#!/usr/bin/env python3
# coding=utf-8
# Copyright (c) Facebook, Inc. and Huggingface, 2020
#
# This source code is licensed under the MIT license found in the;
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
BlenderbotConfig has the same signature as BartConfig. We only rewrite the signature in order to document
blenderbot-90M defaults.
"""
from ..bart.configuration_bart import BartConfig
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/blenderbot-3B": "https://cdn.huggingface.co/facebook/blenderbot-3B/config.json",
"facebook/blenderbot-90M": "https://cdn.huggingface.co/facebook/blenderbot-90M/config.json",
}
[docs]class BlenderbotConfig(BartConfig):
r"""
This is the configuration class to store the configuration of a
:class:`~transformers.BlenderbotForConditionalGeneration`. It inherits from :class:`~transformers.BartConfig` and
has the same signature with different defaults.
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:
vocab_size (:obj:`int`, `optional`, defaults to 54944):
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.BlenderbotForConditionalGeneration`.
d_model (:obj:`int`, `optional`, defaults to 512):
Dimensionality of the layers and the pooler layer.
encoder_layers (:obj:`int`, `optional`, defaults to 8):
Number of encoder layers, 6 are used for the `blenderbot-90M` model.
decoder_layers (:obj:`int`, `optional`, defaults to 8):
Number of decoder layers, 6 are used for the `blenderbot-90M` model.
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 2048):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (:obj:`int`, `optional`, defaults to 2048):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
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.
classifier_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (:obj:`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 (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
add_bias_logits (:obj:`bool`, `optional`, defaults to :obj:`False`):
This should be completed, specific to marian.
normalize_before (:obj:`bool`, `optional`, defaults to :obj:`False`):
Call layernorm before attention ops.
normalize_embedding (:obj:`bool`, `optional`, defaults to :obj:`True`):
Call layernorm after embeddings.
static_position_embeddings (:obj:`bool`, `optional`, defaults to :obj:`False`):
Don't learn positional embeddings, use sinusoidal.
add_final_layer_norm (:obj:`bool`, `optional`, defaults to :obj:`False`):
Why not add another layernorm?
do_blenderbot_90_layernorm (:obj:`bool`, `optional`, defaults to :obj:`True`):
Blenderbot-90m checkpoint uses `layernorm_embedding` one line earlier in the decoder.
scale_embedding (:obj:`bool`, `optional`, defaults to :obj:`False`):
Scale embeddings by diving by sqrt(d_model).
eos_token_id (:obj:`int`, `optional`, defaults to 2)
End of stream token id.
pad_token_id (:obj:`int`, `optional`, defaults to 1)
Padding token id.
bos_token_id (:obj:`int`, `optional`, defaults to 0)
Beginning of stream token id.
encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
The LayerDrop probability for the encoder. See the `LayerDrop paper <see
https://arxiv.org/abs/1909.11556>`__ for more details.
decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
The LayerDrop probability for the decoder. See the `LayerDrop paper <see
https://arxiv.org/abs/1909.11556>`__ for more details.
extra_pos_embeddings: (:obj:`int`, `optional`, defaults to 2):
How many extra learned positional embeddings to use. Should be set to :obj:`pad_token_id+1`.
is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether this is an encoder/decoder model.
force_bos_token_to_be_generated (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to force BOS token to be generated at step 1 (after ``decoder_start_token_id``),
"""
model_type = "blenderbot"
def __init__(
self,
activation_dropout=0.0,
extra_pos_embeddings=0,
activation_function="gelu",
vocab_size=54944,
d_model=512,
encoder_ffn_dim=2048,
encoder_layers=8,
encoder_attention_heads=16,
decoder_ffn_dim=2048,
decoder_layers=8,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
attention_dropout=0.0,
dropout=0.1,
max_position_embeddings=512,
classifier_dropout=0.0,
is_encoder_decoder=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
normalize_before=False,
add_final_layer_norm=False,
do_blenderbot_90_layernorm=True,
scale_embedding=False,
normalize_embedding=True,
static_position_embeddings=False,
add_bias_logits=False,
force_bos_token_to_be_generated=False,
**common_kwargs
):
r"""
Examples::
>>> from transformers import BlenderbotConfig
>>> config = BlenderbotConfig.from_pretrained('facebook/blenderbot-90M')
"""
if "hidden_size" in common_kwargs:
raise ValueError("hidden size is called d_model")
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,
vocab_size=vocab_size,
d_model=d_model,
encoder_ffn_dim=encoder_ffn_dim,
encoder_layers=encoder_layers,
encoder_layerdrop=encoder_layerdrop,
encoder_attention_heads=encoder_attention_heads,
decoder_layerdrop=decoder_layerdrop,
decoder_ffn_dim=decoder_ffn_dim,
decoder_layers=decoder_layers,
normalize_before=normalize_before,
normalize_embedding=normalize_embedding,
static_position_embeddings=static_position_embeddings,
add_bias_logits=add_bias_logits,
force_bos_token_to_be_generated=force_bos_token_to_be_generated,
do_blenderbot_90_layernorm=do_blenderbot_90_layernorm,
add_final_layer_norm=add_final_layer_norm,
scale_embedding=scale_embedding,
attention_dropout=attention_dropout,
dropout=dropout,
classifier_dropout=classifier_dropout,
activation_dropout=activation_dropout,
max_position_embeddings=max_position_embeddings,
extra_pos_embeddings=extra_pos_embeddings,
activation_function=activation_function,
decoder_attention_heads=decoder_attention_heads,
**common_kwargs,
)