147 lines
6.5 KiB
Python
147 lines
6.5 KiB
Python
# coding=utf-8
|
|
# Copyright 2025 The BitNet Team 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
|
|
"""BitNet model configuration"""
|
|
|
|
from ...configuration_utils import PretrainedConfig
|
|
from ...utils import logging
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
class BitNetConfig(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`BitNetModel`]. It is used to instantiate an BitNet
|
|
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
|
defaults will yield a similar configuration to that of
|
|
BitNet b1.58 2B4T [microsoft/bitnet-b1.58-2B-4T](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T).
|
|
|
|
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 128256):
|
|
Vocabulary size of the BitNet model. Defines the number of different tokens that can be represented by the
|
|
`inputs_ids` passed when calling [`BitNetModel`]
|
|
hidden_size (`int`, *optional*, defaults to 2560):
|
|
Dimension of the hidden representations.
|
|
intermediate_size (`int`, *optional*, defaults to 6912):
|
|
Dimension of the MLP representations.
|
|
num_hidden_layers (`int`, *optional*, defaults to 30):
|
|
Number of hidden layers in the Transformer decoder.
|
|
num_attention_heads (`int`, *optional*, defaults to 20):
|
|
Number of attention heads for each attention layer in the Transformer decoder.
|
|
num_key_value_heads (`int`, *optional*, defaults to 5):
|
|
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
|
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
|
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
|
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
|
by meanpooling all the original heads within that group. For more details, check out [this
|
|
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
|
|
`num_attention_heads`.
|
|
hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
|
|
The non-linear activation function (function or string) in the decoder.
|
|
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
|
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.
|
|
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
|
The epsilon used by the rms normalization layers.
|
|
use_cache (`bool`, *optional*, defaults to `True`):
|
|
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
|
relevant if `config.is_decoder=True`.
|
|
pad_token_id (`int`, *optional*):
|
|
Padding token id.
|
|
bos_token_id (`int`, *optional*, defaults to 128000):
|
|
Beginning of stream token id.
|
|
eos_token_id (`int`, *optional*, defaults to 128001):
|
|
End of stream token id.
|
|
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
|
Whether to tie weight embeddings
|
|
rope_theta (`float`, *optional*, defaults to 500000.0):
|
|
The base period of the 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.
|
|
|
|
```python
|
|
>>> from transformers import BitNetModel, BitNetConfig
|
|
|
|
>>> # Initializing a BitNet style configuration
|
|
>>> configuration = BitNetConfig()
|
|
|
|
>>> # Initializing a model from the BitNet style configuration
|
|
>>> model = BitNetModel(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "bitnet"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=128256,
|
|
hidden_size=2560,
|
|
intermediate_size=6912,
|
|
num_hidden_layers=30,
|
|
num_attention_heads=20,
|
|
num_key_value_heads=5,
|
|
hidden_act="relu2",
|
|
max_position_embeddings=2048,
|
|
initializer_range=0.02,
|
|
rms_norm_eps=1e-5,
|
|
use_cache=True,
|
|
pad_token_id=None,
|
|
bos_token_id=128000,
|
|
eos_token_id=128001,
|
|
tie_word_embeddings=False,
|
|
rope_theta=500000.0,
|
|
attention_bias=False,
|
|
attention_dropout=0.0,
|
|
**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
|
|
|
|
# for backward compatibility
|
|
if num_key_value_heads is None:
|
|
num_key_value_heads = num_attention_heads
|
|
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.hidden_act = hidden_act
|
|
self.initializer_range = initializer_range
|
|
self.rms_norm_eps = rms_norm_eps
|
|
self.use_cache = use_cache
|
|
self.rope_theta = rope_theta
|
|
self.attention_bias = attention_bias
|
|
self.attention_dropout = attention_dropout
|
|
|
|
super().__init__(
|
|
pad_token_id=pad_token_id,
|
|
bos_token_id=bos_token_id,
|
|
eos_token_id=eos_token_id,
|
|
tie_word_embeddings=tie_word_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
__all__ = ["BitNetConfig"]
|