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# This file was automatically generated from src/transformers/models/modernbert/modular_modernbert.py.
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# Copyright 2024 Answer.AI, LightOn, and contributors, and the HuggingFace Inc. team. All rights reserved.
#
#
# 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.
from typing import Literal
from transformers.configuration_utils import PretrainedConfig
class ModernBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ModernBertModel`]. It is used to instantiate an ModernBert
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 ModernBERT-base.
e.g. [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
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 50368):
Vocabulary size of the ModernBert model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ModernBertModel`]
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 1152):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 22):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer decoder.
hidden_activation (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the decoder. Will default to `"gelu"`
if not specified.
max_position_embeddings (`int`, *optional*, defaults to 8192):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_cutoff_factor (`float`, *optional*, defaults to 2.0):
The cutoff factor for the truncated_normal_initializer for initializing all weight matrices.
norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
norm_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the normalization layers.
pad_token_id (`int`, *optional*, defaults to 50283):
Padding token id.
eos_token_id (`int`, *optional*, defaults to 50282):
End of stream token id.
bos_token_id (`int`, *optional*, defaults to 50281):
Beginning of stream token id.
cls_token_id (`int`, *optional*, defaults to 50281):
Classification token id.
sep_token_id (`int`, *optional*, defaults to 50282):
Separation token id.
global_rope_theta (`float`, *optional*, defaults to 160000.0):
The base period of the global RoPE embeddings.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
global_attn_every_n_layers (`int`, *optional*, defaults to 3):
The number of layers between global attention layers.
local_attention (`int`, *optional*, defaults to 128):
The window size for local attention.
local_rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the local RoPE embeddings.
embedding_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the embeddings.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the MLP layers.
mlp_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the MLP layers.
decoder_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in the decoder layers.
classifier_pooling (`str`, *optional*, defaults to `"cls"`):
The pooling method for the classifier. Should be either `"cls"` or `"mean"`. In local attention layers, the
CLS token doesn't attend to all tokens on long sequences.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the classifier.
classifier_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the classifier.
classifier_activation (`str`, *optional*, defaults to `"gelu"`):
The activation function for the classifier.
deterministic_flash_attn (`bool`, *optional*, defaults to `False`):
Whether to use deterministic flash attention. If `False`, inference will be faster but not deterministic.
sparse_prediction (`bool`, *optional*, defaults to `False`):
Whether to use sparse prediction for the masked language model instead of returning the full dense logits.
sparse_pred_ignore_index (`int`, *optional*, defaults to -100):
The index to ignore for the sparse prediction.
reference_compile (`bool`, *optional*):
Whether to compile the layers of the model which were compiled during pretraining. If `None`, then parts of
the model will be compiled if 1) `triton` is installed, 2) the model is not on MPS, 3) the model is not
shared between devices, and 4) the model is not resized after initialization. If `True`, then the model may
be faster in some scenarios.
Examples:
```python
>>> from transformers import ModernBertModel, ModernBertConfig
>>> # Initializing a ModernBert style configuration
>>> configuration = ModernBertConfig()
>>> # Initializing a model from the modernbert-base style configuration
>>> model = ModernBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "modernbert"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=50368,
hidden_size=768,
intermediate_size=1152,
num_hidden_layers=22,
num_attention_heads=12,
hidden_activation="gelu",
max_position_embeddings=8192,
initializer_range=0.02,
initializer_cutoff_factor=2.0,
norm_eps=1e-5,
norm_bias=False,
pad_token_id=50283,
eos_token_id=50282,
bos_token_id=50281,
cls_token_id=50281,
sep_token_id=50282,
global_rope_theta=160000.0,
attention_bias=False,
attention_dropout=0.0,
global_attn_every_n_layers=3,
local_attention=128,
local_rope_theta=10000.0,
embedding_dropout=0.0,
mlp_bias=False,
mlp_dropout=0.0,
decoder_bias=True,
classifier_pooling: Literal["cls", "mean"] = "cls",
classifier_dropout=0.0,
classifier_bias=False,
classifier_activation="gelu",
deterministic_flash_attn=False,
sparse_prediction=False,
sparse_pred_ignore_index=-100,
reference_compile=None,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
cls_token_id=cls_token_id,
sep_token_id=sep_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.initializer_range = initializer_range
self.initializer_cutoff_factor = initializer_cutoff_factor
self.norm_eps = norm_eps
self.norm_bias = norm_bias
self.global_rope_theta = global_rope_theta
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.hidden_activation = hidden_activation
self.global_attn_every_n_layers = global_attn_every_n_layers
self.local_attention = local_attention
self.local_rope_theta = local_rope_theta
self.embedding_dropout = embedding_dropout
self.mlp_bias = mlp_bias
self.mlp_dropout = mlp_dropout
self.decoder_bias = decoder_bias
self.classifier_pooling = classifier_pooling
self.classifier_dropout = classifier_dropout
self.classifier_bias = classifier_bias
self.classifier_activation = classifier_activation
self.deterministic_flash_attn = deterministic_flash_attn
self.sparse_prediction = sparse_prediction
self.sparse_pred_ignore_index = sparse_pred_ignore_index
self.reference_compile = reference_compile
if self.classifier_pooling not in ["cls", "mean"]:
raise ValueError(
f'Invalid value for `classifier_pooling`, should be either "cls" or "mean", but is {self.classifier_pooling}.'
)
__all__ = ["ModernBertConfig"]