o Zh"@sdZddlmZddlmZmZmZmZddlm Z m Z m Z ddl m Z ddlmZmZddlmZeeZGd d d e ZGd d d eZd d gZd S)zGPT-J model configuration) OrderedDict)AnyListMappingOptional)PreTrainedTokenizer TensorTypeis_torch_available)PretrainedConfig)OnnxConfigWithPast PatchingSpec)loggingcsVeZdZdZdZdddddZ            dfdd ZZS) GPTJConfiga= This is the configuration class to store the configuration of a [`GPTJModel`]. It is used to instantiate a GPT-J 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 GPT-J [EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) architecture. 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 50400): Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GPTJModel`]. n_positions (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). n_embd (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. rotary_dim (`int`, *optional*, defaults to 64): Number of dimensions in the embedding that Rotary Position Embedding is applied to. n_inner (`int`, *optional*, defaults to None): Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd activation_function (`str`, *optional*, defaults to `"gelu_new"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`int`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers. 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). Example: ```python >>> from transformers import GPTJModel, GPTJConfig >>> # Initializing a GPT-J 6B configuration >>> configuration = GPTJConfig() >>> # Initializing a model from the configuration >>> model = GPTJModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zgptj n_positionsn_embdn_headn_layer)Zmax_position_embeddings hidden_sizenum_attention_headsZnum_hidden_layers@Ngelu_newh㈵>{Gz?TPFc s~||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_tjd|||d|dS)N) bos_token_id eos_token_idtie_word_embeddings) vocab_sizerrrrn_inner rotary_dimactivation_function resid_pdrop embd_pdrop attn_pdroplayer_norm_epsiloninitializer_range use_cacher!r"super__init__)selfr%rrrrr'r&r(r)r*r+r,r-r.r!r"r#kwargs __class__r$Z/var/www/auris/lib/python3.10/site-packages/transformers/models/gptj/configuration_gptj.pyr0]s* zGPTJConfig.__init__)rrrrrrNrrrrrrTr r F)__name__ __module__ __qualname____doc__Z model_typeZ attribute_mapr0 __classcell__r$r$r3r5rs47 rcseZdZ   ddededeeedeffdd Z e d e ee e efffd d Z e d e fd dZe d e fddZ    ddede de dedeed e eeff fdd Ze d e fddZZS)GPTJOnnxConfigdefaultNFconfigtaskpatching_specsuse_pastcs2tj||||dt|jddsd|j_dSdS)N)r>r?r@ pad_token_idr)r/r0getattr_configrA)r1r=r>r?r@r3r$r5r0s zGPTJOnnxConfig.__init__returncCsJtddddi}|jr|j|ddddd|d<|Sddd|d<|S) N input_idsbatchsequence)rinputs) directionzpast_sequence + sequenceattention_mask)rr@Zfill_with_past_key_values_)r1 common_inputsr$r$r5rIszGPTJOnnxConfig.inputscC|jjSN)rCrr1r$r$r5 num_layerszGPTJOnnxConfig.num_layerscCrMrN)rCrrOr$r$r5rrQz"GPTJOnnxConfig.num_attention_heads tokenizer batch_size seq_lengthis_pair frameworkc stt|j|||||d}td|di}|jrItstdddl|dj\}} | d} ||j | |j j |j ffddt |j D|d<|d |d <|jrj|d j} j|d j|| | d gd d |d <|S) N)rTrUrVrWrEzACannot generate dummy past_keys inputs without PyTorch installed.rcs g|] }fqSr$)Zzeros).0_Z past_shapetorchr$r5 sz8GPTJOnnxConfig.generate_dummy_inputs..Zpast_key_valuesrK)dtyperH)dim)r/r generate_dummy_inputsrr@r ValueErrorr\shaperrCrrangerPr^catZones) r1rSrTrUrVrWrLZordered_inputsrFZseqlenZpast_key_values_lengthZ mask_dtyper3r[r5r`s2       z$GPTJOnnxConfig.generate_dummy_inputscCsdS)N r$rOr$r$r5default_onnx_opsetsz!GPTJOnnxConfig.default_onnx_opset)r<NF)rRrRFN)r6r7r8r strrrr boolr0propertyrintrIrPrrr rr`rfr:r$r$r3r5r;sL    ,r;N)r9 collectionsrtypingrrrrrr r Zconfiguration_utilsr Zonnxr r utilsrZ get_loggerr6loggerrr;__all__r$r$r$r5s    m Q