o ei)@sLddlmZmZddlmZddlmZeeZ GdddeZ dgZ dS))PreTrainedConfiglayer_type_validation)RopeParameters)loggingc>seZdZdZdZdgZddddddddddddd Zdgdgfd d gd gfd gd gfd Zd d iZ                              d?d e dBd!e dBd"e dBd#e dBd$e dBd%e dBd&e dBd'e dBd e dBd(e dBd)e dBd*e dBd+e dBd,e dBd-e dBd.e dBd/e dBd0e dBd1e dBd2e dBd3e ee e fBdBd4e dBd5e dBd6e dBd7e dBd8e dBd9ee dBd:e dBd;e dBd ZZS)@ 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_valuescolwiserowwise) zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projz!layers.*.mlp.experts.gate_up_projzlayers.*.mlp.experts.down_projz%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_expertsR*> NFsilu{Gz?ư>T? 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  s|_|_|_|_|_|_|_|_| _| _ | _ |_ |dur*|}| _ | _ |_|_|_|_|_|_|_|_|_|_|_jdurcfddtjD_tjj|_|_|_|_|_t j!di|dS)Ncs(g|]}jdur|jkrdndqS)Nsliding_attentionfull_attention)r:r;).0iselfk/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/dots1/configuration_dots1.py s z(Dots1Config.__init__..rF)"r#r1r$r%r&r'r(r*rr-r.r/r+r,r)r0r2r3r4r7r8r9r:r;r<rangerr5r=r>r?r6super__init__) rEr#r$r%r&r'r(r)r*rr+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?kwargs __class__rDrGrKsL"  zDots1Config.__init__)rrrrrrrNNrrNrFrrrrTFNFr r!r"rNNNN)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_plan attribute_mapintboolstrfloatrdictlistrK __classcell__rFrFrMrGrsY         rN) configuration_utilsrrmodeling_rope_utilsrutilsr get_loggerrOloggerr__all__rFrFrFrGs    F