a h/2@sdZddlmZddlmZddlmZmZer`ddlm Z m Z m Z m Z m Z mZmZmZdZnRddlmZed Z ed Z ed Z ed Z ed Z edZeeedddZdZeeZGdddeZdgZdS)zxLSTM configuration.)Optional)PretrainedConfig)is_xlstm_availablelogging)BackendModeTypeChunkwiseKernelType DtypeTypeSequenceKernelTypeStepKernelTypeWeightModeTyperound_up_to_next_multiple_ofxLSTMLargeConfigT)Literal)trainZtrain_with_padding inference)chunkwise--native_autogradzparallel--native_autograd)float32bfloat16Zfloat16native_sequence__nativenative)singleZfused)x multiple_ofreturncCst||d||S)z0Rounds up x to the next multiple of multiple_of.)int)rrri/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/xlstm/configuration_xlstm.pyr 2sr Fc"seZdZdZdZd)eeeeeeeeeeeeeeeee e e e eee ee eeeeeeeeeed fdd Zedd Zed!d"Zed#d$Zed%d&Zd'd(ZZS)* xLSTMConfiga This is the configuration class to store the configuration of a [`xLSTM`]. It is used to instantiate a xLSTM 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 xLSTM-7b [NX-AI/xLSTM-7b](https://huggingface.co/NX-AI/xLSTM-7b) model. 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, *optional*, defaults to 50304): Vocabulary size of the xLSTM model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`xLSTMModel`]. Defaults to the GPT2-NeoX tokenizer size. hidden_size (int, optional, *optional*, defaults to 4096): Dimensionality of the embeddings or hidden states. embedding_dim (int, optional, *optional*, defaults to 4096): Dimensionality of the embeddings or hidden states, use hidde_size if None. num_hidden_layers (int, optional, *optional*, defaults to 32): Number of blocks of the xLSTM model. num_blocks (int, optional, *optional*, defaults to 32): Number of blocks of the xLSTM model, use num_hidden_layers if None. num_heads (int, optional, *optional*, defaults to 8): Number of heads for the xLSTM Layer/Cell. use_bias (bool, optional, *optional*, defaults to `False`): Whether to use biases in the xLSTM model. norm_reduction_force_float32 (bool, optional, *optional*, defaults to `True`): Whether to force the float32 norm reduction op to be done in fp32 precision. tie_word_embeddings (bool, optional, *optional*, defaults to `False`): Whether to tie word embeddings to the lm head weights. add_out_norm (bool, optional, *optional*, defaults to `True`): Whether to add an output norm after the blocks before the LMHead. norm_eps (float, optional, *optional*, defaults to 1e-06): Norm eps for RMSNorm and Layer Norm. qk_dim_factor (float, optional, *optional*, defaults to 0.5): Scale factor for the query and key dimension. v_dim_factor (float, optional, *optional*, defaults to 1.0): Scale factor for the value dimension. chunkwise_kernel (ChunkwiseKernelType, optional, *optional*, defaults to `"chunkwise--native_autograd"`): Kernel type for chunkwise processing mode. sequence_kernel (SequenceKernelType, optional, *optional*, defaults to `"native_sequence__native"`): Kernel type for sequence processing mode. step_kernel (StepKernelType, optional, *optional*, defaults to `"native"`): Kernel type for step processing mode. mode (BackendModeType, optional, *optional*, defaults to `"inference"`): Operation mode (inference is needed for generation). chunk_size (int, optional, *optional*, defaults to 64): Internal chunk size. return_last_states (bool, optional, *optional*, defaults to `True`): If to return the last states / cache internally. Needed as True for generation. autocast_kernel_dtype (DtypeType, optional, *optional*, defaults to `"bfloat16"`): Kernel dtype for the states. eps (float, optional, *optional*, defaults to 1e-06): Epsilon for the mLSTM cell post norm. inference_state_dtype (DtypeType, optional, *optional*, defaults to `"float32"`): Kernel dtype for states in inference. ffn_proj_factor (float, optional, *optional*, defaults to 2.667): Size factor of the post-up projection gated Feed Forward network. ffn_round_up_to_multiple_of (int, optional, *optional*, defaults to 64): Size factor round value of the post-up projection gated Feed Forward network. gate_soft_cap (float, optional, *optional*, defaults to 15.0): Gate soft cap scale. output_logit_soft_cap (float, optional, *optional*, defaults to 30.0): Output logit soft cap scale. weight_mode (`Literal`, *optional*, defaults to `"single"`): Whether parallel linear layers are separated or fused (single). use_cache (bool, optional, *optional*, defaults to `True`): Whether to use the cache (xLSTMCache). pad_token_id (int, optional, *optional*, defaults to 1): Pad token id needed for generation. bos_token_id (int, optional, *optional*, defaults to 0): BOS token id needed for generation. eos_token_id (int, optional, *optional*, defaults to 2): EOS token id needed for generation. max_inference_chunksize (int, optional, *optional*, defaults to 16384): Limit the chunk size for inference to save memory. Example: ```python >>> from transformers import xLSTMConfig, xLSTMModel >>> # Initializing a xLSTM configuration >>> configuration = xLSTMConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = xLSTMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZxlstmN FTư>??rrrr@rrtV@.@>@rrr@) vocab_size hidden_size embedding_dimnum_hidden_layers num_blocks num_headsuse_biasnorm_reduction_force_float32tie_word_embeddings add_out_normnorm_eps qk_dim_factor v_dim_factorchunkwise_kernelsequence_kernel step_kernelmode chunk_sizereturn_last_statesautocast_kernel_dtypeepsinference_state_dtypeffn_proj_factorffn_round_up_to_multiple_of gate_soft_capoutput_logit_soft_cap weight_mode use_cache pad_token_id bos_token_id eos_token_idmax_inference_chunksizec!" s||_|dur|n||_|dur$|n||_|dur6|n||_|durH|n||_||_||_| |_| |_| |_ ||_ | |_ | |_ ||_ ||_||_||_||_||_||_||_||_||_||_||_||_||_||_||_||_||_| |_t j!f|||| d|!dS)N)rJrKrIr5)"r-r.r/r0r1r2r3r5r6r7r4r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLsuper__init__)"selfr-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLkwargs __class__rrrNsP,zxLSTMConfig.__init__cCst|j|jddSNr')r)r r.r8rOrrrqk_dims zxLSTMConfig.qk_dimcCst|j|jddSrS)r r.r9rTrrrv_dims zxLSTMConfig.v_dimcCs |j|jSN)rUr2rTrrr qk_head_dimszxLSTMConfig.qk_head_dimcCs |j|jSrW)rVr2rTrrr v_head_dimszxLSTMConfig.v_head_dimcCsttrlt|j|j|j|j|j|j|j|j |j |j |j |j |j|j|j|j|j|j|j|j|j|j|j|jdS|SdS)N)r-r/r1r2r3r6r7r4r8r9r:r;r<r=r>r?r@rArBrCrDrErFrG)external_xlstmrr-r.r0r2r3r6r7r4r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrTrrrto_xlstm_block_config s8z!xLSTMConfig.to_xlstm_block_config) r r!Nr"Nr#FTFTr$r%r&rrrrr'Trr$rr(r'r)r*rTrrr+r,)__name__ __module__ __qualname____doc__Z model_typerrboolfloatrr r rr r rNpropertyrUrVrXrYr[ __classcell__rrrQrr<s[Z    rN)r_typingrZconfiguration_utilsrutilsrrZxlstm.xlstm_large.modelrrr r r r r rrZrrZ get_loggerr\loggerr__all__rrrrs*  (   s