NllbMoeSparseMLP
<
source
>
(
config: NllbMoeConfig
ffn_dim: int
expert_class: Module = <class 'transformers.models.nllb_moe.modeling_nllb_moe.NllbMoeDenseActDense'>
)
Implementation of the NLLB-MoE sparse MLP module.
forward
<
source
>
(
hidden_states: Tensor
padding_mask: typing.Optional[torch.Tensor] = False
)
→
hidden_states (torch.Tensor of shape (batch_size, sequence_length, hidden_dim))
Parameters 
hidden_states (torch.Tensor of shape (batch_size, sequence_length, hidden_dim)) —
The hidden states
padding_mask (torch.Tensor, optional, defaults to False) —
Attention mask. Can be in the causal form or not.
Returns
hidden_states (torch.Tensor of shape (batch_size, sequence_length, hidden_dim))
Updated hidden states
router_logits (torch.Tensor of shape (batch_size, sequence_length, num_experts)):
Needed for computing the loss
The goal of this forward pass is to have the same number of operation as the equivalent NllbMoeDenseActDense
(mlp) layer. This means that all of the hidden states should be processed at most twice ( since we are using a
top_2 gating mecanism). This means that we keep the complexity to O(batch_size x sequence_length x hidden_dim)
instead of O(num_experts x batch_size x sequence_length x hidden_dim).
1- Get the router_probs from the router. The shape of the router_mask is (batch_size X sequence_length, num_expert) and corresponds to the boolean version of the router_probs. The inputs are masked using the
router_mask.
2- Dispatch the hidden_states to its associated experts. The router probabilities are used to weight the
contribution of each experts when updating the masked hidden states.
NllbMoeModel
	
class transformers.NllbMoeModel
<
source
>
(
config: NllbMoeConfig
)
Parameters 
config (NllbMoeConfig) —
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
from_pretrained() method to load the model weights.
The bare NllbMoe Model outputting raw hidden-states without any specific head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
decoder_input_ids: typing.Optional[torch.LongTensor] = None
decoder_attention_mask: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.Tensor] = None
decoder_head_mask: typing.Optional[torch.Tensor] = None
cross_attn_head_mask: typing.Optional[torch.Tensor] = None
encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
output_router_logits: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.Seq2SeqMoEModelOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Returns
transformers.modeling_outputs.Seq2SeqMoEModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Seq2SeqMoEModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (NllbMoeConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the decoder of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse
modules.
The NllbMoeModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
The NllbMoeModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
	Copied
 from transformers import AutoTokenizer, NllbMoeModel
 tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts")
 model = SwitchTransformersModel.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts")
 input_ids = tokenizer(
...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids  # Batch size 1
 decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
 # preprocess: Prepend decoder_input_ids with start token which is pad token for NllbMoeModel
 decoder_input_ids = model._shift_right(decoder_input_ids)
 # forward pass
 outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
 last_hidden_states = outputs.last_hidden_state
NllbMoeForConditionalGeneration
	
class transformers.NllbMoeForConditionalGeneration
<
source
>
(
config: NllbMoeConfig
)
Parameters 
config (NllbMoeConfig) —
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
from_pretrained() method to load the model weights.
The NllbMoe Model with a language modeling head. Can be used for summarization.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
decoder_input_ids: typing.Optional[torch.LongTensor] = None
decoder_attention_mask: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.Tensor] = None
decoder_head_mask: typing.Optional[torch.Tensor] = None
cross_attn_head_mask: typing.Optional[torch.Tensor] = None
encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
output_router_logits: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.Seq2SeqMoEOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
NllbMoe uses the eos_token_id as the starting token for decoder_input_ids generation. If
past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_key_values).
decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.
head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length). inputs_embeds (torch.FloatTensor of shape
(batch_size, sequence_length, hidden_size), optional): Optionally, instead of passing input_ids you
can choose to directly pass an embedded representation. This is useful if you want more control over how to
convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) —
Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded
representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (see past_key_values). This is useful if you want more control over how to convert
decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.
use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
output_router_logits (bool, optional) —
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored
(masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].
Returns
transformers.modeling_outputs.Seq2SeqMoEOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Seq2SeqMoEOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (NllbMoeConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the encoder model, useful to compute the auxiliary loss and z_loss for Mixture of Experts
models.
The NllbMoeForConditionalGeneration forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Translation example:
	Copied
 from transformers import AutoTokenizer, NllbMoeForConditionalGeneration
 model = NllbMoeForConditionalGeneration.from_pretrained("facebook/nllb-moe-54b")
 tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b")
 text_to_translate = "Life is like a box of chocolates"
 model_inputs = tokenizer(text_to_translate, return_tensors="pt")
 # translate to French
 gen_tokens = model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("eng_Latn"))
 print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))
←NLLB
Nyströmformer→
NLLB-MOE
Overview
Implementation differences with SwitchTransformers
Generating with NLLB-MoE
Generating from any other language than English
Documentation resources
NllbMoeConfig
NllbMoeTop2Router
NllbMoeSparseMLP
NllbMoeModel
NllbMoeForConditionalGeneration
