`ix7ddlmZmZddlZddlmZddlmZddlm Z ddl m Z ddl m Z ddlmZdd lmZd d lmZmZmZd d lmZd d lmZmZmZmZmZmZeje Z!GddeZ"GddeZ#dZ$GddeZ%GddeZ&GddeZ'GddeZ(GddeZ)GddeZ*gdZ+dS) )CallableOptionalN)TransformersKwargs)Cache)ALL_ATTENTION_FUNCTIONS)Unpack)logging)deprecate_kwarg)LlamaPreTrainedModel LlamaRMSNormeager_attention_forward) OlmoConfig) OlmoAttentionOlmoDecoderLayerOlmoForCausalLM OlmoModelOlmoRotaryEmbeddingapply_rotary_pos_embceZdZdZdZddddddddZdgd gfd d gd gfd gd gfd Z dfd ZxZS) Olmo2Configa This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the [allenai/Olmo2-7B-1124-hf](https://huggingface.co/allenai/Olmo2-7B-1124-hf). 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 50304): Vocabulary size of the Olmo2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Olmo2Model`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension 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, check out [this paper](https://huggingface.co/papers/2305.13245). 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. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. 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 1): Padding token id. bos_token_id (`int`, *optional*): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 50279): End of stream token id. 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. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. attention_bias (`bool`, defaults to `False`, *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. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. ```python >>> from transformers import Olmo2Model, Olmo2Config >>> # Initializing a Olmo2 7B style configuration >>> configuration = Olmo2Config() >>> # Initializing a model from the Olmo2 7B style configuration >>> model = Olmo2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` olmo2 colwise_rep rowwise_repcolwiserowwise)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_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnorm+ Nsilu{Gz?TgF@h㈵>c tjdid|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d|d||||_|`dS)N vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddings rope_theta rope_scalingattention_biasattention_dropout)super__init__ rms_norm_epsclip_qkv)selfr2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrGkwargs __class__s {/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/olmo2/modular_olmo2.pyrFzOlmo2Config.__init__zs.    !z #  0/ 0/  !4 3  !4 3  "z %<$; 0/  i & & & !4 3 "z &! "*># $0/'   ,) MMM)r%r&r'r(r(Nr)r*r+Tr,Nr-Fr.NFr/r0) __name__ __module__ __qualname____doc__ model_typebase_model_tp_planbase_model_pp_planrF __classcell__rKs@rLrrsKKZJ%2%2%2%2"+ )"+&(9:#%568IJ!"_$56  $!)..........rMrceZdZdZdS) Olmo2RMSNormc|j}|tj}|ddd}|tj||jzz}|j|z|S)Nr T)keepdim) dtypetotorchfloat32powmeanrsqrtvariance_epsilonweight)rIr input_dtypevariances rLforwardzOlmo2RMSNorm.forwards|#) %((77  $$Q'',,R,>>% Ht?T4T(U(UU  m+// <<