a h/@sdZddlmZddlmZddlmZmZddlm Z m Z m Z ddl m Z ddlmZmZdd lmZeeZGd d d e ZGd d d eZd d gZdS)zOpenAI GPT-2 configuration) OrderedDict)Mapping)AnyOptional)PreTrainedTokenizer TensorTypeis_torch_available)PretrainedConfig)OnnxConfigWithPast PatchingSpec)loggingcs:eZdZdZdZdgZdddddZdfdd ZZS) GPT2ConfigaK This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to instantiate a GPT-2 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-2 [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) 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 50257): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`]. n_positions (`int`, *optional*, defaults to 1024): 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 768): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. 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 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`float`, *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-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. summary_type (`string`, *optional*, defaults to `"cls_index"`): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and [`TFGPT2DoubleHeadsModel`]. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and [`TFGPT2DoubleHeadsModel`]. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary. Used in for the multiple choice head in [`GPT2DoubleHeadsModel`]. Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and [`TFGPT2DoubleHeadsModel`]. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and [`TFGPT2DoubleHeadsModel`]. The dropout ratio to be used after the projection and activation. scale_attn_weights (`bool`, *optional*, defaults to `True`): Scale attention weights by dividing by sqrt(hidden_size).. 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): Id of the beginning of sentence token in the vocabulary. eos_token_id (`int`, *optional*, defaults to 50256): Id of the end of sentence token in the vocabulary. scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): Whether to additionally scale attention weights by `1 / layer_idx + 1`. reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention dot-product/softmax to float() when training with mixed precision. Example: ```python >>> from transformers import GPT2Config, GPT2Model >>> # Initializing a GPT2 configuration >>> configuration = GPT2Config() >>> # Initializing a model (with random weights) from the configuration >>> model = GPT2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zgpt2past_key_valuesn_embd n_positionsn_headn_layer) hidden_sizeZmax_position_embeddingsnum_attention_headsZnum_hidden_layersQ Ngelu_new皙?h㈵>{Gz? cls_indexTPFc s||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_||_||_||_||_||_||_||_tjf||d|dS)N) bos_token_id eos_token_id) vocab_sizerrrrn_inneractivation_function resid_pdrop embd_pdrop attn_pdroplayer_norm_epsiloninitializer_range summary_typesummary_use_projsummary_activationsummary_first_dropoutsummary_proj_to_labelsscale_attn_weights use_cachescale_attn_by_inverse_layer_idxreorder_and_upcast_attnr r!super__init__)selfr"rrrrr#r$r%r&r'r(r)r*r+r,r.r-r/r0r r!r1r2kwargs __class__g/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/gpt2/configuration_gpt2.pyr4s0zGPT2Config.__init__)rrrrrNrrrrrrrTNTrTTrrFF) __name__ __module__ __qualname____doc__Z model_typeZkeys_to_ignore_at_inferenceZ attribute_mapr4 __classcell__r9r9r7r:rs@a rc seZdZdeeeeeedfdd Z e e ee e effddd Z e e dd d Ze e dd d Zdee e eeee eefdfdd Ze e dddZZS)GPT2OnnxConfigdefaultNF)configtaskpatching_specsuse_pastcs.tj||||dt|jdds*d|j_dS)N)rCrDrE pad_token_idr)r3r4getattr_configrF)r5rBrCrDrEr7r9r:r4szGPT2OnnxConfig.__init__)returncCsHtddddi}|jr6|j|ddddd|d<nddd|d<|S) N input_idsbatchsequence)rinputs) directionzpast_sequence + sequenceattention_mask)rrEZfill_with_past_key_values_)r5 common_inputsr9r9r:rNs zGPT2OnnxConfig.inputscCs|jjSN)rHrr5r9r9r: num_layersszGPT2OnnxConfig.num_layerscCs|jjSrR)rHrrSr9r9r:rsz"GPT2OnnxConfig.num_attention_heads) tokenizer batch_size seq_lengthis_pair frameworkrIc stt|j|||||d}td|di}|jrts@tdnTddl|dj\}} | d} ||j | |j j |j ffddt |j D|d<|d |d <|jr|d j} j|d j|| | d gd d |d <|S) N)rWrXrYrZrJzACannot generate dummy past_keys inputs without PyTorch installed.rcs g|]}fqSr9)Zzeros).0_Z past_shapetorchr9r: sz8GPT2OnnxConfig.generate_dummy_inputs..rrP)dtyperM)dim)r3r generate_dummy_inputsrrEr ValueErrorr_shaperrHrrangerTracatZones) r5rVrWrXrYrZrQZordered_inputsrKZseqlenZpast_key_values_lengthZ mask_dtyper7r^r:rcs2         z$GPT2OnnxConfig.generate_dummy_inputscCsdS)N r9rSr9r9r:default_onnx_opset sz!GPT2OnnxConfig.default_onnx_opset)rANF)rUrUFN)r;r<r=r strrlistr boolr4propertyrintrNrTrrrrrcrir?r9r9r7r:r@s:    ,r@N)r> collectionsrcollections.abcrtypingrrrrr Zconfiguration_utilsr Zonnxr r utilsr Z get_loggerr;loggerrr@__all__r9r9r9r:s     #Q