o sh| @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$Zd2d ed eefd%d&Zd2d ed eefd'd(Zd2d ed eefd)d*Zd2d ed eefd+d,Z d2d ed eefd-d.Z!eeeee e!dZ"d2d ed eefd/d0Z#dS)4Nwraps)Optional)PretrainedConfig)is_torch_availableloggingcs,ddddtfdd}|S)ad Decorator function to update the RoPE parameters in the forward pass, if the model is using a dynamic RoPE (i.e. a RoPE implementation that may recompute its frequencies in the forward pass). Args: rope_forward (Callable): The forward pass of the RoPE implementation. Returns: The decorated forward pass. cSst|d}t|jdr|jj}n|jj}||kr8t|ds-|j|j||dd\|_}|jd|jdddS|j ||_ |jd|j dddS) zbLongrope uses long factor if sequence is larger than original pretraining length, short otherwise.r original_max_position_embeddings long_inv_freqseq_leninv_freqF persistentN) torchmaxhasattrconfigr max_position_embeddings rope_init_fnr register_bufferoriginal_inv_freqto)self position_idsdevicer r _r^/var/www/html/alpaca_bot/venv/lib/python3.10/site-packages/transformers/modeling_rope_utils.pylongrope_frequency_update+s     z6dynamic_rope_update..longrope_frequency_updatecSst|d}||jkr#|j|j||d\}|_|jd|dd||_||jkrD|j|jkrF|j ||_|jd|jdd|j|_dSdSdS)a dynamic RoPE layers should recompute `inv_freq` in the following situations: 1 - growing beyond the cached sequence length (allow scaling) 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) rr r FrN) rrmax_seq_len_cachedrrattention_scalingroriginal_max_seq_lenrr)rrrr r rrrdynamic_frequency_update>s  z5dynamic_rope_update..dynamic_frequency_updatecsBd|jvr|||jdn |jdkr|||jd|||S)Ndynamic)rlongrope) rope_typer)rxrr#r rope_forwardrrwrapperQs   z$dynamic_rope_update..wrapperr)r)r*rr(rdynamic_rope_updates  r+rrz torch.devicer returnz torch.Tensorc Csv|j}t|dr |jnd}t|ddp|j|j}t||}d}d|tjd|dtj dj |tj d|}||fS) ax 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. 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). partial_rotary_factor?head_dimNrdtyperr2) rope_thetarr-getattr hidden_sizenum_attention_headsintrarangeint64rfloat) rrr baser-r/dimattention_factorr rrr _compute_default_rope_parameters\s ,r?cCs*|jd}t|||\}}||}||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. 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). factor) rope_scalingr?)rrr r@r r>rrr'_compute_linear_scaling_rope_parameterszs rBc Cs|j}t|dr |jnd}t|d|j|j}t||}|j}|jd}d} |dur.|}nt |t j rCt |t j ||j|jd}nt||}|||||d||d}d|t jd |dt jd j|t jd |} | | fS) a 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. 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). r-r.r/r@Nr2rrr0rr1r3)r4rr-r5r6r7r8rrA isinstancerTensormaximumtensorr2rrr9r:rr;) rrr r<r-r/r=rr@r>r rrr_compute_dynamic_ntk_parameterss$    $,rHcs|j}t|dr |jnd}t|d|j|j}t||}|jd}|jd}|jd} |jd} |jdp<|j } dd d } |d ur[| rW| rWt | || | || }n| |}|jd pbd} |jdpjd }ddfdd}dd}|t d|dj |t j d|}d|}d||}|jdd}|| |||| |\}}d ||||dj |t j d}|d |||}||fS)a Computes the inverse frequencies with NTK scaling. Please refer to the [original paper](https://huggingface.co/papers/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. 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. r-r.r/r@r>mscalemscale_all_dimr rcSs"|dkrdSd|t|dS)Nrr.g?)mathlog)scalerIrrr get_mscalesz,_compute_yarn_parameters..get_mscaleN beta_fast beta_slowcSs*|t||dtjdt|S)zPInverse dimension formula to find the dimension based on the number of rotationsr0)rKrLpi) num_rotationsr=r<rrrrfind_correction_dims*z5_compute_yarn_parameters..find_correction_dimcsL||||}||||}|rt|}t|}t|dt||dfS)z.Find dimension range bounds based on rotationsrr)rKfloorceilrmin)low_rothigh_rotr=r<rtruncatelowhighrTrrfind_correction_ranges   z7_compute_yarn_parameters..find_correction_rangecSs>||kr|d7}tj|tjd|||}t|dd}|S)NgMbP?r1rr)rr9float32clamp)rWrr= linear_func ramp_funcrrrlinear_ramp_factors z4_compute_yarn_parameters..linear_ramp_factorrr0r3rZT)r)r4rr-r5r6r7r8rAgetrr;rr9r)rrr r<r-r/r=r@r>rIrJr rNrOrQr^rc pos_freqsinv_freq_extrapolationinv_freq_interpolationrZr[r\inv_freq_extrapolation_factorr rr]r_compute_yarn_parameterss>        "    ricCs&|j}t|dr |jnd}t|d|j|j}t||}|jd}|jd}|jd} |jd} t|drB|j } |j |j } n|j } | d ur_| dkrPd} nt d t | t | } |ro|| krotj|tj|d } n tj|tj|d } tjd |d tj|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. 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. r-r.r/ long_factor short_factorr@r>r NrrCrr0)r4rr-r5r6r7r8rArdr rrKsqrtrLrrGr_r9r:r;)rrr r<r-r/r=rjrkr@r>r ext_factorsinv_freq_shaper rrr_compute_longrope_parameterss,       rocCst|||\}}|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. 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. r@low_freq_factorhigh_freq_factorr r0r)r?rArKrRrwhere)rrr r r>r@rprqold_context_lenlow_freq_wavelenhigh_freq_wavelenwaveleninv_freq_llama smooth_factorsmoothed_inv_freqis_medium_freqrrr_compute_llama3_parametersRs    r{)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)rArdsetkeysr)rrrAr&rrrrr!_validate_default_rope_parameterss  rcCsx|j}|d|dd}ddh}t|}t||||d|d}|dus0t|tr0|dkr:td|dSdS)Nr&rr@rr.8`rope_scaling`'s factor field must be a float >= 1, got rArdrrrrDr;rr)rrrAr&rrr@rrr(_validate_linear_scaling_rope_parameterss rcCs|j}|d|dd}ddh}dh}t|}t|||||d|d}|dus4t|tr4|dkr>td|dSdS)Nr&rr@r rr.rr)rrrAr&rrrr@rrr)_validate_dynamic_scaling_rope_parameterss rc Cs|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| d|jd} | dur|j | } | |krt d|d| d|ddSdSt ddS)Nr&rr@>rIrZrOrQrJr>r rr.rr>rL`rope_scaling`'s attention_factor field must be a float greater than 0, got rOz6`rope_scaling`'s beta_fast field must be a float, got rQz6`rope_scaling`'s beta_slow field must be a float, got rPrzO`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 zHThe explicitly set RoPE scaling factor (config.rope_scaling['factor'] = z) does not match the ratio implicitly set by other parameters (implicit factor = post-yarn context length / pre-yarn context length = config.max_position_embeddings / config.rope_scaling['original_max_position_embeddings'] = z). Using the explicit factor (z) in YaRN. This may cause unexpected behaviour in model usage, please correct the 'max_position_embeddings' fields in the model config.a~config.rope_scaling['original_max_position_embeddings'], the pre-yarn context length, is unset. We will **assume** config.max_position_embeddings holds the pre-yarn context length. Some use cases may expect config.max_position_embeddings to hold the post-yarn context length (pre-yarn context length * factor) -- we recommend updating both fields for optimal downstream model usage.) rArdrrrrDr;rrr warning_once) rrrAr&rrrr@r>rOrQr implicit_factorrrr_validate_yarn_parameterssR       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 krptd| d dt| |d} t | t st dd | Drtd| t| | d krtd| d dt| t|drtddS|d} | durtdnt | tr| dkrtd| |d} | durt | tr| dkrtd| dSdSdS)Nr&r>r&rjrk>r@r>r rr-r.r/rkcs|] }t|ttfVqdSNrDr8r;.0r'rrr z0_validate_longrope_parameters..zC`rope_scaling`'s short_factor field must be a list of numbers, got r0z5`rope_scaling`'s short_factor field must have length z, got rjcsrrrrrrrrrzB`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.r@z1Missing required keys in `rope_scaling`: 'factor'rr>gr)rArdrrrrr-r5r6r7r8rDlistallrrlenrr;)rrrAr&rrrr-r/r=rkrjr@r>rrr_validate_longrope_parameters sH        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>r@r&rprqr rr@r.rrprqz<`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=) rArdrrrrDr;rrr8r) rrrAr&rrr@rprqr rrr_validate_llama3_parameters;sL  rcCsdt|dd}|dur dS|d|dd}t|}|dur'|||ddStd|ddS) zO Validate the RoPE config arguments, given a `PretrainedConfig` object rANr&rr|rzTMissing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='')r5rdROPE_VALIDATION_FUNCTIONSrr)rrrAr& validation_fnrrrrope_config_validationjs   r)NNNr)NN)$rK functoolsrtypingrconfiguration_utilsrutilsrr get_logger__name__rrr+r8tupler;r?rBrHriror{ROPE_INIT_FUNCTIONSstrrrrrrrrrrrrrrrs    ?      .  X  7  -  B2&