gfdZddlZddlmZddlmZejeZGddeZ dS)zMAMBA2 configurationN)PretrainedConfig)loggingcveZdZdZdZddddddddd d d d d d dddddddddedfd dddd ffd ZxZS) Mamba2Configal 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 ```mamba2@iigh㈵>rFTsilug?autogMbP?g-C6?ginfc ||_||_||_||_||_| |_| |_| |_| |_||_ ||_ ||_ ||_ ||_ |dkrtj|jdz n||_||_||_||_||_||_||_| |_||_||_||_||_||_||_||_t=jd| | ||d|dS)Nr) bos_token_id eos_token_id pad_token_idtie_word_embeddings) vocab_size hidden_size state_sizenum_hidden_layerslayer_norm_epsilon conv_kernelexpandrrruse_bias use_conv_bias hidden_actinitializer_rangemathceiltime_step_rank time_step_min time_step_maxtime_step_floorrescale_prenorm_residualresidual_in_fp32 use_cachen_groups num_headshead_dimrms_norm chunk_sizetime_step_limitrsuper__init__)selfr/r0rrrrrrrrr rr.r!r"r#r$r,r'r(r)r*r3r+r-r1r2rkwargs __class__s k/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/mamba2/configuration_mamba2.pyr5zMamba2Config.__init__osJ@%&$!2"4& (((  *$!2BPTZBZBZdi(82(=>>>`n**.(@% 0"  "    $$.#6  %%% 3         )__name__ __module__ __qualname____doc__ model_typefloatr5 __classcell__)r8s@r9rrsP P dJeeEll+!&!;E E E E E E E E E E r:r) r>r%configuration_utilsrutilsr get_loggerr;loggerrrr:r9rFs 333333  H % %Z Z Z Z Z #Z Z Z Z Z r: