g%dZddlmZddlmZmZmZmZddlm Z m Z m Z ddl m Z ddlmZmZddlmZejeZGd d e ZGd d eZd S)zCodeGen model configuration) OrderedDict)AnyListMappingOptional)PreTrainedTokenizer TensorTypeis_torch_available)PretrainedConfig)OnnxConfigWithPast PatchingSpec)loggingcZeZdZdZdZdddddZ dfd ZxZS) 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 ```codegen n_positionsn_embdn_headn_layer)max_position_embeddings hidden_sizenum_attention_headsnum_hidden_layers@Ngelu_newh㈵>{Gz?TPFc 0||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_||_t#jd|||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/r0r1r2r3r4r5r'r(r)kwargs __class__s m/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/codegen/configuration_codegen.pyr7zCodeGenConfig.__init__fs,% &    $#6 &$$"4!2"(( %LVi  ms     )rrrrrrr Nr!r"r"r"r#r$Tr%r%F)__name__ __module__ __qualname____doc__ model_type attribute_mapr7 __classcell__r:s@r;rrs> > @J#0'& M&!'+ + + + + + + + + + r<rceZdZ ddededeedeffd Ze d e ee e efffd Z e d e fd Z e d e fd Z ddede de dedeed e eeff fd Ze d e fdZxZS)CodeGenOnnxConfigdefaultNFconfigtaskpatching_specsuse_pastct||||t|jddsd|j_dSdS)N)rIrJrK pad_token_idr)r6r7getattr_configrM)r8rHrIrJrKr:s r;r7zCodeGenOnnxConfig.__init__sW d>T\]]]t|^T:: *()DL % % % * *r<returnctddddi}|jr ||dddd|d<nddd|d<|S) N input_idsbatchsequence)rinputs) directionzpast_sequence + sequenceattention_mask)rrKfill_with_past_key_values_)r8 common_inputss r;rVzCodeGenOnnxConfig.inputssp#[g*2M2M$NOO = J  + +MX + N N N29>X.Y.YM* + +29j.I.IM* +r<c|jjSN)rOrr8s r; num_layerszCodeGenOnnxConfig.num_layerss |##r<c|jjSr\)rOrr]s r;rz%CodeGenOnnxConfig.num_attention_headss |""r< tokenizer batch_size seq_lengthis_pair frameworkc> tt||||||}td|di}|jrwt st dddl |dj\}} | dz} ||j | |j j |j zf fdt|j D|d<|d|d<|jrE|dj} |d || | gd |d<|S) N)rbrcrdrerRzACannot generate dummy past_keys inputs without PyTorch installed.rcdg|],}f-Sr*)zeros).0_ past_shapetorchs r; z;CodeGenOnnxConfig.generate_dummy_inputs..sC555KLU[[,,ekk*.E.EF555r<past_key_valuesrX)dtyperU)dim)r6r generate_dummy_inputsrrKr ValueErrorrmshaperrOrranger^rpcatones)r8rarbrcrdrerZordered_inputsrSseqlenpast_key_values_length mask_dtyperlrmr:s @@r;rrz'CodeGenOnnxConfig.generate_dummy_inputssw0$77MM *W`iN  %k=3M%NOO = %''  !deee -k : @ v)/!&,*L,0HH  55555PUVZVePfPf55501,99I+J'( = '(89?J/4yy 015::eE[cm:3n3nouv0900N+ ,r<cdS)N r*r]s r;default_onnx_opsetz$CodeGenOnnxConfig.default_onnx_opsetsrr<)rGNF)r`r`FN)r=r>r?r strrrboolr7propertyrintrVr^rr rr rrrr~rCrDs@r;rFrFs-1 * *  * *\* *  * * * * * *WS#X%6 67X$C$$$X$#S###X# *. **&** *  * J' * c ******XCXr<rFN)r@ collectionsrtypingrrrrr r r configuration_utilsr onnxr rutilsr get_loggerr=loggerrrFr*r<r;rs$"!######////////////CCCCCCCCCC33333344444444  H % %t t t t t $t t t pNNNNN*NNNNNr<