o Zh)@@sdddlmZmZddlmZddlmZddlmZm Z GdddeZ Gd d d eZ d dgZ d S) )DictOptional)PretrainedConfig)rope_config_validation)CONFIG_MAPPING AutoConfigc seZdZdZdZdgZddddddddZdgdgfd d gd gfd gd gfd Zd Z                        d"de de de de ffd d! Z Z S)#AriaTextConfiga> This class handles the configuration for the text component of the Aria model. Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria [rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architecture. This class extends the LlamaConfig to include additional parameters specific to the Mixture of Experts (MoE) architecture. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LlamaModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 4096): The size of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): 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*): 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 `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens, Llama 2 up to 4096, CodeLlama up to 16384. 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-06): 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`. pad_token_id (`int`, *optional*, defaults to 2): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. pretraining_tp (`int`, *optional*, defaults to 1): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_heads moe_num_experts (`int`, *optional*, defaults to 8): The number of experts in the MoE layer. moe_topk (`int`, *optional*, defaults to 2): The number of top experts to route to for each token. moe_num_shared_experts (`int`, *optional*, defaults to 2): The number of shared experts. Z aria_textZpast_key_valuesZcolwiseZrowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_projZ input_idsZ inputs_embedsZ hidden_statesZattention_mask)Z embed_tokensZlayersZnorm text_config} Nsilu{Gz?ư>TrF@intermediate_sizemoe_num_expertsmoe_topkmoe_num_shared_expertsc stjd| | ||d|||_||_||_||_||_||_|dur&|}||_||_ | |_ | |_ ||_ | |_ ||_||_||_||_||_|durM|n|j|j|_|jdurfd|jvrf|jd|jd<t|||_||_||_dS)N) pad_token_id bos_token_id eos_token_idtie_word_embeddingstypeZ rope_type)super__init__ vocab_sizemax_position_embeddings hidden_sizernum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actinitializer_range rms_norm_epspretraining_tp use_cache rope_theta rope_scalingattention_biasattention_dropoutmlp_biashead_dimrrrr)selfr#r%rr&r'r(r)r$r*r+r-rrrr,rr.r/r0r1r2r3rrrkwargs __class__r Z/var/www/auris/lib/python3.10/site-packages/transformers/models/aria/configuration_aria.pyr"sD zAriaTextConfig.__init__)r r r rrNrrrrTrrrrFrNFrFNrrr) __name__ __module__ __qualname____doc__ model_typeZkeys_to_ignore_at_inferenceZbase_model_tp_planZbase_model_pp_planZbase_config_keyintr" __classcell__r r r6r8r sfj  r c s^eZdZdZdZddiZeedZ      dd e d ed e e de d e f fdd Z ZS) AriaConfiga This class handles the configuration for both vision and text components of the Aria model, as well as additional parameters for image token handling and projector mapping. Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria [rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (`AriaVisionConfig` or `dict`, *optional*): Configuration for the vision component. vision_feature_layer (`int`, *optional*, defaults to -1): The index of the layer to select the vision feature. text_config (`AriaTextConfig` or `dict`, *optional*): Configuration for the text component. projector_patch_to_query_dict (`dict`, *optional*): Mapping of patch sizes to query dimensions. image_token_index (`int`, *optional*, defaults to 9): Index used to represent image tokens. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated normal initializer for initializing all weight matrices. Attributes: model_type (`str`): Type of the model, set to `"aria"`. image_token_index (`int`): Index used to represent image tokens. projector_patch_to_query_dict (`dict`): Mapping of patch sizes to query dimensions. vision_config (`AriaVisionConfig`): Configuration for the vision component. text_config (`AriaTextConfig`): Configuration for the text component. ZariaZimage_token_idimage_token_index)r vision_configN rvision_feature_layerr projector_patch_to_query_dictr*c s||_|dur ddd}dd|D|_t|j|_||_t|tr6d|d<t |ddi|}n |dur?t d}||_ ||_ t|trVd|vrVt di|}n|dur]t }||_ tjdi|dS) N)ii$cSsi|] \}}t|t|qSr )r>).0kvr r r8 sz'AriaConfig.__init__..Zidefics3_visionr=r )rAitemsrFmaxvaluesZ'max_value_projector_patch_to_query_dictrE isinstancedictrrBr*r r r!r")r4rBrEr rFrAr*r5r6r r8r" s*   zAriaConfig.__init__)NrCNNrDr)r9r:r;r<r=Z attribute_mapr r Z sub_configsr>rrfloatr"r?r r r6r8r@s.$ r@N) typingrrZconfiguration_utilsrZmodeling_rope_utilsrautorr r r@__all__r r r r8s  D T