# coding=utf-8 # 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 ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) 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 128): 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 4096): 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 64): 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 1): Padding token id. bos_token_id (`int`, *optional*, defaults to 0): 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 8): 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 `False`): 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. Example: ```python >>> from transformers import Mamba2Config, Mamba2Model >>> # Initializing a Mamba2 configuration >>> configuration = Mamba2Config() >>> # Initializing a model (with random weights) from the configuration >>> model = Mamba2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "mamba2" def __init__( self, num_heads=128, head_dim=64, vocab_size=32768, hidden_size=4096, state_size=128, num_hidden_layers=64, layer_norm_epsilon=1e-5, pad_token_id=1, bos_token_id=0, eos_token_id=2, expand=2, conv_kernel=4, n_groups=8, use_bias=False, use_conv_bias=True, hidden_act="silu", initializer_range=0.1, residual_in_fp32=True, time_step_rank="auto", time_step_min=0.001, time_step_max=0.1, time_step_floor=1e-4, time_step_limit=(0.0, float("inf")), rescale_prenorm_residual=False, use_cache=True, rms_norm=True, chunk_size=256, tie_word_embeddings=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.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, ) __all__ = ["Mamba2Config"]