qi*lddlmZmZddlmZddlmZejeZ GddeZ dgZ y))PreTrainedConfiglayer_type_validation)RopeParameters)loggingc>eZdZdZdZdgZdddddddddddddddd Zd gd gfd d gd gfd gd gfdZddiZ d0de dzde dzde dzde dzde dzde dzde dzde dzde dzde dzde dzde dzde dzde dzde dzd e dzd!e dzd"e dzd#e dzd$e dzd%e ee e fzdzd&e dzd'e dzd(e dzd)e dzd*e dzd+ee dzd,e dzd-e dzd.e dzf<fd/ ZxZS)1 Dots1Configa= This is the configuration class to store the configuration of a [`Dots1Model`]. It is used to instantiate a `dots.llm1` model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of [rednote-hilab/dots.llm1.base](https://huggingface.co/rednote-hilab/dots.llm1.base). 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 152064): Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids` passed when calling [`Dots1Model`]. hidden_size (`int`, *optional*, defaults to 4608): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 10944): Dimension of the MLP representations. moe_intermediate_size (`int`, *optional*, defaults to 1408): Dimension of the MoE representations. num_hidden_layers (`int`, *optional*, defaults to 62): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 32): Number of key/value heads for Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, Multi Head Attention (MHA) is used. If `num_key_value_heads=1`, Multi Query Attention (MQA) is used. Otherwise, Grouped Query Attention (GQA) is used. If not specified, defaults to `num_attention_heads`. n_shared_experts (`int`, *optional*, default=None): Number of shared experts. None means dense model. n_routed_experts (`int`, *optional*, default=None): Number of routed experts. None means dense model. n_group (`int`, *optional*, defaults to 1): Number of groups for routed experts. topk_group (`int`, *optional*, defaults to 1): Number of selected groups for each token (selected experts only within `topk_group` groups). num_experts_per_tok (`int`, *optional*, default=None): Number of selected experts. None means dense model. first_k_dense_replace (`int`, *optional*, defaults to 0): Number of dense layers at the beginning of the model before the first MoE layer. norm_topk_prob (`bool`, *optional*, defaults to `False`): Whether to normalize the weights of the routed experts. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string). max_position_embeddings (`int`, *optional*, defaults to 2048): Maximum sequence length the model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): Standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): 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. Only relevant if `config.is_decoder=True`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie the input and output word embeddings. 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`. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the self-attention projections. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout ratio for the attention probabilities. routed_scaling_factor (`float`, *optional*, defaults to 1.0): Scaling factor for routed experts. sliding_window (`int`, *optional*, defaults to 4096): Size of the sliding window for attention. If not specified, defaults to `4096`. max_window_layers (`int`, *optional*, defaults to 62): The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any additional layer afterwards will use SWA (Sliding Window Attention). layer_types (`list`, *optional*): Attention pattern for each layer. pad_token_id (`int`, *optional*): Padding token id. bos_token_id (`int`, *optional*): Beginning of stream token id. eos_token_id (`int`, *optional*): End of stream token id. Examples: ```python >>> from transformers import Dots1Model, Dots1Config >>> # Initializing a Dots1 style configuration >>> configuration = Dots1Config() >>> # Accessing the model configuration >>> configuration = model.config ``` dots1past_key_valuescolwiserowwisereplicated_with_grad_allreducepacked_colwisemoe_tp_experts)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.self_attn.q_normzlayers.*.self_attn.k_normz!layers.*.mlp.experts.gate_up_projzlayers.*.mlp.experts.down_projzlayers.*.mlp.expertsz%layers.*.mlp.shared_experts.gate_projz#layers.*.mlp.shared_experts.up_projz%layers.*.mlp.shared_experts.down_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnormnum_local_expertsn_routed_expertsN vocab_size hidden_sizeintermediate_sizemoe_intermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_headsn_shared_expertsn_group topk_groupnum_experts_per_tokfirst_k_dense_replacenorm_topk_prob hidden_actmax_position_embeddingsinitializer_range rms_norm_eps use_cachetie_word_embeddingsrope_parametersattention_biasattention_dropoutrouted_scaling_factorsliding_windowmax_window_layers layer_types pad_token_id bos_token_id eos_token_idc ||_||_||_||_||_||_||_||_| |_| |_ | |_ ||_ ||}| |_ | |_ ||_||_||_||_||_||_||_||_||_||_||_|j0Et3|j D cgc]!} |j,| |j.k\rdnd#c} |_t5|j0|j ||_||_||_||_||_tA!|di|ycc} w)Nsliding_attentionfull_attention)"rr'rrrrrr rr#r$r%r!r"rr&r(r)r*r-r.r/r0r1r2rangerr+r3r4r5r,super__init__)"selfrrrrrrrr rr!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5kwargsi __class__s" _/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/dots1/configuration_dots1.pyr<zDots1Config.__init__sD%'>$&!2%:"!2#6 0 0#6 %:",  &"5  $#6 $!2(",!2%:",!2&    # t556  &&2qD rCNNrDNFsiluig{Gz?gư>TFNFgg?irBNNNN)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_plan attribute_mapintboolstrfloatrdictlistr< __classcell__)r@s@rArrsWrJ#4"5&/%.%.%.%E%E-=*3 01:/81:"+ )"+&&(9:#%568IJ!"_$56 /M "("&(-,0(**,*,'+'+!"*.,-&+!'.2*.#'!%+0MQ&+*-.1%)(*(,#'#'#'?M#$JM#4ZM#: M# #Tz M# : M#!4ZM#!4ZM#*M#*M#tM#$JM#!4ZM# #TzM#t M# $J!M#""%t#M#$!4<%M#&Dj'M#($;)M#*"D[+M#,($sN/B*CCdJ-M#.t /M#0!4<1M#2 %t|3M#4d 5M#6:7M#8#Y%9M#:Dj;M#<Dj=M#>Dj?M#M#rN) configuration_utilsrrmodeling_rope_utilsrutilsr get_loggerrGloggerr__all__r9rWrAr^s>K1   H %E#"E#P /rW