o Zh&@s@dZddlmZddlmZeeZGdddeZdgZ dS)zBamba model configuration)PretrainedConfig)loggingcsneZdZdZdZdgZ                     dfdd ZeddZZ S) BambaConfiga9 This is the configuration class to store the configuration of a [`BambaModel`]. It is used to instantiate a BambaModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with defaults taken from [ibm-fms/Bamba-9.8b-2.2T-hf](https://huggingface.co/ibm-fms/Bamba-9.8b-2.2T-hf). The BambaModel is a hybrid [mamba2](https://github.com/state-spaces/mamba) architecture with SwiGLU. The checkpoints are jointly trained by IBM, Princeton, and UIUC. 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 128000): Vocabulary size of the Bamba model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BambaModel`] tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the model has an output word embedding layer. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 8): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. num_logits_to_keep (`int` or `None`, *optional*, defaults to 1): Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the logits of the last prompt token are needed for generation. For long sequences, the logits for the entire sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint significantly. pad_token_id (`int`, *optional*, defaults to 0): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2): The id of the "end-of-sequence" token. max_position_embeddings (`int`, *optional*, defaults to 262144): Max cached sequence length for the model attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. attn_layer_indices (`list`, *optional*): Specifies the layer indices that will have full attention. Must contain values at most num_hidden_layers. mamba_n_heads (`int`, *optional*, defaults to 128): The number of mamba heads used in the v2 implementation. mamba_d_head (`int`, *optional*, defaults to `"auto"`): Head embedding dimension size mamba_n_groups (`int`, *optional*, defaults to 1): The number of the mamba groups used in the v2 implementation. mamba_d_state (`int`, *optional*, defaults to 256): The dimension the mamba state space latents mamba_d_conv (`int`, *optional*, defaults to 4): The size of the mamba convolution kernel mamba_expand (`int`, *optional*, defaults to 2): Expanding factor (relative to hidden_size) used to determine the mamba intermediate size mamba_chunk_size (`int`, *optional*, defaults to 256): The chunks in which to break the sequence when doing prefill/training mamba_conv_bias (`bool`, *optional*, defaults to `True`): Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block. mamba_proj_bias (`bool`, *optional*, defaults to `False`): Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block ZbambaZpast_key_valuesF8 silu{Gz?h㈵>TNautoc s||_||_||_||_||_||_||_||_d|_d|_ |dur$|}||_ ||_ | |_ | |_ | |_| |_||_d|_d|_d|_||}||dkrPtd|dkrX||}|||krbtd||_||_||_||_||_||_||_||_||_tjd | |||d|dS) NFg@g?rz4mamba_n_heads must divide mamba_expand * hidden_sizerzPThe dimensions for the Mamba head state do not match the model intermediate_size) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizer hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsmax_position_embeddingsattention_dropoutZattention_biasZmlp_biasnum_key_value_heads hidden_actinitializer_range rms_norm_eps use_cachenum_logits_to_keepattn_layer_indicesZ rope_thetaZ rope_scalingZpartial_rotary_factor ValueError mamba_n_heads mamba_d_headmamba_n_groups mamba_d_state mamba_d_conv mamba_expandmamba_chunk_sizemamba_conv_biasmamba_proj_biassuper__init__)selfrrrrrrr"r#r$r%r&r'rrrr r!r(r*r+r,r-r.r/r0r1r2kwargsZmamba_intermediate __class__r\/var/www/auris/lib/python3.10/site-packages/transformers/models/bamba/configuration_bamba.pyr4ms\   zBambaConfig.__init__csfddtjDS)Ncs$g|]}jr|jvrdndqS)Z attentionZmamba)r().0ir5rr9 sz1BambaConfig.layers_block_type..)rangerr<rr<r9layers_block_types zBambaConfig.layers_block_type)rFrrrrr r r r Tr rr rrrNrrr rrrrTF) __name__ __module__ __qualname____doc__Z model_typeZkeys_to_ignore_at_inferencer4propertyr? __classcell__rrr7r9rsDQYrN) rCZconfiguration_utilsrutilsrZ get_loggerr@loggerr__all__rrrr9s    7