gQddlmZddlmZmZmZmZddlZddlm Z eGddZ ddeej d eej eefd ej d e d ee f d Z ddeej d eej eefd ej d e d ee f dZddej dejdee fdZddej dejdee fdZ dd eej eefdejdejd e d ee deej f dZdS)) dataclass)ListOptionalTupleUnionN)is_torchdynamo_compilingceZdZUdZeed<eed<d dedeefdZ d!deded ed e j d e e j d fd ee j f dZ d de j ded e j d eed e j f dZe d"de jd e j d e j dedeef dZed de j d e j deefdZede jdefdZe d#dee j de j dedeeded ef dZdS)$AttentionMaskConvertera9 A utility attention mask class that allows one to: - Create a causal 4d mask - Create a causal 4d mask with slided window - Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length, key_value_length) that can be multiplied with attention scores Examples: ```python >>> import torch >>> from transformers.modeling_attn_mask_utils import AttentionMaskConverter >>> converter = AttentionMaskConverter(True) >>> converter.to_4d(torch.tensor([[0, 0, 0, 1, 1]]), 5, key_value_length=5, dtype=torch.float32) tensor([[[[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38], [-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38], [-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38], [-3.4028e+38, -3.4028e+38, -3.4028e+38, 0.0000e+00, -3.4028e+38], [-3.4028e+38, -3.4028e+38, -3.4028e+38, 0.0000e+00, 0.0000e+00]]]]) ``` Parameters: is_causal (`bool`): Whether the attention mask should be a uni-directional (causal) or bi-directional mask. sliding_window (`int`, *optional*): Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer. is_causalsliding_windowNcz||_||_|j#|jdkrtd|jddSdS)NrzaMake sure that when passing `sliding_window` that its value is a strictly positive integer, not ``)r r ValueError)selfr r s a/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py__init__zAttentionMaskConverter.__init__9si",   *t/Ba/G/GKtxuHKKK  + */G/Gcpu batch_size query_lengthkey_value_lengthdtypedevicestrreturnc|jstd|jd||f}||z }d}|ddks|j||||||j}|S)z Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative bias to upper right hand triangular matrix (causal mask). z"Please use `to_causal_4d` only if z has `is_causal` set to True.Nrrpast_key_values_lengthr )r r __class__r _make_causal_mask) rrrrrr input_shaper causal_4d_masks r to_causal_4dz#AttentionMaskConverter.to_causal_4dBs~ qo$.ooopp p"<0 !1L!@ r?Q  $"5"A!33'=#2 4Nrattention_mask_2dc|jd|f}d}|ddks|jB|jr;|td||z }||||j||j}n|jt d||||d|j}|?| | tj |j }|} | S) a Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length, key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is causal, a causal mask will be added. rNrrzpThis attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask.rz?Sliding window is currently only implemented for causal masking)tgt_len)shaper r rr"rNotImplementedError _expand_maskto masked_fillbooltorchfinfomin) rr&rrrr#r$r expanded_attn_maskexpanded_4d_masks rto_4dzAttentionMaskConverter.to_4dcs5).q1<@  Oa  4#6#B#B' G&6 %D "!33(/'=#2 4NN ,%&ghh h"../@%Q\]_Q`.aadd  $    %!/!;!;>>  )y1}&:&:499R==!&L&LLaPPPwwu~~ !A % %9ek'3IQV_efffhlmsuvvvD  %->BH :eod%*&M&M&MX`aaaL   lEK,>,>,B C C CD$111$%,,S!WgH^>^___rrEr(cL|\}}||n|}|ddddddf|d|||}d|z }||tjt j|jS)zg Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. Nr?)r<rCr,r-r/r.r0r1)rErr(rDsrc_len expanded_mask inverted_masks rr+z#AttentionMaskConverter._expand_masks yy{{ W$0''gQQQdAAA-.55c1gwOORRSXYY m+ (()9)9%*)E)Eu{SXGYGYG]^^^rrK min_dtypec|jtjkrtd|tj||kddS)a Attend to all tokens in masked rows from the expanded attention mask, for example the relevant first rows when using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. Details: https://github.com/pytorch/pytorch/issues/110213 `expanded_mask` is [bsz, num_masks, tgt_seq_len, src_seq_len] or [bsz, tgt_seq_len, src_seq_len]. `attention_mask` is [bsz, src_seq_len]. The dimension num_masks of `expanded_mask` is most often 1, but it can also be the number of heads in the case of alibi attention bias. For example, if `expanded_mask` is (e.g. here left-padding case) ``` [[[[0, 0, 0], [0, 0, 0], [0, 0, 1]]], [[[1, 0, 0], [1, 1, 0], [1, 1, 1]]], [[[0, 0, 0], [0, 1, 0], [0, 1, 1]]]] ``` then the modified `expanded_mask` will be ``` [[[[1, 1, 1], <-- modified [1, 1, 1], <-- modified [0, 0, 1]]], [[[1, 0, 0], [1, 1, 0], [1, 1, 1]]], [[[1, 1, 1], <-- modified [0, 1, 0], [0, 1, 1]]]] ``` z\AttentionMaskConverter._unmask_unattended expects a float `expanded_mask`, got a BoolTensor.rT)r8keepdim)rr/r.rmulall)rKrMs r_unmask_unattendedz)AttentionMaskConverter._unmask_unattendedsZT  %* , ,n   %)MY,FBX\"]"]"]!]^^^rFattention_mask inputs_embeds is_trainingc|jd|jd}}||z}tjp,t |tjjp t}d} ||s|s|dks||kr |||krd} nJ|||krBt|jdkrdS|s&tj |dkr|dks||krd} | S)a9 Detects whether the optional user-specified attention_mask & the automatically created causal mask can be ignored in case PyTorch's SDPA is used, rather relying on SDPA's `is_causal` argument. In case no token is masked in the `attention_mask` argument, if `query_length == 1` or `key_value_length == query_length`, we rather rely on SDPA `is_causal` argument to use causal/non-causal masks, allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed). rrFNT) r)r/jit is_tracing isinstancefxProxyr lenrQ) rSrTr r rU_rrrYignore_causal_masks r_ignore_causal_mask_sdpaz/AttentionMaskConverter._ignore_causal_mask_sdpas$(-a0-2Ea2H<'*@@Y))++vz-/X/Xv\t\v\v "  ! *$. *!Q&&*:l*J*J#+/?./P/P%)"  #'7.'H'H>'((A--u .EIn.A$B$B .1$$(8L(H(H)-&"!rN)rrN)NF)__name__ __module__ __qualname____doc__r.__annotations__intrrr/rrrTensorr%r4 staticmethodSizer"r+ FloatTensorfloatrRr`rrr r s<OOO$ .3   {  elE)*  %, L+/ - -  <- - { - #3- -  - - - - ^ '((, ```{` `!$ ` ! ```\`: _ _5< _ _hsm _ _ _\ _._(._._._._\._` )-! 7"7" .7"|7"!$7"! 7"  7"  7"7"7"\7"7"7"rr rSr#rTr r ctd|}|d|z}|=t|jdkr%|||d||j}n|t|jdkr|dd |d |f}t |j|kr(t d t |jd |d d |z }||tj tj |jj }n0| |d|d||j|j}|S)a Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)` Args: attention_mask (`torch.Tensor` or `None`): A 2D attention mask of shape `(batch_size, key_value_length)` input_shape (`tuple(int)` or `list(int)` or `torch.Size`): The input shape should be a tuple that defines `(batch_size, query_length)`. inputs_embeds (`torch.Tensor`): The embedded inputs as a torch Tensor. past_key_values_length (`int`): The length of the key value cache. sliding_window (`int`, *optional*): If the model uses windowed attention, a sliding window should be passed. Tr r rN)rrrWrrz#Incorrect 4D attention_mask shape: z ; expected: .rIr7)r r]r)r4rtuplerr-r,r/r.r0r1r%r) rSr#rTr r attn_mask_converterrexpected_shaperLs r!_prepare_4d_causal_attention_maskrv*sx.14P^___"2)??!c.*>&?&?1&D&D,22 KO>NVcVi3    #N,@(A(AQ(F(F%a.![^=MN % & &. 8 8peNrsg"!!!!!//////////// 888888 P"P"P"P"P"P"P" P"p%) 11U\*1uz5$./1<1 1 SM 1111t%) 77U\*7uz5$./7<7 7 SM 7777t X XU\ X%+ XPXY\P] X X X X \\el\5;\YabeYf\\\\:#$$( uz5$./ ; L  SM  el r