o ZhR@s<ddlmZddlmZmZddlZddlmZeGdddZ ddeej d eej e e fd ej d e d ee f d dZ ddeej d eej e e fd ej d e d ee f ddZddej dejdee fddZddej dejdee fddZ dd eej e e fdejdejd e d ee deej f ddZdS)) dataclass)OptionalUnionN)is_torchdynamo_compilingc@sDeZdZUdZeed<eed<d'dedeefddZ d(ded ed ed e j d e e j d fdee j f ddZ d'de j d ed e j d eede j f ddZe  d)de jd e j d e j dedeef ddZed'de j d e j deefddZede jdefdd Ze  !d*d"ee j d#e j dedeed$edef d%d&ZdS)+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_windowNcCs:||_||_|jdur|jdkrtd|jddSdS)NrzaMake sure that when passing `sliding_window` that its value is a strictly positive integer, not ``)rr ValueError)selfrr r T/var/www/auris/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py__init__9s zAttentionMaskConverter.__init__cpu batch_size query_lengthkey_value_lengthdtypedevicestrreturnc Cs\|js td|jd||f}||}d}|ddks!|jdur,|j|||||jd}|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 )rr __class__r _make_causal_mask) r rrrrr input_shapercausal_4d_maskr r r to_causal_4dBs z#AttentionMaskConverter.to_causal_4dattention_mask_2dc Cs|jd|f}d}|ddks|jdur0|jr0|durtd||}|j|||j||jd}n |jdur9td|j|||dd|j}|durW| | t |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 rr rrNotImplementedError _expand_maskto masked_fillbooltorchfinfomin) r r rrrrrrZexpanded_attn_maskexpanded_4d_maskr r rto_4dcs0  zAttentionMaskConverter.to_4drinput_ids_shaperc Cs|\}}tj||ft|j|d}tj|d|d}|||d|ddkd||}|dkrFtj tj ||||d|gdd}|durn||d} tj tj |tj d| d } trd|}|| t|j|ddddddf|d|||S) zJ Make causal mask used for bi-directional self-attention. )rrrrrr)dimN)r)diagonal)r(fullr)r*ZarangesizeZ masked_fill_viewr%catZzerosZtrilZ ones_liker'rcloneexpand) r-rrrr bszr!maskZ mask_condr0Z context_maskr r rrs "   (z(AttentionMaskConverter._make_causal_maskr8r!cCsj|\}}|dur |n|}|ddddddf|d|||}d|}||tjt|jS)zg Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. Nr?)r2r6r%r&r(r'r)r*)r8rr!r7Zsrc_len expanded_mask inverted_maskr r rr$s *z#AttentionMaskConverter._expand_maskr: min_dtypecCs0|jtjkr td|tj||kdddS)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)r/Zkeepdim)rr(r'r mulall)r:r<r r r_unmask_unattendeds *z)AttentionMaskConverter._unmask_unattendedFattention_mask inputs_embeds is_trainingc Cs|jd|jd}}||}tjpt|tjjpt}d} |dur<|s(|s:|dks0||kr:|dus8||kr:d} | S|dusD||kr`t|jdkrMdS|s`t |dkr`|dks^||kr`d} | 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 isinstancefxProxyrlenr>) r@rArr rB_rrrEignore_causal_maskr r r_ignore_causal_mask_sdpas( z/AttentionMaskConverter._ignore_causal_mask_sdpaN)rrN)NF)__name__ __module__ __qualname____doc__r'__annotations__intrrr(rrrTensorrr, staticmethodSizerr$Z FloatTensorfloatr?rLr r r rrs   & / !"  0rr@rrArr c Cstd|d}|d|}|dur%t|jdkr%|j||d||jd}|S|durdt|jdkrd|dd |d |f}t|j|krOtd t|jd |d d |}||t j t |jj }|S|j |d|d||j|jd}|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. Trr rN)rrrCrrz#Incorrect 4D attention_mask shape: z ; expected: .r9r.)rrIr"r,rtupler r&r%r(r'r)r*rr) r@rrArr attn_mask_converterrZexpected_shaper;r r r!_prepare_4d_causal_attention_mask.s,  r^c Cstd|d}|d|}tjpt|tjjpt}tj||||d}|r*d} | S|dur@|j |d|d||j |j d} | S| dkrI|} n |j ||d|j |d } |si| j jd kritj| t|j jd } | S) a Prepares the correct `attn_mask` argument to be used by `torch.nn.functional.scaled_dot_product_attention`. In case no token is masked in the `attention_mask` argument, we simply set it to `None` for the cases `query_length == 1` and `key_value_length == query_length`, and rely instead 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). TrYr)r@rArr Nrr.rC)rrcuda)r<)rr(rDrErFrGrHrrLrrrr/r,typer?r)r*) r@rrArr r]rrErKr+r r r*_prepare_4d_causal_attention_mask_for_sdpacs<    rar8rr!cCstj|||dS) Creates a non-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: mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` dtype (`torch.dtype`): The torch dtype the created mask shall have. tgt_len (`int`): The target length or query length the created mask shall have. r8rr!)rr$rcr r r_prepare_4d_attention_masks rdcCs^|j\}}|dur |n|}tjpt|tjjpt}|s't|dkr'dSt j |||dS)rbNrrc) r"r(rDrErFrGrHrr>rr$)r8rr!rJrrEr r r#_prepare_4d_attention_mask_for_sdpas rerrcCs8td|d}||d}|j|d|d|||d}|S)a/ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` Args: input_shape (`tuple(int)` or `list(int)` or `torch.Size`): The input shape should be a tuple that defines `(batch_size, query_length)`. dtype (`torch.dtype`): The torch dtype the created mask shall have. device (`int`): The torch device the created mask shall have. sliding_window (`int`, *optional*): If the model uses windowed attention, a sliding window should be passed. TrYrrr.)rr)rrrrr r]rr@r r r _create_4d_causal_attention_masks  rfrMrN) dataclassesrtypingrrr(Zutils.import_utilsrrrUrWr\listrTr^rarrdrerrfr r r rsb   : :