qi-ldZddlmZddlmZddlmZejeZ GddeZ dgZ y)zBamba model configuration)PreTrainedConfig)RopeParameters)loggingcBeZdZdZdZdgZddddddd d d d d ddddddddddddddd ddddedfddf dedzdedzdedzdedzdedzd edzd!edzd"e dzd#edzd$edzd%edzd&edzd'edzd(edzd)edzd*edzd+edzd,e edzd-edzd.e dzd/edzd0edzd1edzd2edzd3edzd4edzd5edzd6edzd7edzd8e eefdzd9edzd:e dzf@fd; Z ed<ZxZS)= BambaConfiga 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, check out [this paper](https://huggingface.co/papers/2305.13245). 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 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_limit (`tuple`, *optional*, defaults to `(0.0, inf)`): Accepted range of time step values for clamping. z_loss_coefficient (`float`, *optional*, defaults to 0.0): Coefficient for auxiliary z-loss used to control logit growth during training rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. bambapast_key_valuesiFii8 silug{Gz?gh㈵>TigNautogMbP?g?inf vocab_sizetie_word_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actinitializer_range rms_norm_eps use_cachenum_logits_to_keep pad_token_id bos_token_id eos_token_idmax_position_embeddingsattention_dropoutattn_layer_indices mamba_n_heads mamba_d_headmamba_n_groups mamba_d_state mamba_d_conv mamba_expandmamba_chunk_sizemamba_conv_biasmamba_proj_bias time_step_min time_step_maxtime_step_limitz_loss_coefficientrope_parametersc! ||_||_||_||_||_||_||_||_d|_d|_ ||}||_ ||_ | |_ | |_ | |_| |_||_||z}"|"|zdk7r t#d|dk(r|"|z}||z|"k7r t#d||_||_||_||_||_||_||_||_||_||_||_| t;|nd|_||_| |_ d|!d<||_| |_!||_"||_#tI#|di|!y) NFrz4mamba_n_heads must divide mamba_expand * hidden_sizerzPThe dimensions for the Mamba head state do not match the model intermediate_sizeg?partial_rotary_factor)&rrrrrrr$r%attention_biasmlp_biasrrrrrr r& ValueErrorr'r(r)r*r+r,r-r.r/r0r1tupler2r3r4r!r"r#super__init__)$selfrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4kwargsmamba_intermediate __class__s$ _/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/bamba/configuration_bamba.pyr=zBambaConfig.__init__xsH%#6 &!2!2#6 '>$!2#   &"5 #6 $!2(""4"4)K7  - 2ST T 6 !->L - '+= =op p*(,*(( 0..**9H9Tu_5Z^"4.*-&'#6 ((( "6"ct|jDcgc] }|jr||jvrdnd"c}Scc}w)N attentionmamba)rangerr&)r>is rBlayers_block_typezBambaConfig.layers_block_typesJ4112 !33T=T=T8TK[b b   s%A)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencefloatintboolstrlistr;rr=propertyrI __classcell__)rAs@rBrrsZxJ#4"5"(+0"&(-(**,*+!'*.%)!%)*#$#$#$.4*-/3$'#)%&$'#$#$'*'+',&+&)7:E%L6I+.15C\#$J\#"D[\#4Z \# : \# : \#!4Z\#!4Z\#$J\#!4<\#dl\#$;\# $J\#Dj\#Dj\# Dj!\#""%t#\#$!4<%\#&!I,'\#(Tz)\#*Dj+\#,d -\#.Tz/\#0Dj1\#2Dj3\#4*5\#67\#89\#:t|;\#<t|=\#>ue|,t3?\#@"DLA\#B($.C\#|  rCrN) rMconfiguration_utilsrmodeling_rope_utilsrutilsr get_loggerrJloggerr__all__r7rCrBr]s@ 31   H %C "C L /rC