# 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. """FALCONMAMBA configuration""" import math from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class FalconMambaConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`FalconMambaModel`]. It is used to instantiate a FALCON_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 FALCON_MAMBA [tiiuae/falcon-mamba-7b](https://huggingface.co/tiiuae/falcon-mamba-7b) 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*, defaults to 50280): Vocabulary size of the FALCON_MAMBA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FalconMambaModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the embeddings and hidden states. state_size (`int`, *optional*, defaults to 16): shape of the state space latents. num_hidden_layers (`int`, *optional*, defaults to 32): 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 0): The id of the beginning of sentence token in the vocabulary. eos_token_id (`int`, *optional*, defaults to 0): 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. 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_scale (`float`, *optional*, defaults to 1.0): Scale used used to scale `dt_proj.bias`. 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_init_scheme (`float`, *optional*, defaults to `"random"`): Init scheme used for `dt_proj.weight`. Should be one of `["random","uniform"]` time_step_floor (`float`, *optional*, defaults to 0.0001): Minimum clamping value of the `dt_proj.bias` layer initialization. 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. use_mambapy (`bool`, *optional*, defaults to `False`): Determines the fallback strategy during training if the CUDA-based official implementation of FalconMamba is not available. If `True`, the falcon_mamba.py implementation is used. If `False`, the naive and slower implementation is used. Consider switching to the naive version if memory is limited. mixer_rms_eps (`float`, *optional*, defaults to 1e-06): The RMS norm epsilon value that is used in the Mixer RMS norm for B, C and dt states. Example: ```python >>> from transformers import FalconMambaConfig, FalconMambaModel >>> # Initializing a FalconMamba configuration >>> configuration = FalconMambaConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = FalconMambaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "falcon_mamba" def __init__( self, vocab_size=50280, hidden_size=768, state_size=16, num_hidden_layers=32, layer_norm_epsilon=1e-5, pad_token_id=0, bos_token_id=0, eos_token_id=0, expand=2, conv_kernel=4, use_bias=False, use_conv_bias=True, hidden_act="silu", initializer_range=0.1, residual_in_fp32=True, time_step_rank="auto", time_step_scale=1.0, time_step_min=0.001, time_step_max=0.1, time_step_init_scheme="random", time_step_floor=1e-4, rescale_prenorm_residual=False, use_cache=True, use_mambapy=False, mixer_rms_eps=1e-6, **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.use_mambapy = use_mambapy self.mixer_rms_eps = mixer_rms_eps super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs) __all__ = ["FalconMambaConfig"]