o  h @sxddlZddlmZddlmZddlmZddlmZm Z e e Z er*ddl Z ddZ   d1d eed ed d eed edeffddZ   d1d eed ed d eed edeffddZ   d1d eed ed d eed edeffddZ d2d ed d d eed edeffddZ d2d ed d d eed edeffddZ d2d ed d d eed edeffddZeeeeeedZ  d3dedededeed eef d!d"Zd2d ed eefd#d$Zd2d ed eefd%d&Zd2d ed eefd'd(Zd2d ed eefd)d*Zd2d ed eefd+d,Z d2d ed eefd-d.Z!eeeee e!dZ"d2d ed eefd/d0Z#dS)4Nwraps)Optional)PretrainedConfig)is_torch_availableloggingcs,ddddtfdd}|S)ad Decorator function to update the RoPE parameters in the forward pass, if the model is using a dynamic RoPE (i.e. a RoPE implementation that may recompute its frequencies in the forward pass). Args: rope_forward (Callable): The forward pass of the RoPE implementation. Returns: The decorated forward pass. cSst|d}t|jdr|jj}n|jj}||kr8t|ds-|j|j||dd\|_}|jd|jdddS|j ||_ |jd|j dddS) zbLongrope uses long factor if sequence is larger than original pretraining length, short otherwise.r original_max_position_embeddings long_inv_freqseq_leninv_freqF persistentN) torchmaxhasattrconfigr max_position_embeddings rope_init_fnr register_bufferoriginal_inv_freqto)self position_idsdevicer r _rt/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/transformers/modeling_rope_utils.pylongrope_frequency_update+s     z6dynamic_rope_update..longrope_frequency_updatecSst|d}||jkr#|j|j||d\}|_|jd|dd||_||jkrD|j|jkrF|j ||_|jd|jdd|j|_dSdSdS)a dynamic RoPE layers should recompute `inv_freq` in the following situations: 1 - growing beyond the cached sequence length (allow scaling) 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) rr r FrN) rrmax_seq_len_cachedrrattention_scalingroriginal_max_seq_lenrr)rrrr r rrrdynamic_frequency_update>s  z5dynamic_rope_update..dynamic_frequency_updatecsBd|jvr|||jdn |jdkr|||jd|||S)Ndynamic)rlongrope) rope_typer)rxrr#r rope_forwardrrwrapperQs   z$dynamic_rope_update..wrapperr)r)r*rr(rdynamic_rope_updates  r+rrz torch.devicer returnz torch.Tensorc Ks|durt|dkrtd|d|t|dkr#|d}|d}n!|durD|j}t|dr2|jnd}t|d |j|j}t||}d}d|t j d|d t j d j |t j d |} | |fS) a Computes the inverse frequencies according to the original RoPE implementation Args: config ([`~transformers.PretrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. Unused for this type of RoPE. rope_kwargs (`Dict`, *optional*): BC compatibility with the previous RoPE class instantiation, will be removed in v4.45. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). NrzUnexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in `_compute_default_rope_parameters`, got `rope_kwargs`= and `config`=basedimpartial_rotary_factor?head_dimdtyperr5)len ValueError rope_thetarr0getattr hidden_sizenum_attention_headsintrarangeint64rfloat) rrr rope_kwargsr.r/r0r2attention_factorr rrr _compute_default_rope_parameters\s&   ,rCcKsx|durt|dkrtd|d|t|dkr|d}n |dur(|jd}t|||fi|\}}||}||fS)a Computes the inverse frequencies with linear scaling. Credits to the Reddit user /u/kaiokendev Args: config ([`~transformers.PretrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. Unused for this type of RoPE. rope_kwargs (`Dict`, *optional*): BC compatibility with the previous RoPE class instantiation, will be removed in v4.45. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). NrzUnexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in `_compute_linear_scaling_rope_parameters`, got `rope_kwargs`=r-factor)r7r8 rope_scalingrC)rrr rArDr rBrrr'_compute_linear_scaling_rope_parameterss   rFc Ks|durt|dkrtd|d|t|dkr+|d}|d}|d}|d}n)|durT|j}t|d r:|jnd }t|d |j|j} t| |}|j }|j d}d } |dur`||kr`|n|}|||||d ||d }d |t j d|d t j dj|t jd|} | | fS)a4 Computes the inverse frequencies with NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla Args: config ([`~transformers.PretrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length, used to update the dynamic RoPE at inference time. rope_kwargs (`Dict`, *optional*): BC compatibility with the previous RoPE class instantiation, will be removed in v4.45. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). NrzUnexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in `_compute_dynamic_ntk_parameters`, got `rope_kwargs`=r-r.r/rrDr0r1r2rr3r4r6)r7r8r9rr0r:r;r<r=rrErr>r?rr@) rrr rAr.r/rrDr0r2rBr rrr_compute_dynamic_ntk_parameterss2    $,rGc st|dkr td||j}t|dr|jnd}t|d|j|j}t||}|j d}|j d} |j d} |j d } d |j vrQ|j d } |j | }n|j } dd d } | durr| rn| rnt | || | || } n| |} |j dpyd}|j dpd }ddfdd}dd}|t d|dj|t j d|}d|}d||}|||||| \}}d ||||dj|t j d}|d |||}|| fS)a Computes the inverse frequencies with NTK scaling. Please refer to the [original paper](https://arxiv.org/abs/2309.00071) Args: config ([`~transformers.PretrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. Unused for this type of RoPE. rope_kwargs (`Dict`, *optional*): BC compatibility with the previous RoPE class instantiation, will be removed in v4.45. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin. rzYUnexpected arguments: `**rope_kwargs` should be unset in `_compute_yarn_parameters`, got r0r1r2rDrBmscalemscale_all_dimr rcSs"|dkrdSd|t|dS)Nrr1g?)mathlog)scalerHrrr get_mscale sz,_compute_yarn_parameters..get_mscaleN beta_fast beta_slowcSs*|t||dtjdt|S)zPInverse dimension formula to find the dimension based on the number of rotationsr3)rJrKpi) num_rotationsr/r.rrrrfind_correction_dims*z5_compute_yarn_parameters..find_correction_dimcs@t||||}t||||}t|dt||dfS)z.Find dimension range bounds based on rotationsrr)rJfloorceilrmin)low_rothigh_rotr/r.rlowhighrSrrfind_correction_range#sz7_compute_yarn_parameters..find_correction_rangecSs>||kr|d7}tj|tjd|||}t|dd}|S)NgMbP?r4rr)rr>float32clamp)rVrr/ linear_func ramp_funcrrrlinear_ramp_factor)s z4_compute_yarn_parameters..linear_ramp_factorr3r6)r)r7r8r9rr0r:r;r<r=rEgetrr@rr>r)rrr rAr.r0r2r/rDrBrHrIr rMrNrPr\ra pos_freqsinv_freq_extrapolationinv_freq_interpolationrYrZinv_freq_extrapolation_factorr rr[r_compute_yarn_parameterssH           "    rgcKs@t|dkr td||j}t|dr|jnd}t|d|j|j}t||}|j d}|j d} |j d} |j d } t|d rO|j } |j |j } n|j } | d url| dkr]d} nt d t | t | } |r||| kr|tj|tj|d } n tj| tj|d } tjd|dtj|d |}d| ||}|| fS)a  Computes the inverse frequencies with LongRoPE scaling. Please refer to the [original implementation](https://github.com/microsoft/LongRoPE) Args: config ([`~transformers.PretrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. rope_kwargs (`Dict`, *optional*): BC compatibility with the previous RoPE class instantiation, will be removed in v4.45. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin. rz]Unexpected arguments: `**rope_kwargs` should be unset in `_compute_longrope_parameters`, got r0r1r2 long_factor short_factorrDrBr Nr)r5rr3)r7r8r9rr0r:r;r<r=rErbr rrJsqrtrKrtensorr]r>r?r@)rrr rAr.r0r2r/rhrirDrBr ext_factorsinv_freq_shaper rrr_compute_longrope_parametersBs8        rncKst|||fi|\}}|jd}|jd}|jd}|jd} | |} | |} dtj|} t| | k|||} | | |||}d|| ||| }| | k| | k}t||| } | |fS)a Computes the inverse frequencies for llama 3.1. Args: config ([`~transformers.PretrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. Unused for this type of RoPE. rope_kwargs (`Dict`, *optional*): BC compatibility with the previous RoPE class instantiation, will be removed in v4.45. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin. rDlow_freq_factorhigh_freq_factorr r3r)rCrErJrQrwhere)rrr rAr rBrDrorpold_context_lenlow_freq_wavelenhigh_freq_wavelenwaveleninv_freq_llama smooth_factorsmoothed_inv_freqis_medium_freqrrr_compute_llama3_parameterss    rz)defaultlinearr$yarnr%llama3r& received_keys required_keys optional_keys ignore_keyscCsd|vr|dh8}|d|dur||8}||}|r&td|d||dur1|||}n||}|rDtd|d|dSdS)zYCompare the received keys in `config.rope_scaling` against the expected and optional keystyper&Nz9Missing required keys in `rope_scaling` for 'rope_type'='z': z5Unrecognized keys in `rope_scaling` for 'rope_type'=')addKeyErrorloggerwarning)r&rrrr missing_keys unused_keysrrr_check_received_keyss  rcCs@|j}|d|dd}dh}t|}t||||ddS)Nr&rr)rErbsetkeysr)rrrEr&rrrrr!_validate_default_rope_parameterss  rcCsx|j}|d|dd}ddh}t|}t||||d|d}|dus0t|tr0|dkr:td|dSdS)Nr&rrDrr18`rope_scaling`'s factor field must be a float >= 1, got rErbrrr isinstancer@rr)rrrEr&rrrDrrr(_validate_linear_scaling_rope_parameterss rcCs|j}|d|dd}ddh}dh}t|}t|||||d|d}|dus4t|tr4|dkr>td|dSdS)Nr&rrDr rr1rr)rrrEr&rrrrDrrr)_validate_dynamic_scaling_rope_parameterss rc Cs6|j}|d|dd}ddh}hd}t|}t|||||d|d}|dus5t|tr5|dkr=td||d}|durWt|trO|d krWtd ||d } | durmt| tsmtd | |d } | durt| tstd| | pd| pdkrtd| d| ddSdS)Nr&rrD>rHrNrPrIrBr rr1rrBrL`rope_scaling`'s attention_factor field must be a float greater than 0, got rNz6`rope_scaling`'s beta_fast field must be a float, got rPz6`rope_scaling`'s beta_slow field must be a float, got rOrzO`rope_scaling`'s beta_fast field must be greater than beta_slow, got beta_fast=z( (defaults to 32 if None) and beta_slow=z (defaults to 1 if None)r) rrrEr&rrrrDrBrNrPrrr_validate_yarn_parameterss6    rcCs|j}|d|dd}hd}hd}t|}t|||||dt|dr,|jnd}t|d|j|j }t ||} |d } t | t sYt d d | DrYtd | t| | d ksptd| d dt| |d} t | t st dd | Drtd| t| | d kstd| d dt| t|drtddS|d} | durtdnt | tr| dkrtd| |d} | durt | tr| dkrtd| dSdSdS)Nr&r>r&rhri>rDrBr rr0r1r2rics|] }t|ttfVqdSNrr=r@.0r'rrr ,z0_validate_longrope_parameters..zC`rope_scaling`'s short_factor field must be a list of numbers, got r3z5`rope_scaling`'s short_factor field must have length z, got rhcsrrrrrrrr2rzB`rope_scaling`'s long_factor field must be a list of numbers, got z4`rope_scaling`'s long_factor field must have length r aYThis model has set a `original_max_position_embeddings` field, to be used together with `max_position_embeddings` to determine a scaling factor. Please set the `factor` field of `rope_scaling`with this ratio instead -- we recommend the use of this field over `original_max_position_embeddings`, as it is compatible with most model architectures.rDz1Missing required keys in `rope_scaling`: 'factor'rrBgr)rErbrrrrr0r:r;r<r=rlistallrrr7 warning_oncer@)rrrEr&rrrr0r2r/rirhrDrBrrr_validate_longrope_parameterssH        rc Cs6|j}|d|dd}hd}t|}t||||d|d}|dus0t|tr0|dkr8td||d}|d }|dusIt|tsQtd ||dusZt|tsbtd |||krqtd |d ||d} | dus~t| t std| | |j krtd| d|j dSdS)Nr&r>rDr&rorpr rrDr1rrorpz<`rope_scaling`'s low_freq_factor field must be a float, got z=`rope_scaling`'s high_freq_factor field must be a float, got zc`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor=z and low_freq_factor=r zP`rope_scaling`'s original_max_position_embeddings field must be an integer, got zg`rope_scaling`'s original_max_position_embeddings field must be less than max_position_embeddings, got z and max_position_embeddings=) rErbrrrrr@rrr=r) rrrEr&rrrDrorpr rrr_validate_llama3_parametersPsL  rcCsdt|dd}|dur dS|d|dd}t|}|dur'|||ddStd|ddS) zO Validate the RoPE config arguments, given a `PretrainedConfig` object rENr&rr{rzTMissing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='')r:rbROPE_VALIDATION_FUNCTIONSrr)rrrEr& validation_fnrrrrope_config_validations   r)NNNr)NN)$rJ functoolsrtypingrconfiguration_utilsrutilsrr get_logger__name__rrr+r=tupler@rCrFrGrgrnrzROPE_INIT_FUNCTIONSstrrrrrrrrrrrrrrrs    ?  +  *  4  a  @  /  &2&