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# Copyright 2024 The HuggingFace Inc. team.
#
# 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.
"""MAMBA2 configuration"""
import math
from transformers.configuration_utils import PretrainedConfig
class Mamba2Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`Mamba2Model`]. It is used to instantiate a MAMBA2
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 MAMBA2
[state-spaces/mamba2-2.8b](https://huggingface.co/state-spaces/mamba2-2.8b) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_heads (`int`, *optional*, defaults to 64):
Number of heads for the evolution matrices of mamba 2.
head_dim (`int`, *optional*, defaults to 64):
Dimension of each head.
vocab_size (`int`, *optional*, defaults to 32768):
Vocabulary size of the MAMBA2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Mamba2Model`].
hidden_size (`int`, *optional*, defaults to 2048):
Dimensionality of the embeddings and hidden states.
state_size (`int`, *optional*, defaults to 128): shape of the state space latents.
num_hidden_layers (`int`, *optional*, defaults to 48):
Number of hidden layers in the model.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
The epsilon to use in the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the beginning of sentence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the end of sentence token in the vocabulary.
expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel.
n_groups (`int`, *optional*, defaults to 1):
Number of groups for the evolution matrices of mamba 2.
use_bias (`bool`, *optional*, defaults to `False`):
Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block
use_conv_bias (`bool`, *optional*, defaults to `True`):
Whether or not to use bias in the convolution layer of the mixer block.
hidden_act (`str`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.1):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
residual_in_fp32 (`bool`, *optional*, defaults to `True`):
Whether or not residuals should be in `float32`.
If set to `False` residuals will keep the same `dtype` as the rest of the model
time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
Rank of the discretization projection matrix.
`"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
time_step_min (`float`, *optional*, defaults to 0.001):
Minimum `time_step` used to bound `dt_proj.bias`.
time_step_max (`float`, *optional*, defaults to 0.1):
Maximum `time_step` used to bound `dt_proj.bias`.
time_step_floor (`float`, *optional*, defaults to 0.0001):
Minimum clamping value of the `dt_proj.bias` layer initialization.
time_step_limit (`tuple`, *optional*, defaults to `(0.0, inf)`):
Accepted range of time step values.
rescale_prenorm_residual (`bool`, *optional*, defaults to `True`):
Whether or not to rescale `out_proj` weights when initializing.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the cache should be used.
rms_norm (`bool`, *optional*, defaults to `True`):
Whether to use RMS norm or not.
chunk_size (`int`, *optional*, defaults to 256):
Size of the chunks that will comprise the sequence.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie word embeddings or not.
"""
model_type = "mamba2"
def __init__(
self,
num_heads: int = 64,
head_dim: int = 64,
vocab_size: int = 32000,
hidden_size: int = 2048,
state_size: int = 128,
num_hidden_layers: int = 48,
layer_norm_epsilon: float = 1e-5,
pad_token_id: int = 0,
bos_token_id: int = 1,
eos_token_id: int = 2,
expand: int = 2,
conv_kernel: int = 4,
n_groups: int = 1,
use_bias: bool = False,
use_conv_bias: bool = True,
hidden_act: str = "silu",
initializer_range: float = 0.1,
residual_in_fp32: bool = True,
time_step_rank: str = "auto",
time_step_min: float = 0.001,
time_step_max: float = 0.1,
time_step_floor: float = 1e-4,
time_step_limit=(0.0, float("inf")),
rescale_prenorm_residual: bool = True,
use_cache: bool = True,
rms_norm: bool = True,
chunk_size: int = 256,
fuse_norm: bool = True,
fuse_cross_entropy: bool = True,
tie_word_embeddings: bool = False,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.state_size = state_size
self.num_hidden_layers = num_hidden_layers
self.layer_norm_epsilon = layer_norm_epsilon
self.conv_kernel = conv_kernel
self.expand = expand
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.use_bias = use_bias
self.use_conv_bias = use_conv_bias
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.time_step_rank = (
math.ceil(self.hidden_size / 16)
if time_step_rank == "auto"
else time_step_rank
)
self.time_step_min = time_step_min
self.time_step_max = time_step_max
self.time_step_floor = time_step_floor
self.rescale_prenorm_residual = rescale_prenorm_residual
self.residual_in_fp32 = residual_in_fp32
self.use_cache = use_cache
self.n_groups = n_groups
self.num_heads = num_heads
self.head_dim = head_dim
self.rms_norm = rms_norm
self.state_size = state_size
self.chunk_size = chunk_size
self.time_step_limit = time_step_limit
self.fuse_norm = fuse_norm
self.fuse_cross_entropy = fuse_cross_entropy
self.tie_word_embeddings = tie_word_embeddings
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)