c130e67dce
* add forced logits processors * delete adjust_logits method * add forced_eos_token_id argument in config * add tests for forced logits processors * update gen utils tests * add forced option to tf generate * remove adjust_logits method from tf models * update adjust_logits for marian * delete _force_token_id_to_be_generated method * style * import warnings * pass max_length to _get_logits_processor * set forced_eos_token_id to None * set forced attributes in conf utils * typo * fix rag generate * add forced_eos_token_id in rag config * remove force_bos_token_to_be_generated from BartConfig * remove _force_token_ids_generation from FSMT * nit * fix negative constant * apply suggestions from code review
189 lines
8.8 KiB
Python
189 lines
8.8 KiB
Python
# coding=utf-8
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# Copyright 2021 The Fairseq Authors 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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""" BART model configuration """
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import warnings
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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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BART_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
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# See all BART models at https://huggingface.co/models?filter=bart
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}
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class BartConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a :class:`~transformers.BartModel`. It is used to
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instantiate a BART 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 BART `facebook/bart-large
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<https://huggingface.co/facebook/bart-large>`__ architecture.
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Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
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outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
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Args:
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vocab_size (:obj:`int`, `optional`, defaults to 50265):
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Vocabulary size of the BART model. Defines the number of different tokens that can be represented by the
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:obj:`inputs_ids` passed when calling :class:`~transformers.BartModel` or
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:class:`~transformers.TFBartModel`.
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d_model (:obj:`int`, `optional`, defaults to 1024):
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Dimensionality of the layers and the pooler layer.
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encoder_layers (:obj:`int`, `optional`, defaults to 12):
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Number of encoder layers.
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decoder_layers (:obj:`int`, `optional`, defaults to 12):
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Number of decoder layers.
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encoder_attention_heads (:obj:`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 (:obj:`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 (:obj:`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 (:obj:`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 (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string,
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:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported.
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dropout (:obj:`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 (:obj:`float`, `optional`, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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activation_dropout (:obj:`float`, `optional`, defaults to 0.0):
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The dropout ratio for activations inside the fully connected layer.
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classifier_dropout (:obj:`float`, `optional`, defaults to 0.0):
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The dropout ratio for classifier.
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max_position_embeddings (:obj:`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 (:obj:`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: (:obj:`float`, `optional`, defaults to 0.0):
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The LayerDrop probability for the encoder. See the `LayerDrop paper <see
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https://arxiv.org/abs/1909.11556>`__ for more details.
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decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
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The LayerDrop probability for the decoder. See the `LayerDrop paper <see
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https://arxiv.org/abs/1909.11556>`__ for more details.
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gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
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If True, use gradient checkpointing to save memory at the expense of slower backward pass.
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scale_embedding (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Scale embeddings by diving by sqrt(d_model).
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use_cache (:obj:`bool`, `optional`, defaults to :obj:`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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num_labels: (:obj:`int`, `optional`, defaults to 3):
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The number of labels to use in :class:`~transformers.BartForSequenceClassification`.
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forced_eos_token_id (:obj:`int`, `optional`, defaults to 2):
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The id of the token to force as the last generated token when :obj:`max_length` is reached. Usually set to
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:obj:`eos_token_id`.
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Example::
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>>> from transformers import BartModel, BartConfig
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>>> # Initializing a BART facebook/bart-large style configuration
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>>> configuration = BartConfig()
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>>> # Initializing a model from the facebook/bart-large style configuration
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>>> model = BartModel(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 = "bart"
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keys_to_ignore_at_inference = ["past_key_values"]
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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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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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classifier_dropout=0.0,
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scale_embedding=False,
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gradient_checkpointing=False,
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use_cache=True,
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num_labels=3,
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pad_token_id=1,
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bos_token_id=0,
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eos_token_id=2,
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is_encoder_decoder=True,
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decoder_start_token_id=2,
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forced_eos_token_id=2,
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**kwargs
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):
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super().__init__(
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num_labels=num_labels,
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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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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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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.classifier_dropout = classifier_dropout
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self.use_cache = use_cache
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self.num_hidden_layers = encoder_layers
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self.gradient_checkpointing = gradient_checkpointing
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self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
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# ensure backward compatibilty for BART CNN models
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if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
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self.forced_bos_token_id = self.bos_token_id
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warnings.warn(
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f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions."
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"The config can simply be saved and uploaded again to be fixed."
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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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