# coding=utf-8 # Copyright 2020 The Microsoft Authors and The HuggingFace Inc. team. # # 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. """ProphetNet model configuration""" from typing import Callable, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class ProphetNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ProphetNetModel`]. It is used to instantiate a ProphetNet 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 ProphetNet [microsoft/prophetnet-large-uncased](https://huggingface.co/microsoft/prophetnet-large-uncased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: activation_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for activations inside the fully connected layer. 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. vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ProphetNetModel`]. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. num_encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. num_encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the `intermediate` (often named feed-forward) layer in decoder. num_decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. num_decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. max_position_embeddings (`int`, *optional*, defaults to 512): 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. add_cross_attention (`bool`, *optional*, defaults to `True`): Whether cross-attention layers should be added to the model. is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether this is an encoder/decoder model. pad_token_id (`int`, *optional*, defaults to 1) Padding token id. bos_token_id (`int`, *optional*, defaults to 0) Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2) End of stream token id. ngram (`int`, *optional*, defaults to 2) Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first token. num_buckets (`int`, *optional*, defaults to 32) The number of buckets to use for each attention layer. This is for relative position calculation. See the [T5 paper](see https://arxiv.org/abs/1910.10683) for more details. relative_max_distance (`int`, *optional*, defaults to 128) Relative distances greater than this number will be put into the last same bucket. This is for relative position calculation. See the [T5 paper](see https://arxiv.org/abs/1910.10683) for more details. disable_ngram_loss (`bool`, *optional*, defaults to `False`): Whether be trained predicting only the next first token. eps (`float`, *optional*, defaults to 0.0): Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label smoothing is performed. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ model_type = "prophetnet" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_attention_heads": "num_encoder_attention_heads", } def __init__( self, activation_dropout: Optional[float] = 0.1, activation_function: Optional[Union[str, Callable]] = "gelu", vocab_size: Optional[int] = 30522, hidden_size: Optional[int] = 1024, encoder_ffn_dim: Optional[int] = 4096, num_encoder_layers: Optional[int] = 12, num_encoder_attention_heads: Optional[int] = 16, decoder_ffn_dim: Optional[int] = 4096, num_decoder_layers: Optional[int] = 12, num_decoder_attention_heads: Optional[int] = 16, attention_dropout: Optional[float] = 0.1, dropout: Optional[float] = 0.1, max_position_embeddings: Optional[int] = 512, init_std: Optional[float] = 0.02, is_encoder_decoder: Optional[bool] = True, add_cross_attention: Optional[bool] = True, decoder_start_token_id: Optional[int] = 0, ngram: Optional[int] = 2, num_buckets: Optional[int] = 32, relative_max_distance: Optional[int] = 128, disable_ngram_loss: Optional[bool] = False, eps: Optional[float] = 0.0, use_cache: Optional[bool] = True, pad_token_id: Optional[int] = 0, bos_token_id: Optional[int] = 1, eos_token_id: Optional[int] = 2, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.encoder_ffn_dim = encoder_ffn_dim self.num_encoder_layers = num_encoder_layers self.num_encoder_attention_heads = num_encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.num_decoder_layers = num_decoder_layers self.num_decoder_attention_heads = num_decoder_attention_heads self.max_position_embeddings = max_position_embeddings self.init_std = init_std # Normal(0, this parameter) self.activation_function = activation_function # parameters for prophetnet self.ngram = ngram self.num_buckets = num_buckets self.relative_max_distance = relative_max_distance self.disable_ngram_loss = disable_ngram_loss self.eps = eps # 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, is_encoder_decoder=is_encoder_decoder, add_cross_attention=add_cross_attention, decoder_start_token_id=decoder_start_token_id, **kwargs, ) @property def num_hidden_layers(self) -> int: return self.num_encoder_layers + self.num_decoder_layers @num_hidden_layers.setter def num_hidden_layers(self, value): raise NotImplementedError( "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and" " `num_decoder_layers`." ) __all__ = ["ProphetNetConfig"]