165 lines
7.4 KiB
Python
165 lines
7.4 KiB
Python
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# coding=utf-8
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# Copyright 2021, Google and The HuggingFace Inc. team. All rights reserved.
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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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""" PEGASUS model configuration"""
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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 PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class PegasusConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`PegasusModel`]. It is used to instantiate an
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PEGASUS model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the PEGASUS
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[google/pegasus-large](https://huggingface.co/google/pegasus-large) 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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vocab_size (`int`, *optional*, defaults to 50265):
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Vocabulary size of the PEGASUS model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`PegasusModel`] or [`TFPegasusModel`].
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d_model (`int`, *optional*, defaults to 1024):
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Dimensionality of the layers and the pooler layer.
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encoder_layers (`int`, *optional*, defaults to 12):
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Number of encoder layers.
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decoder_layers (`int`, *optional*, defaults to 12):
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Number of decoder layers.
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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_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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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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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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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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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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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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activation_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for activations inside the fully connected layer.
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max_position_embeddings (`int`, *optional*, defaults to 1024):
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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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encoder_layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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decoder_layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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scale_embedding (`bool`, *optional*, defaults to `False`):
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Scale embeddings by diving by sqrt(d_model).
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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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forced_eos_token_id (`int`, *optional*, defaults to 1):
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The id of the token to force as the last generated token when `max_length` is reached. Usually set to
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`eos_token_id`.
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Example:
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```python
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>>> from transformers import PegasusConfig, PegasusModel
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>>> # Initializing a PEGASUS google/pegasus-large style configuration
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>>> configuration = PegasusConfig()
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>>> # Initializing a model (with random weights) from the google/pegasus-large style configuration
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>>> model = PegasusModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "pegasus"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
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def __init__(
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self,
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vocab_size=50265,
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max_position_embeddings=1024,
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encoder_layers=12,
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encoder_ffn_dim=4096,
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encoder_attention_heads=16,
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decoder_layers=12,
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decoder_ffn_dim=4096,
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decoder_attention_heads=16,
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encoder_layerdrop=0.0,
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decoder_layerdrop=0.0,
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use_cache=True,
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is_encoder_decoder=True,
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activation_function="gelu",
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d_model=1024,
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dropout=0.1,
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attention_dropout=0.0,
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activation_dropout=0.0,
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init_std=0.02,
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decoder_start_token_id=0,
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scale_embedding=False,
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pad_token_id=0,
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eos_token_id=1,
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forced_eos_token_id=1,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.d_model = d_model
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self.encoder_ffn_dim = encoder_ffn_dim
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self.encoder_layers = encoder_layers
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self.encoder_attention_heads = encoder_attention_heads
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self.decoder_ffn_dim = decoder_ffn_dim
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self.decoder_layers = decoder_layers
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self.decoder_attention_heads = decoder_attention_heads
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.activation_function = activation_function
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self.init_std = init_std
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self.encoder_layerdrop = encoder_layerdrop
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self.decoder_layerdrop = decoder_layerdrop
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self.use_cache = use_cache
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self.num_hidden_layers = encoder_layers
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self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
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super().__init__(
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pad_token_id=pad_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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decoder_start_token_id=decoder_start_token_id,
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forced_eos_token_id=forced_eos_token_id,
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**kwargs,
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)
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@property
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def num_attention_heads(self) -> int:
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return self.encoder_attention_heads
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@property
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def hidden_size(self) -> int:
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return self.d_model
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