Source code for transformers.models.fsmt.configuration_fsmt

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""" FSMT configuration """


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP = {}


class DecoderConfig(PretrainedConfig):
    r"""
    Configuration class for FSMT's decoder specific things. note: this is a private helper class
    """
    model_type = "fsmt_decoder"

    def __init__(self, vocab_size=0, bos_token_id=0):
        super().__init__()
        self.vocab_size = vocab_size
        self.bos_token_id = bos_token_id


[docs]class FSMTConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.FSMTModel`. It is used to instantiate a FSMT model according to the specified arguments, defining the model architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: langs (:obj:`List[str]`): A list with source language and target_language (e.g., ['en', 'ru']). src_vocab_size (:obj:`int`): Vocabulary size of the encoder. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed to the forward method in the encoder. tgt_vocab_size (:obj:`int`): Vocabulary size of the decoder. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed to the forward method in the decoder. d_model (:obj:`int`, `optional`, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (:obj:`int`, `optional`, defaults to 12): Number of encoder layers. decoder_layers (:obj:`int`, `optional`, defaults to 12): Number of decoder layers. encoder_attention_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"relu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. max_position_embeddings (:obj:`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 (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. scale_embedding (:obj:`bool`, `optional`, defaults to :obj:`True`): Scale embeddings by diving by sqrt(d_model). bos_token_id (:obj:`int`, `optional`, defaults to 0) Beginning of stream token id. pad_token_id (:obj:`int`, `optional`, defaults to 1) Padding token id. eos_token_id (:obj:`int`, `optional`, defaults to 2) End of stream token id. decoder_start_token_id (:obj:`int`, `optional`): This model starts decoding with :obj:`eos_token_id` encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): Google "layerdrop arxiv", as its not explainable in one line. decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): Google "layerdrop arxiv", as its not explainable in one line. is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether this is an encoder/decoder model. tie_word_embeddings (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to tie input and output embeddings. num_beams (:obj:`int`, `optional`, defaults to 5) Number of beams for beam search that will be used by default in the :obj:`generate` method of the model. 1 means no beam search. length_penalty (:obj:`float`, `optional`, defaults to 1) Exponential penalty to the length that will be used by default in the :obj:`generate` method of the model. early_stopping (:obj:`bool`, `optional`, defaults to :obj:`False`) Flag that will be used by default in the :obj:`generate` method of the model. Whether to stop the beam search when at least ``num_beams`` sentences are finished per batch or not. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last key/values attentions (not used by all models). forced_eos_token_id (:obj:`int`, `optional`, defaults to 2): The id of the token to force as the last generated token when :obj:`max_length` is reached. Usually set to :obj:`eos_token_id`. Examples:: >>> from transformers import FSMTConfig, FSMTModel >>> config = FSMTConfig.from_pretrained('facebook/wmt19-en-ru') >>> model = FSMTModel(config) """ model_type = "fsmt" attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} # update the defaults from config file def __init__( self, langs=["en", "de"], src_vocab_size=42024, tgt_vocab_size=42024, activation_function="relu", d_model=1024, max_length=200, max_position_embeddings=1024, encoder_ffn_dim=4096, encoder_layers=12, encoder_attention_heads=16, encoder_layerdrop=0.0, decoder_ffn_dim=4096, decoder_layers=12, decoder_attention_heads=16, decoder_layerdrop=0.0, attention_dropout=0.0, dropout=0.1, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=2, is_encoder_decoder=True, scale_embedding=True, tie_word_embeddings=False, num_beams=5, length_penalty=1.0, early_stopping=False, use_cache=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, forced_eos_token_id=2, **common_kwargs ): self.langs = langs self.src_vocab_size = src_vocab_size self.tgt_vocab_size = tgt_vocab_size self.d_model = d_model # encoder_embed_dim and decoder_embed_dim self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = self.num_hidden_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.max_position_embeddings = max_position_embeddings self.init_std = init_std # Normal(0, this parameter) self.activation_function = activation_function self.decoder = DecoderConfig(vocab_size=tgt_vocab_size, bos_token_id=eos_token_id) if "decoder" in common_kwargs: del common_kwargs["decoder"] self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True # 3 Types of Dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.dropout = dropout self.use_cache = use_cache super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, decoder_start_token_id=decoder_start_token_id, is_encoder_decoder=is_encoder_decoder, tie_word_embeddings=tie_word_embeddings, forced_eos_token_id=forced_eos_token_id, max_length=max_length, num_beams=num_beams, length_penalty=length_penalty, early_stopping=early_stopping, **common_kwargs, )
[docs] def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig`. Returns: :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = copy.deepcopy(self.__dict__) output["decoder"] = self.decoder.to_dict() output["model_type"] = self.__class__.model_type return output