181 lines
8.8 KiB
Python
181 lines
8.8 KiB
Python
# coding=utf-8
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# Copyright 2020 The Microsoft Authors and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" ProphetNet model configuration"""
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from typing import Callable, Optional, Union
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class ProphetNetConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ProphetNetModel`]. It is used to instantiate a
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ProphetNet model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the ProphetNet
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[microsoft/prophetnet-large-uncased](https://huggingface.co/microsoft/prophetnet-large-uncased) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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activation_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for activations inside the fully connected layer.
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activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`ProphetNetModel`].
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hidden_size (`int`, *optional*, defaults to 1024):
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Dimensionality of the layers and the pooler layer.
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encoder_ffn_dim (`int`, *optional*, defaults to 4096):
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
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num_encoder_layers (`int`, *optional*, defaults to 12):
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Number of encoder layers.
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num_encoder_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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decoder_ffn_dim (`int`, *optional*, defaults to 4096):
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Dimensionality of the `intermediate` (often named feed-forward) layer in decoder.
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num_decoder_layers (`int`, *optional*, defaults to 12):
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Number of decoder layers.
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num_decoder_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer decoder.
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attention_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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init_std (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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add_cross_attention (`bool`, *optional*, defaults to `True`):
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Whether cross-attention layers should be added to the model.
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is_encoder_decoder (`bool`, *optional*, defaults to `True`):
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Whether this is an encoder/decoder model.
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pad_token_id (`int`, *optional*, defaults to 1)
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 0)
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2)
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End of stream token id.
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ngram (`int`, *optional*, defaults to 2)
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Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
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token.
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num_buckets (`int`, *optional*, defaults to 32)
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The number of buckets to use for each attention layer. This is for relative position calculation. See the
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[T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
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relative_max_distance (`int`, *optional*, defaults to 128)
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Relative distances greater than this number will be put into the last same bucket. This is for relative
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position calculation. See the [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
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disable_ngram_loss (`bool`, *optional*, defaults to `False`):
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Whether be trained predicting only the next first token.
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eps (`float`, *optional*, defaults to 0.0):
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Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label
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smoothing is performed.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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"""
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model_type = "prophetnet"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"num_attention_heads": "num_encoder_attention_heads",
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}
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def __init__(
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self,
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activation_dropout: Optional[float] = 0.1,
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activation_function: Optional[Union[str, Callable]] = "gelu",
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vocab_size: Optional[int] = 30522,
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hidden_size: Optional[int] = 1024,
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encoder_ffn_dim: Optional[int] = 4096,
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num_encoder_layers: Optional[int] = 12,
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num_encoder_attention_heads: Optional[int] = 16,
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decoder_ffn_dim: Optional[int] = 4096,
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num_decoder_layers: Optional[int] = 12,
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num_decoder_attention_heads: Optional[int] = 16,
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attention_dropout: Optional[float] = 0.1,
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dropout: Optional[float] = 0.1,
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max_position_embeddings: Optional[int] = 512,
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init_std: Optional[float] = 0.02,
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is_encoder_decoder: Optional[bool] = True,
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add_cross_attention: Optional[bool] = True,
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decoder_start_token_id: Optional[int] = 0,
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ngram: Optional[int] = 2,
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num_buckets: Optional[int] = 32,
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relative_max_distance: Optional[int] = 128,
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disable_ngram_loss: Optional[bool] = False,
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eps: Optional[float] = 0.0,
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use_cache: Optional[bool] = True,
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pad_token_id: Optional[int] = 0,
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bos_token_id: Optional[int] = 1,
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eos_token_id: Optional[int] = 2,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.encoder_ffn_dim = encoder_ffn_dim
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self.num_encoder_layers = num_encoder_layers
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self.num_encoder_attention_heads = num_encoder_attention_heads
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self.decoder_ffn_dim = decoder_ffn_dim
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self.num_decoder_layers = num_decoder_layers
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self.num_decoder_attention_heads = num_decoder_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.init_std = init_std # Normal(0, this parameter)
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self.activation_function = activation_function
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# parameters for prophetnet
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self.ngram = ngram
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self.num_buckets = num_buckets
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self.relative_max_distance = relative_max_distance
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self.disable_ngram_loss = disable_ngram_loss
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self.eps = eps
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# 3 Types of Dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.dropout = dropout
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self.use_cache = use_cache
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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is_encoder_decoder=is_encoder_decoder,
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add_cross_attention=add_cross_attention,
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decoder_start_token_id=decoder_start_token_id,
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**kwargs,
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)
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@property
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def num_hidden_layers(self) -> int:
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return self.num_encoder_layers + self.num_decoder_layers
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@num_hidden_layers.setter
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def num_hidden_layers(self, value):
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raise NotImplementedError(
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"This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
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" `num_decoder_layers`."
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)
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