a h|@s"ddlZddlmZddlmZddlmZddlmZm Z e e Z erTddl Z ddZd+eeed eeed efd d d Zd,eeed eeed efd ddZd-eeed eeed efd ddZd.ed eeed efd ddZd/ed eeed efd ddZd0ed eeed efd ddZeeeeeedZd1eeeeeeedddZd2eeedddZd3eeeddd Zd4eeedd!d"Zd5eeedd#d$Zd6eeedd%d&Z d7eeedd'd(Z!eeeee e!dZ"d8eeedd)d*Z#dS)9Nwraps)Optional)PretrainedConfig)is_torch_availableloggingcs,ddddtfdd}|S)ad Decorator function to update the RoPE parameters in the forward pass, if the model is using a dynamic RoPE (i.e. a RoPE implementation that may recompute its frequencies in the forward pass). Args: rope_forward (Callable): The forward pass of the RoPE implementation. Returns: The decorated forward pass. cSst|d}t|jdr$|jj}n|jj}||krnt|dsZ|j|j||dd\|_}|jd|jddn |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/assistant/venv/lib/python3.9/site-packages/transformers/modeling_rope_utils.pylongrope_frequency_update+s    z6dynamic_rope_update..longrope_frequency_updatecSst|d}||jkrF|j|j||d\}|_|jd|dd||_||jkr|j|jkr|j ||_|jd|jdd|j|_dS)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) rrZmax_seq_len_cachedrrZattention_scalingrZoriginal_max_seq_lenrr)rrrr r rrrdynamic_frequency_update>s z5dynamic_rope_update..dynamic_frequency_updatecsBd|jvr|||jdn|jdkr6|||jd|||S)Ndynamic)rlongrope) rope_typer)rxrr r rope_forwardrrwrapperQs   z$dynamic_rope_update..wrapperr)r&r'rr%rdynamic_rope_updates r(z torch.devicez torch.Tensor)rrr returnc Csv|j}t|dr|jnd}t|ddp0|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_dimNrdtyperr/) 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 r?c Cs|j}t|dr|jnd}t|d|j|j}t||}|j}|jd}d} |dur\|}n4t |t j rt |t j ||j|jd}n t||}|||||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=Nr/rrr-rr.r0)r1rr*r2r3r4r5rr> isinstancerZTensormaximumtensorr/rrr6r7rr8) rrr r9r*r,r:rr=r;r rrr_compute_dynamic_ntk_parameterss$    $,rDcs|j}t|dr|jnd}t|d|j|j}t||}|jd}|jd}|jd} |jd} |jdpx|j } dd d } |d ur| r| rt | || | || }n| |}|jd pd} |jdpd }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"|dkr dSd|t|dS)Nrr+g?)mathlog)scalerErrr 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 rotationsr-)rGrHpi)Z num_rotationsr:r9rrrrfind_correction_dimsz5_compute_yarn_parameters..find_correction_dimcsL||||}||||}|r4t|}t|}t|dt||dfS)z.Find dimension range bounds based on rotationsrr)rGfloorceilrmin)Zlow_rotZhigh_rotr:r9rtruncatelowhighrOrrfind_correction_ranges   z7_compute_yarn_parameters..find_correction_rangecSs>||kr|d7}tj|tjd|||}t|dd}|S)NgMbP?r.rr)rr6float32clamp)rRrr:Z linear_funcZ ramp_funcrrrlinear_ramp_factors z4_compute_yarn_parameters..linear_ramp_factorrr-r0rST)r)r1rr*r2r3r4r5r>getrr8rr6r)rrr r9r*r,r:r=r;rErFr rJrKrMrWrZZ pos_freqsZinv_freq_extrapolationZinv_freq_interpolationrSrTrUZinv_freq_extrapolation_factorr rrVr_compute_yarn_parameterss>        "   r\cCs&|j}t|dr|jnd}t|d|j|j}t||}|jd}|jd}|jd} |jd} t|dr|j } |j |j } n|j } | d ur| dkrd} nt d t | t | } |r|| krtj|tj|d } ntj|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 Nrr@rr-)r1rr*r2r3r4r5r>r[r rrGsqrtrHrrCrXr6r7r8)rrr r9r*r,r:r]r^r=r;r Z ext_factorsZinv_freq_shaper rrr_compute_longrope_parameterss,       r`cCst|||\}}|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 r-r)r<r>rGrNrwhere)rrr r r;r=rarbZold_context_lenZlow_freq_wavelenZhigh_freq_wavelenZwavelenZinv_freq_llamaZ smooth_factorZsmoothed_inv_freqZis_medium_freqrrr_compute_llama3_parametersRs    rd)defaultZlinearr!yarnr"Zllama3)r# received_keys required_keys optional_keys ignore_keyscCsd|vr|dh8}|d|dur,||8}||}|rLtd|d||durb|||}n||}|rtd|d|dS)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#rgrhrirjZ missing_keysZ unused_keysrrr_check_received_keyss   rp)rrjcCs@|j}|d|dd}dh}t|}t||||ddS)Nr#rkrj)r>r[setkeysrp)rrjr>r#rhrgrrr!_validate_default_rope_parameterss  rtcCst|j}|d|dd}ddh}t|}t||||d|d}|dus`t|tr`|dkrptd|dS)Nr#rkr=rqr+8`rope_scaling`'s factor field must be a float >= 1, got r>r[rrrsrprAr8rnro)rrjr>r#rhrgr=rrr(_validate_linear_scaling_rope_parameterss rwcCs||j}|d|dd}ddh}dh}t|}t|||||d|d}|dusht|trh|dkrxtd|dS)Nr#rkr=r rqr+rurv)rrjr>r#rhrirgr=rrr)_validate_dynamic_scaling_rope_parameterss rxc Cs|j}|d|dd}ddh}hd}t|}t|||||d|d}|dusjt|trj|dkrztd||d}|durt|tr|d krtd ||d } | durt| tstd | |d } | dur t| ts td| | pd| pdkr8td| d| d|jd} | dur|j | } | |krt d|d| d|dn t ddS)Nr#rkr=>rMr;rFrKrSrEr rqr+rur;rL`rope_scaling`'s attention_factor field must be a float greater than 0, got rKz6`rope_scaling`'s beta_fast field must be a float, got rMz6`rope_scaling`'s beta_slow field must be a float, got rLrzO`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.) r>r[rrrsrprAr8rnror warning_once) rrjr>r#rhrirgr=r;rKrMr Zimplicit_factorrrr_validate_yarn_parameterssP         r{cCs|j}|d|dd}hd}hd}t|}t|||||dt|drX|jnd}t|d|j|j }t ||} |d } t | t st d d | Drtd | t| | d krtd| d dt| |d} t | t st dd | Drtd| t| | d krJtd| d dt| t|drbtdn|d} | durtdn&t | tr| dkrtd| |d} | durt | tr| dkrtd| dS)Nr#rk>r]r#r^>r=r;r rqr*r+r,r^css|]}t|ttfVqdSNrAr5r8.0r$rrr z0_validate_longrope_parameters..zC`rope_scaling`'s short_factor field must be a list of numbers, got r-z5`rope_scaling`'s short_factor field must have length z, got r]css|]}t|ttfVqdSr|r}r~rrrrrzB`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'rur;gry)r>r[rrrsrprr*r2r3r4r5rAlistallrnrolenrzr8)rrjr>r#rhrirgr*r,r:r^r]r=r;rrr_validate_longrope_parameters sD           rc Cs6|j}|d|dd}hd}t|}t||||d|d}|dus`t|tr`|dkrptd||d}|d }|dust|tstd ||dust|tstd |||krtd |d ||d} | dust| t std| | |j kr2td| d|j dS)Nr#rk>r=r#rar rbrqr=r+rurarbz<`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=) r>r[rrrsrprAr8rnror5r) rrjr>r#rhrgr=rarbr rrr_validate_llama3_parameters;sJ  rcCsbt|dd}|durdS|d|dd}t|}|durL|||dntd|ddS) zO Validate the RoPE config arguments, given a `PretrainedConfig` object r>Nr#rkrerqzTMissing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='')r2r[ROPE_VALIDATION_FUNCTIONSrnro)rrjr>r#Z validation_fnrrrrope_config_validationjs   r)NNN)NNN)NNN)N)N)N)NN)N)N)N)N)N)N)N)$rG functoolsrtypingrZconfiguration_utilsrutilsrrZ get_logger__name__rnrr(r5tupler8r<r?rDr\r`rdZROPE_INIT_FUNCTIONSstrrrrprtrwrxr{rrrrrrrrs    ?   .  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