o ¾e¦i£-ã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zBamba model configurationé)ÚPreTrainedConfig)ÚRopeParameters)ÚloggingcBsŽeZdZdZdZdgZddddddd d d d d ddddddddddddddd ddddedƒfddf dedBdedBdedBdedBdedBd edBd!edBd"e dBd#edBd$edBd%edBd&edBd'edBd(edBd)edBd*edBd+edBd,e edBd-edBd.e dBd/edBd0edBd1edBd2edBd3edBd4edBd5edBd6edBd7edBd8e eefdBd9edBd:e dBf@‡fd;d<„ Z ed=d>„ƒZ‡ZS)?Ú 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`. ÚbambaÚpast_key_valuesiôFii8é éÚsilug{®Gáz”?gñh㈵øä>TéééigNé€Úautoéégü©ñÒMbP?gš™™™™™¹?ÚinfÚ vocab_sizeÚtie_word_embeddingsÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚ hidden_actÚinitializer_rangeÚ rms_norm_epsÚ use_cacheÚnum_logits_to_keepÚ pad_token_idÚ bos_token_idÚ eos_token_idÚmax_position_embeddingsÚattention_dropoutÚattn_layer_indicesÚ mamba_n_headsÚ mamba_d_headÚmamba_n_groupsÚ mamba_d_stateÚ mamba_d_convÚ mamba_expandÚmamba_chunk_sizeÚmamba_conv_biasÚmamba_proj_biasÚ time_step_minÚ time_step_maxÚtime_step_limitÚz_loss_coefficientÚrope_parametersc!# sL||_||_||_||_||_||_||_||_d|_d|_ |dur$|}||_ ||_ | |_ | |_ | |_| |_||_||}"|"|dkrGtdƒ‚|dkrO|"|}|||"krYtdƒ‚||_||_||_||_||_||_||_||_||_||_||_|dur‚t|ƒnd|_||_| |_ d|!d<||_| |_!||_"||_#t$ƒj%di|!¤ŽdS) NFr z4mamba_n_heads must divide mamba_expand * hidden_sizerzPThe dimensions for the Mamba head state do not match the model intermediate_sizegà?Úpartial_rotary_factor©)&rrrrrrr"r#Úattention_biasÚmlp_biasrrrrrrr$Ú ValueErrorr%r&r'r(r)r*r+r,r-r.r/Útupler0r1r2rr r!ÚsuperÚ__init__)#Úselfrrrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2ÚkwargsÚmamba_intermediate©Ú __class__r4úk/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/bamba/configuration_bamba.pyr:xs\$  zBambaConfig.__init__cs‡fdd„tˆjƒDƒS)Ncs$g|]}ˆjr|ˆjvrdnd‘qS)Ú attentionÚmamba)r$)Ú.0Úi©r;r4r@Ú Øsÿÿz1BambaConfig.layers_block_type..)ÚrangerrEr4rEr@Úlayers_block_typeÖs þzBambaConfig.layers_block_type)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚfloatÚintÚboolÚstrÚlistr8rr:ÚpropertyrHÚ __classcell__r4r4r>r@rsÎ\ ßþýüûúùø ÷ ö õ ô óòñðïî íìëêéèçæåäãâá à!ß^rN) rLÚconfiguration_utilsrÚmodeling_rope_utilsrÚutilsrÚ get_loggerrIÚloggerrÚ__all__r4r4r4r@Ús     G