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# coding=utf-8
# TODO: Add license
#
# 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.
""" PagnolXl configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class PagnolXlConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PagnolXlModel`]. It is used to instantiate a PagnolXl
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 [PagnolXl]() architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 65024):
Vocabulary size of the PagnolXl model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`PagnolXlModel`]
d_model (`int`, *optional*, defaults to 4544):
Dimension of the hidden representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
n_heads (`int`, *optional*, defaults to 71):
Number of attention heads for each attention layer in the Transformer encoder.
sigma (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether the model should return the last key/values attentions (not used by all models). Only relevant if
`config.is_decoder=True`.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for MLP layers.
bos_token_id (`int`, *optional*, defaults to 11):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 11):
The id of the "end-of-sequence" token.
Example:
```python
>>> from transformers import PagnolXlModel, PagnolXlConfig
>>> # Initializing a small (2-layer) PagnolXl configuration
>>> configuration = PagnolXlConfig(num_hidden_layers=2)
>>> # Initializing a model from the small configuration
>>> model = PagnolXlModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "pagnolxl"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=65024,
activation_function="gelu",
d_model=4544,
d_feedforward=18176,
n_heads=71,
n_layers=32,
layer_norm_epsilon=1e-5,
sigma=0.02,
use_cache=True,
dropout=0.0,
bos_token_id=11,
eos_token_id=11,
**kwargs,
):
self.vocab_size = vocab_size
# Backward compatibility with n_embed kwarg
n_embed = kwargs.pop("n_embed", None)
self.activation_function = activation_function
self.d_model = d_model if n_embed is None else n_embed
self.d_feedforward = d_feedforward
self.n_heads = n_heads
self.n_layers = n_layers
self.layer_norm_epsilon = layer_norm_epsilon
self.sigma = sigma
self.use_cache = use_cache
self.dropout = dropout
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@property
def head_dim(self):
return self.d_model // self.n_heads
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