o eia(@sLdZddlmZddlmZddlmZeeZ GdddeZ dgZ dS)zPyTorch Phi-MoE model.)PreTrainedConfig)RopeParameters)loggingc6sPeZdZdZdZdgZdZ                     d5dedBdedBdedBdedBdedBdedBdedBdedBde dBd edBd!e dBd"edBd#edBd$edBd%edBd&e e ee fBdBd'edBd(e dBd)edBd*edBd+e dBd,e dBd-e dBd.e dBd/e dBd0e dBf4fd1d2 Z d6fd3d4 ZZS)7 PhimoeConfiga This is the configuration class to store the configuration of a [`PhimoeModel`]. It is used to instantiate a Phi-moe 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 [microsoft/Phi-3.5-MoE-instruct](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct). 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 32064): Vocabulary size of the Phimoe model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`PhimoeModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 6400): 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. max_position_embeddings (`int`, *optional*, defaults to `4096*32`): The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention allows sequence of up to 4096*32 tokens. 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`. pad_token_id (`int`, *optional*): 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. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. 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`. sliding_window (`int`, *optional*): Sliding window attention window size. If not specified, will default to `262144`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. num_experts_per_tok (`int`, *optional*, defaults to 2): The number of experts to root per-token, can be also interpreted as the `top-p` routing parameter num_local_experts (`int`, *optional*, defaults to 16): Number of experts per Sparse MLP layer. output_router_logits (`bool`, *optional*, defaults to `False`): Whether or not the router logits should be returned by the model. Enabling this will also allow the model to output the auxiliary loss. See [here]() for more details router_aux_loss_coef (`float`, *optional*, defaults to 0.001): The aux loss factor for the total loss. router_jitter_noise (`float`, *optional*, defaults to 0.01): Amount of noise to add to the router. input_jitter_noise (`float`, *optional*, defaults to 0.0): Input jitter noise attention_bias (`bool`, *optional*, defaults to `False`): Attention bias lm_head_bias (`bool`, *optional*, defaults to `False`): LM head bias Example: ```python >>> from transformers import PhimoeModel, PhimoeConfig >>> # Initializing a Phi-3 style configuration >>> configuration = PhimoeConfig.from_pretrained("microsoft/Phi-3.5-MoE-instruct") >>> # Initializing a model from the configuration >>> model = PhimoeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```phimoepast_key_valuesg.A@} silu{Gz?h㈵>TNFMbP?{Gz? vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range rms_norm_eps use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddingsrope_parameterssliding_windowattention_dropoutnum_experts_per_toknum_local_expertsoutput_router_logitsrouter_aux_loss_coefrouter_jitter_noiseinput_jitter_noiseattention_bias lm_head_biasc s||_||_||_||_||_||_||_||_||_|dur!|}||_ ||_ | |_ | |_ | |_ ||_||_||_||_||_||_||_||_||_| |_| |_||_tjdi|dS)N)rrrrrrr'r/r0rrrr r!r(r)r*r+r,r-r.r&r%r"r#r$super__init__)selfrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0kwargs __class__r1m/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/phimoe/configuration_phimoe.pyr3ps:zPhimoeConfig.__init__cstj|d|jddkrCd|jvr|jd|_|jdd}|jdd}t|ttfs5td|t|ttfsEtd |dSdS) z? 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