# 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. """MAMBA configuration""" import math from transformers.configuration_utils import PretrainedConfig class MambaConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`MambaModel`]. It is used to instantiate a MAMBA 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 MAMBA [state-spaces/mamba-2.8b](https://huggingface.co/state-spaces/mamba-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: vocab_size (`int`, *optional*): Vocabulary size of the Mamba model. hidden_size (`int`, *optional*): Dimensionality of the embeddings and hidden states. Default: 2048. state_size (`int`, *optional*): Shape of the state space latents. Default: 16. num_hidden_layers (`int`, *optional*): Number of hidden layers in the model. Default: 48. layer_norm_epsilon (`float`, *optional*): The epsilon to use in the layer normalization layers. Default: 1e-5. pad_token_id (`int`, *optional*): Padding token id. Default: 0. bos_token_id (`int`, *optional*): The id of the beginning of sentence token in the vocabulary. Default: 0. eos_token_id (`int`, *optional*): The id of the end of sentence token in the vocabulary. Default: 0. expand (`int`, *optional*): Expanding factor used to determine the intermediate size. Default: 2. conv_kernel (`int`, *optional*): Size of the convolution kernel. Default: 4. use_bias (`bool`, *optional*): Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block. Default: `False`. use_conv_bias (`bool`, *optional*): Whether or not to use bias in the convolution layer of the mixer block. Default: `True`. hidden_act (`str`, *optional*): The non-linear activation function (function or string) in the decoder. Default: `"silu"`. initializer_range (`float`, *optional*): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Default: 0.1. residual_in_fp32 (`bool`, *optional*): Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model. Default: `True`. time_step_rank (`Union[int,str]`, *optional*): Rank of the the discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`. Default: `"auto"`. time_step_scale (`float`, *optional*): Scale used used to scale `dt_proj.bias`. Default: 1.0. time_step_min (`float`, *optional*): Minimum `time_step` used to bound `dt_proj.bias`. Default: 0.001. time_step_max (`float`, *optional*): Maximum `time_step` used to bound `dt_proj.bias`. Default: 0.1. time_step_init_scheme (`float`, *optional*): Init scheme used for `dt_proj.weight`. Should be one of `["random","uniform"]`. Default: `"random"`. time_step_floor (`float`, *optional*): Minimum clamping value of the `dt_proj.bias` layer initialization. Default: 0.0001. window_size (`int`, *optional*): The window size used for sliding window attention. Default: 2048. rescale_prenorm_residual (`bool`, *optional*): Whether or not to rescale `out_proj` weights when initializing. Default: `False`. use_cache (`bool`, *optional*): Whether or not the cache should be used. Default: `True`. Example: ```python >>> from transformers import MambaConfig, MambaModel >>> # Initializing a Mamba configuration >>> configuration = MambaConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = MambaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "mamba" def __init__( self, vocab_size: int = 32000, hidden_size: int = 2048, state_size: int = 16, num_hidden_layers: int = 48, layer_norm_epsilon=1e-5, pad_token_id: int = 0, bos_token_id: int = 1, eos_token_id: int = 2, expand: int = 2, conv_kernel: int = 4, use_bias: bool = False, use_conv_bias: bool = True, hidden_act: str = "silu", initializer_range: str = 0.1, residual_in_fp32: bool = False, time_step_rank: str = "auto", time_step_scale: float = 1.0, time_step_min: float = 0.001, time_step_max: float = 0.1, time_step_init_scheme: str = "random", time_step_floor: float = 1e-4, rescale_prenorm_residual: bool = False, use_cache: bool = True, 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.intermediate_size = int(expand * self.hidden_size) 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_scale = time_step_scale self.time_step_min = time_step_min self.time_step_max = time_step_max self.time_step_init_scheme = time_step_init_scheme 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.fuse_norm = fuse_norm self.fuse_cross_entropy = fuse_cross_entropy 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 )