codingutf8 2023 huggingface inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license nllbmoe model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername nllbmoepretrainedconfigarchivemap facebooknllbmoe54b https huggingface cofacebooknllbmoe54bresolvemainconfig json class nllbmoeconfigpretrainedconfig r this is the configuration class to store the configuration of a nllbmoemodel it is used to instantiate an nllbmoe 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 nllbmoe facebooknllbmoe54bhttps huggingface cofacebooknllbmoe54b architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocabsize int optional defaults to 50265 vocabulary size of the nllbmoe model defines the number of different tokens that can be represented by the inputsids passed when calling nllbmoemodel or dmodel int optional defaults to 1024 dimensionality of the layers and the pooler layer encoderlayers int optional defaults to 12 number of encoder layers decoderlayers int optional defaults to 12 number of decoder layers encoderattentionheads int optional defaults to 16 number of attention heads for each attention layer in the transformer encoder decoderattentionheads int optional defaults to 16 number of attention heads for each attention layer in the transformer decoder decoderffndim int optional defaults to 4096 dimensionality of the intermediate often named feedforward layer in decoder encoderffndim int optional defaults to 4096 dimensionality of the intermediate often named feedforward layer in encoder activationfunction str or function optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu silu and gelunew are supported dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentiondropout float optional defaults to 0 0 the dropout ratio for the attention probabilities activationdropout float optional defaults to 0 0 the dropout ratio for activations inside the fully connected layer classifierdropout float optional defaults to 0 0 the dropout ratio for classifier maxpositionembeddings 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 initstd float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices encoderlayerdrop float optional defaults to 0 0 the layerdrop probability for the encoder see the layerdrop papersee https arxiv orgabs1909 11556 for more details decoderlayerdrop float optional defaults to 0 0 the layerdrop probability for the decoder see the layerdrop papersee https arxiv orgabs1909 11556 for more details secondexpertpolicy str optional default to all the policy used for the sampling the probability of being sampled to a second expert for each token normalizerouterprobbeforedropping bool optional defaults to true whether or not to normalize the router probabilities before applying a mask based on the experts capacity capacity dropping batchprioritizedrouting 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 moeevalcapacitytokenfraction 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 numexperts int optional defaults to 128 number of experts for each nllbmoesparsemlp layer expertcapacity int optional defaults to 64 number of tokens that can be stored in each expert encodersparsestep int optional defaults to 4 frequency of the sparse layers in the encoder 4 means that one out of 4 layers will be sparse decodersparsestep int optional defaults to 4 frequency of the sparse layers in the decoder 4 means that one out of 4 layers will be sparse routerdtype 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 paperhttps arxiv orgabs2101 03961 routerignorepaddingtokens bool optional defaults to false whether to ignore padding tokens when routing if false the padding tokens are not routed to any experts routerbias bool optional defaults to false whether or not the classifier of the router should have a bias moetokendropout float optional defualt ot 0 2 masking rate for moe expert output masking eom which is implemented via a dropout2d on the expert outputs outputrouterlogits bool optional defaults to false whether or not to return the router logits only set to true to get the auxiliary loss when training usecache bool optional defaults to true whether or not the model should return the last keyvalues attentions not used by all models example python from transformers import nllbmoemodel nllbmoeconfig initializing a nllbmoe facebooknllbmoe54b style configuration configuration nllbmoeconfig initializing a model from the facebooknllbmoe54b style configuration model nllbmoemodelconfiguration accessing the model configuration configuration model config modeltype nllbmoe keystoignoreatinference pastkeyvalues attributemap numattentionheads encoderattentionheads hiddensize dmodel def init self vocabsize128112 maxpositionembeddings1024 encoderlayers12 encoderffndim4096 encoderattentionheads16 decoderlayers12 decoderffndim4096 decoderattentionheads16 encoderlayerdrop0 05 decoderlayerdrop0 05 usecachetrue isencoderdecodertrue activationfunctionrelu dmodel1024 dropout0 1 attentiondropout0 1 activationdropout0 0 initstd0 02 decoderstarttokenid2 scaleembeddingtrue routerbiasfalse routerdtypefloat32 routerignorepaddingtokensfalse numexperts128 expertcapacity64 encodersparsestep4 decodersparsestep4 routerzlosscoef0 001 routerauxlosscoef0 001 secondexpertpolicyall normalizerouterprobbeforedroppingfalse batchprioritizedroutingfalse moeevalcapacitytokenfraction1 0 moetokendropout0 2 padtokenid1 bostokenid0 eostokenid2 outputrouterlogitsfalse kwargs self vocabsize vocabsize self maxpositionembeddings maxpositionembeddings self dmodel dmodel self encoderffndim encoderffndim self encoderlayers encoderlayers self encoderattentionheads encoderattentionheads self decoderffndim decoderffndim self decoderlayers decoderlayers self decoderattentionheads decoderattentionheads self dropout dropout self attentiondropout attentiondropout self activationdropout activationdropout self activationfunction activationfunction self initstd initstd self encoderlayerdrop encoderlayerdrop self decoderlayerdrop decoderlayerdrop self usecache usecache self numhiddenlayers encoderlayers self scaleembedding scaleembedding scale factor will be sqrtdmodel if true self routerzlosscoef routerzlosscoef self routerauxlosscoef routerauxlosscoef self decodersparsestep decodersparsestep self encodersparsestep encodersparsestep self numexperts numexperts self expertcapacity expertcapacity self routerbias routerbias if routerdtype not in float32 float16 bfloat16 raise valueerrorfrouterdtype must be one of float32 float16 or bfloat16 got routerdtype self routerdtype routerdtype self routerignorepaddingtokens routerignorepaddingtokens self batchprioritizedrouting batchprioritizedrouting self secondexpertpolicy secondexpertpolicy self normalizerouterprobbeforedropping normalizerouterprobbeforedropping self moeevalcapacitytokenfraction moeevalcapacitytokenfraction self moetokendropout moetokendropout self outputrouterlogits outputrouterlogits super init padtokenidpadtokenid bostokenidbostokenid eostokenideostokenid isencoderdecoderisencoderdecoder decoderstarttokeniddecoderstarttokenid kwargs coding utf 8 2023 huggingface inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license nllb moe model configuration 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 https huggingface co 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 args 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 https arxiv org abs 2101 03961 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 example python 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 scale factor will be sqrt d_model if true
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}


class NllbMoeConfig(PretrainedConfig):
    r

    model_type = "nllb-moe"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

    def __init__(
        self,
        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,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.use_cache = use_cache
        self.num_hidden_layers = encoder_layers
        self.scale_embedding = scale_embedding  
        self.router_z_loss_coef = router_z_loss_coef
        self.router_aux_loss_coef = router_aux_loss_coef
        self.decoder_sparse_step = decoder_sparse_step
        self.encoder_sparse_step = encoder_sparse_step
        self.num_experts = num_experts
        self.expert_capacity = expert_capacity
        self.router_bias = router_bias
        if router_dtype not in ["float32", "float16", "bfloat16"]:
            raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
        self.router_dtype = router_dtype

        self.router_ignore_padding_tokens = router_ignore_padding_tokens
        self.batch_prioritized_routing = batch_prioritized_routing
        self.second_expert_policy = second_expert_policy
        self.normalize_router_prob_before_dropping = normalize_router_prob_before_dropping
        self.moe_eval_capacity_token_fraction = moe_eval_capacity_token_fraction
        self.moe_token_dropout = moe_token_dropout
        self.output_router_logits = output_router_logits
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            decoder_start_token_id=decoder_start_token_id,
            **kwargs,
        )