U 4Af%@sdZddlmZddlmZmZmZmZddlm Z m Z m Z ddl m Z ddlmZmZddlmZeeZGd d d e ZGd d d eZd S)zCodeGen model configuration) OrderedDict)AnyListMappingOptional)PreTrainedTokenizer TensorTypeis_torch_available)PretrainedConfig)OnnxConfigWithPast PatchingSpec)loggingcs4eZdZdZdZdddddZdfdd ZZS) CodeGenConfiga This is the configuration class to store the configuration of a [`CodeGenModel`]. It is used to instantiate a CodeGen 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 CodeGen [Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-2B-mono) 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 CodeGen model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CodeGenModel`]. 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_ctx (`int`, *optional*, defaults to 2048): This attribute is used in `CodeGenModel.__init__` without any real effect. 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*): 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 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`int`, *optional*, defaults to 0.0): The dropout ratio for the embeddings. attn_pdrop (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): 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). bos_token_id (`int`, *optional*, defaults to 50256): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 50256): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the model has a output word embedding layer. Example: ```python >>> from transformers import CodeGenConfig, CodeGenModel >>> # Initializing a CodeGen 6B configuration >>> configuration = CodeGenConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = CodeGenModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zcodegen n_positionsn_embdn_headn_layer)Zmax_position_embeddings hidden_sizenum_attention_headsZnum_hidden_layers@Ngelu_newh㈵>{Gz?TPFc s||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_||_tjf|||d|dS)N) bos_token_id eos_token_idtie_word_embeddings) vocab_sizen_ctxrrrrn_inner rotary_dimactivation_function resid_pdrop embd_pdrop attn_pdroplayer_norm_epsiloninitializer_range use_cacher!r"super__init__)selfr$rr%rrrr'r&r(r)r*r+r,r-r.r!r"r#kwargs __class__U/tmp/pip-unpacked-wheel-zw5xktn0/transformers/models/codegen/configuration_codegen.pyr0fs0zCodeGenConfig.__init__)rrrrrrrNrrrrrrTr r F)__name__ __module__ __qualname____doc__Z model_typeZ attribute_mapr0 __classcell__r5r5r3r6rs4@ rc seZdZdeeeeedfdd Ze e ee e effddd Z e e dd d Z e e dd d Zdee e eeee eefdfdd Ze e dddZZS)CodeGenOnnxConfigdefaultNF)configtaskpatching_specsuse_pastcs.tj||||dt|jdds*d|j_dS)N)r?r@rA pad_token_idr)r/r0getattr_configrB)r1r>r?r@rAr3r5r6r0szCodeGenOnnxConfig.__init__)returncCsHtddddi}|jr6|j|ddddd|d<nddd|d<|S) N input_idsbatchsequence)rinputs) directionzpast_sequence + sequenceattention_mask)rrAZfill_with_past_key_values_)r1 common_inputsr5r5r6rJs zCodeGenOnnxConfig.inputscCs|jjSN)rDrr1r5r5r6 num_layersszCodeGenOnnxConfig.num_layerscCs|jjSrN)rDrrOr5r5r6rsz%CodeGenOnnxConfig.num_attention_heads) tokenizer batch_size seq_lengthis_pair frameworkrEc stt|j|||||d}td|di}|jrts@tdnTddl|dj\}} | d} ||j | |j j |j ffddt |j D|d<|d |d <|jr|d j} j|d j|| | d gd d |d <|S) N)rSrTrUrVrFzACannot generate dummy past_keys inputs without PyTorch installed.rcs g|]}fqSr5)zeros).0_Z past_shapetorchr5r6 sz;CodeGenOnnxConfig.generate_dummy_inputs..Zpast_key_valuesrL)dtyperI)Zdim)r/r generate_dummy_inputsrrAr ValueErrorr\shaperrDrrangerPr^catZones) r1rRrSrTrUrVrMZordered_inputsrGZseqlenZpast_key_values_lengthZ mask_dtyper3r[r6r_s<        z'CodeGenOnnxConfig.generate_dummy_inputscCsdS)N r5rOr5r5r6default_onnx_opsetsz$CodeGenOnnxConfig.default_onnx_opset)r=NF)rQrQFN)r7r8r9r strrr boolr0propertyrintrJrPrrrr rr_rer;r5r5r3r6r<s:   ,r<N)r: collectionsrtypingrrrrrr r Zconfiguration_utilsr Zonnxr r utilsrZ get_loggerr7loggerrr<r5r5r5r6s    x