g"ddlZddlmZmZmZmZddlZddlmcm Z ddl ZddlmZddl m Z ddlmZmZmZddlmZddlmZdd lmZdd lmZmZmZmZmZdd lmZdd l m!Z!dd l"m#Z#ddl$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*ddl+m,Z,e)j-e.Z/dZ0dZ1Gddej2Z3e#j4e3Gddej2Z5Gdde5Z6Gdde5Z7dZ8d>dZ9Gddej2Z:dej;d eGd%d&e>Z?Gd'd(e>Z@e>e?e@d)ZAGd*d+ej2ZBd,ZCe&d-eCGd.d/e!ZDd0ZEe&d-eCGd1d2eDZFGd3d4eDeZGe&d5eCGd6d7eDZHe&d8eCGd9d:eDZIe&d;eCGd<d=eDZJdS)?N)ListOptionalTupleUnion)nn)ACT2FN)Cache DynamicCache StaticCache)GenerationMixin)AttentionMaskConverter)_flash_attention_forward)BaseModelOutputWithPastCausalLMOutputWithPastQuestionAnsweringModelOutput SequenceClassifierOutputWithPastTokenClassifierOutput)ROPE_INIT_FUNCTIONS)PreTrainedModel)ALL_LAYERNORM_LAYERS)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forward#is_flash_attn_greater_or_equal_2_10loggingreplace_return_docstrings) LlamaConfigzmeta-llama/Llama-2-7b-hfrc,eZdZdfd ZdZdZxZS) LlamaRMSNormư>cttjt j||_||_dS)z; LlamaRMSNorm is equivalent to T5LayerNorm N)super__init__r Parametertorchonesweightvariance_epsilon)self hidden_sizeeps __class__s d/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.pyr%zLlamaRMSNorm.__init__=sD l5:k#:#:;; #c|j}|tj}|ddd}|tj||jzz}|j||zS)NT)keepdim) dtypetor'float32powmeanrsqrtr*r))r+ hidden_states input_dtypevariances r/forwardzLlamaRMSNorm.forwardEs|#) %((77  $$Q'',,R,>>% Ht?T4T(U(UU {]--k::::r0cHt|jjd|jS)Nz, eps=)tupler)shaper*r+s r/ extra_reprzLlamaRMSNorm.extra_reprLs& )**II$2GIIIr0)r")__name__ __module__ __qualname__r%r>rC __classcell__r.s@r/r!r!<sb$$$$$$;;;JJJJJJJr0r!cveZdZ d deeffd ZdZejd Z xZ S) LlamaRotaryEmbeddingN'?defaultconfigcXti|_|=td|||||d|_||_||_||_n_|j9|j d|j d|_nd|_|j |_|j |_||_ t|j|_ |j |j |fi|j\}|_|d|d|j|_dS) Nz`LlamaRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46) rope_typefactordimbasemax_position_embeddingsrQtyperNinv_freqF persistent)r$r% rope_kwargslogger warning_oncerQmax_seq_len_cachedoriginal_max_seq_len rope_scalinggetrUrOr rope_init_fnattention_scalingregister_bufferrWoriginal_inv_freq) r+rSrUrTdevicescaling_factorrQrOrWr.s r/r%zLlamaRotaryEmbedding.__init__TsC  >   R    '(+B   D 'DN&=D #(?D % %".!'!4!8!8fFYF]F]^dFeFe!f!f!*&,&DD #(.(FD % /?+<4+zLlamaRotaryEmbedding.forwards  & &  * *< * I I I!M$4-8>>@@GG HZ[\H]_acdee ,QQQaaaZ 8 > > @ @hm %/ S%A%AekUZFZFZkk`e ^ U C C C  &,,..1F1L1L1N1NNYYZ[]^__E)UEN333C''))C''))C                D**D**vvAGv$$cff17f&;&;;;s A>EEE)NrKrLNrMrNN) rDrErFrrr%rkr'no_gradr>rGrHs@r/rJrJSs  $ (,*/*/%*/*/*/*/*/*/X@@@$U]__<<_<<<<ee*********r0rc"eZdZdZfdZxZS)%LlamaDynamicNTKScalingRotaryEmbeddingzmLlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozillacxtdd|d<tj|i|dS)Nz`LlamaDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use `LlamaRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to __init__).rmrQrrs r/r%z.LlamaDynamicNTKScalingRotaryEmbedding.__init__sN    ({$)&)))))r0rrHs@r/rrs>ww*********r0rc|dd|jddzf}|d|jddzdf}tj| |fdS)z*Rotates half the hidden dims of the input..Nr3r2rs)rAr'rz)r}x1x2s r/ rotate_halfrs] 3"!'"+"" " #B 3 q "" " #B 9rc2YB ' ' ''r0c||}||}||zt||zz}||zt||zz}||fS)aApplies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. ) unsqueezer)qkr{r|rj unsqueeze_dimq_embedk_embeds r/apply_rotary_pos_embrsc( -- & &C -- & &C3w;q>>C/0G3w;q>>C/0G G r0c$eZdZfdZdZxZS)LlamaMLPct||_|j|_|j|_t j|j|j|j|_t j|j|j|j|_ t j|j|j|j|_ t|j |_ dS)Nbias)r$r%rOr,intermediate_sizerLinearmlp_bias gate_projup_proj down_projr hidden_actact_fnr+rOr.s r/r%zLlamaMLP.__init__s  !-!'!94#3T5KRXRabbby!143IPVP_``` 4#94;KRXRabbbV./ r0cp |jjdkrN|j|jjz}|jj|d|jj|d |jj|dtj fdt|jjDd}tj fdt|jjDd}| ||z|dfdt|jjD}t|}nQ|| ||z}|S) NrrrscFg|]}tj|SFr).0igate_proj_slicesr}s r/ z$LlamaMLP.forward..s+]]]a!-a011]]]r0r3cFg|]}tj|Srr)rrup_proj_slicesr}s r/rz$LlamaMLP.forward..s* k k kA!^A->!?!? k k kr0r2cRg|]#}tj||$Srr)rrdown_proj_slicesintermediate_statess r/rz$LlamaMLP.forward..s>JK,Q/1A!1DEEr0) rOpretraining_tprrr)splitrrr'rzrangersum) r+r}slicerrrrrrrs ` @@@@r/r>zLlamaMLP.forwards ; % ) )*dk.HHE#~4::5a:HH !\066u!6DDN#~4::5a:HH  ]]]]]5A[;\;\]]]ceIi k k k k kt{OiIjIj k k kqstttG#';;y#9#9G#C"J"J5VW"J"X"X OTUYU`UoOpOpIIIIt{{4>>!3D3D'E'E UV'WXXIr0)rDrErFr%r>rGrHs@r/rrsG00000r0rr;n_repreturnc|j\}}}}|dkr|S|dddddddddf|||||}||||z||S)z This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) rN)rArureshape)r;rbatchnum_key_value_headsslenhead_dims r/ repeat_kvrs 2?1D.E h zz!!!!QQQaaa"23::5BUW\^bdlmmM  (;e(CT8 T TTr0cReZdZdZddedeeffd Z ddej deej d eej d ee d e d e d eej dee ej ej fde ej eej ee ej ffdZxZS)LlamaAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNrO layer_idxct||_||_|(td|jjd|j|_|j |_ |j |_ t|d|j |j z|_ |j|_|j |jz|_|j|_|j|_d|_t'j|j |j |j z|j|_t'j|j |j|j z|j|_t'j|j |j|j z|j|_t'j|j |j z|j |j|_t5|j|_dS)NzInstantiating z without passing a `layer_idx` is not recommended and will lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` when creating this class.rTrrO)r$r%rOrr[r\r.rDattention_dropoutr,num_attention_heads num_headsgetattrrrnum_key_value_groupsrU rope_theta is_causalrrattention_biasq_projk_projv_projo_projrJ rotary_embr+rOrr.s r/r%zLlamaAttention.__init__s  "     ,!8,,,    "(!9!-3 D4D4VWW #)#= $(Nd6N$N!'-'E$ +i 0$.4=2PW]Wlmmm i 0$2JT]2Zagavwww i 0$2JT]2Zagavwww i >@PW]Wlmmm /dkBBBr0Fr;attention_maskrjpast_key_valueoutput_attentions use_cachecache_positionposition_embeddingsrc  \} } } |jjdkrN|j|jz|jjz} |jj|j|jz|jjzd|j j| d|j j| dfdt|jjD}tj |d}fdt|jjD}tj |d}fdt|jjD}tj |d}n?|}| }| }|| | |j|jdd}|| | |j|jdd}|| | |j|jdd}|4t d |||\}}n|\}}t'||||\}}|&|||d }||||j|\}}t-||j}t-||j}tj||dd t3j|jz }|$|ddddddd|jd f}||z}t8j|dtj  |j!}t8j"||j#|j$}tj||| |j| |jfkr5tKd| |j| |jfddd&'| | d|jjdkr|j(|jjzd|j)j|j(|jjzdtUfdt|jjDn|)|sd}||fS)NrrrscFg|]}tj|Srr)rrr; query_slicess r/rz*LlamaAttention.forward..I)pppAH]LODDpppr0r3cFg|]}tj|Srr)rrr; key_slicess r/rz*LlamaAttention.forward..Ls)lllQ!(=*Q-@@lllr0cFg|]}tj|Srr)rrr; value_slicess r/rz*LlamaAttention.forward..Orr0r2YThe attention layers in this model are transitioning from computing the RoPE embeddings internally through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed `position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be removed and `position_embeddings` will be mandatory.r|r{rr)rSr5)ptrainingz `attn_output` should be of size z , but is cRg|]#}tj||$Srr)rr attn_output o_proj_slicess r/rz*LlamaAttention.forward..s.uuuaqx A a8HIIuuur0)+sizerOrrrrr)rrrrrr'rzviewryr[r\rrupdaterrrmatmulmathsqrtrAr functionalsoftmaxr7r6r5dropoutrr ValueError contiguousrr,rr)r+r;rrjrrrrrrbszq_len_key_value_slicing query_states key_states value_statesr{r| cache_kwargs attn_weights causal_maskrrrrrs ` @@@@@r/r>zLlamaAttention.forward3sE&**,, UA ; % ) )!%!9DM!IdkNh h ;-33$-/DK4NNTU4L+112C1KKJ;-334E13MMLpppppeTXT_TnNoNopppL 9\r:::Llllll%PTP[PjJkJklllJ:2666JpppppeTXT_TnNoNopppL 9\r:::LL ;;}55L]33J;;}55L#((eT^T]SS]]^_abcc __S%1I4=YYccdeghii #((eT5Mt}]]gghiklmm  &   G    |\BBHC*HC#7 jRUWZ#[#[ j  %#&snUUL'5'<'>     #t~udm!L L L)CPTP]3^))$$&&))  "++Aq11<<>> !))#ub99 ; % ) )%++D,< @Z,Z`a+bbK K.44T5EIc5cij4kkMuuuuuSXY]YdYsStStuuuvvKK++k22K  LL.88r0NNNNFFNN)rDrErFrrrintr%r'Tensor LongTensorr boolrr>rGrHs@r/rrsOGGCC{Cx}CCCCCC@2637*."'59KOZ9Z9|Z9!.Z9u/0 Z9 ! Z9  Z9Z9!!12Z9&eEL%,,F&GHZ9 u|Xel3XeEL>Q5RR SZ9Z9Z9Z9Z9Z9Z9Z9r0rc:eZdZdZfdZ ddejdeejdeejdee d e d e d eejd ee ejejfd e ejeejee ejffdZ xZ S)LlamaFlashAttention2aF Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. cbtj|i|t |_dSr)r$r%r_flash_attn_uses_top_left_maskrs r/r%zLlamaFlashAttention2.__init__s9$)&))) 3V2W2W.W+++r0NFr;rrjrrrrrrc bt|trtdd}|\} } } ||} ||} ||}| | | |j|j  dd} | | | |j |j  dd} || | |j |j  dd}|4t d|||\}}n|\}}t| | ||\} } |&|||d}|| ||j|\} }| dd} | dd} | dd}|jr|jnd}| j}|t*jkrt+jrt+j}n3t3|jdr |jj}n|jjj}t d |d | |} | |} ||}t=| | ||| ||t?|d d|j |j! }|"| | d #}|$|}|sd}|||fS)Nz`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformersFrr2rr_pre_quantization_dtypezThe input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in .sliding_window)rjrruse_top_left_maskrr3)%rvr rrrrrrrrryrr[r\rrrrrrr5r'r7is_autocast_enabledget_autocast_gpu_dtypehasattrrOrr)r6rrr rrrr)r+r;rrjrrrrrrrrrrrr{r|r dropout_rater< target_dtyperrs r/r>zLlamaFlashAttention2.forwards\ nk 2 2 }  "%**,, UA{{=11 [[// {{=11 $((eT^T]SS]]^_abcc __S%1I4=YYccdeghii #((eT5Mt}]]gghiklmm  &   G    |\BBHC*HC#7 jRUWZ#[#[ j  %#&snUUL'5'<'rGrHs@r/r r s3 XXXXX6:37*."'59KOf9f9|f9!!12f9u/0 f9 ! f9  f9f9!!12f9&eEL%,,F&GHf9 u|Xel3XeEL>Q5RR Sf9f9f9f9f9f9f9f9r0r c4eZdZdZ ddejdeejdeejdeede d e d eejd ee ejejfd e ejeejee ejfffd Z xZ S)LlamaSdpaAttentionz Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. NFr;rrjrrrrrrc |rCtdt||||||||S|\} } } ||} ||}||}| | | |j |j  dd} || | |j |j  dd}|| | |j |j  dd}|4td| ||\}}n|\}}t| |||\} }|&|||d}||||j|\}}t#||j}t#||j}|}||ddddddd|jdf}| jjdkr>|<| } |}|}|| dkrd nd }t.jj| ||||jr|jnd | }| dd}|| | d }||}|d|fS)NaLlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.r;rrjrrrrrrr2rrrcudaTFr) attn_mask dropout_prr3)r[r\r$r>rrrrrrrryrrrrrrrrArerVrr'rrscaled_dot_product_attentionrrr)r+r;rrjrrrrrrrrrrrrr{r|rrrrr.s r/r>zLlamaSdpaAttention.forwards/     [   77??+-)-"3#-$7#   &**,, UA{{=11 [[// {{=11 #((eT^T]SS]]^_abcc __S%1I4=YYccdeghii #((eT5Mt}]]gghiklmm  &   G    |\BBHC*HC#7 jRUWZ#[#[ j  %#&snUUL'5'<'> !&&sE266 kk+.. D.00r0r) rDrErFrr'rrrr r rr>rGrHs@r/rrs 2637*."'59KOX1X1|X1!.X1u/0 X1 ! X1  X1X1!!12X1&eEL%,,F&GHX1 u|Xel3XeEL>Q5RR SX1X1X1X1X1X1X1X1X1X1r0r)eagerflash_attention_2sdpacNeZdZdedeffd Z ddejdeejdeej d ee d ee d ee d eej d ee ejejfde ej ee ej ej fffdZxZS)LlamaDecoderLayerrOrcJt|j|_t|j|||_t ||_t|j|j |_ t|j|j |_ dS)N)rOrr-) r$r%r,LLAMA_ATTENTION_CLASSES_attn_implementation self_attnrmlpr! rms_norm_epsinput_layernormpost_attention_layernormrs r/r%zLlamaDecoderLayer.__init__ss !-01LMU[gpqqqF##+F,>FDWXXX(4V5GVM`(a(a(a%%%r0NFr;rrjrrrrrrc |} ||}|jd||||||||d| \}} } | |z}|} ||}||}| |z}|f} |r| | fz } |r| | fz } | S)a. Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, query_sequence_length, key_sequence_length)` if default attention is used. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, with `head_dim` being the embedding dimension of each attention head. kwargs (`dict`, *optional*): Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code into the model rr)r-r*r.r+)r+r;rrjrrrrrrresidualself_attn_weightspresent_key_valueoutputss r/r>zLlamaDecoderLayer.forward}sD!,,];; ?Mdn ? ')%)/) 3 ? ?  ? ? ; (*;!=0 !55mDD //  =0  "  , )+ +G  , )+ +Gr0r)rDrErFrrr%r'rrrr r r FloatTensorr>rGrHs@r/r%r%rsGb{bsbbbbbb2637*.,1$)59KOBB|B!.Bu/0 B ! B $D> BD>B!!12B&eEL%,,F&GHB u (51BEDU1U+V"WW XBBBBBBBBr0r%aJ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`LlamaConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. zSThe bare LLaMA Model outputting raw hidden-states without any specific head on top.c@eZdZeZdZdZdgZdgZdZ dZ dZ dZ dZ dZdS)LlamaPreTrainedModelmodelTr%past_key_valuesc|jj}t|tjrJ|jjd||j |jj dSdSt|tj rS|jjd||j -|jj|j  dSdSdS)Nr)r9std) rOinitializer_rangervrrr)datanormal_rzero_ Embedding padding_idx)r+moduler:s r/ _init_weightsz"LlamaPreTrainedModel._init_weightssk+ fbi ( ( ? M  & &CS & 9 9 9{&  &&((((('&  - - ? M  & &CS & 9 9 9!- "6#56<<>>>>> ? ?--r0N)rDrErFr config_classbase_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_2_supports_sdpa_supports_cache_class_supports_quantized_cache_supports_static_cacherBrr0r/r6r6se L&*#,-#4"5!N  $! ? ? ? ? ?r0r6a Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. c eZdZdZdeffd ZdZdZee  dde j de e j d e e j d e eeee jfd e e jd e ed e ede ede ede e j deeeffdZde j de j de j d ed ef dZede j dedede jde jde j defdZxZS) LlamaModelz Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] Args: config: LlamaConfig rOctj|_j|_t jjj|j|_t j fdtj D|_ tjj|_t!|_d|_|dS)Nc0g|]}t|Sr)r%)rrrOs r/rz'LlamaModel.__init__..Ms$ c c ci vy 1 1 c c cr0r'rF)r$r% pad_token_idr@ vocab_sizerr?r, embed_tokens ModuleListrnum_hidden_layerslayersr!r,normrJrgradient_checkpointing post_initrs `r/r%zLlamaModel.__init__Fs    !. +L):Fz%LlamaModel.forward..s(ttqfgfsfsfsfsfsttr0)last_hidden_stater8r; attentions)rOrrcruse_return_dictrrXrr[r\rSrvr r from_legacy_cacheget_seq_lengthr'arangerArer_update_causal_maskrrV_gradient_checkpointing_func__call__rWto_legacy_cacher@r)r+rarrjr8rbrrrcrdrreturn_legacy_cachepast_seen_tokensrr;rall_hidden_statesall_self_attnsnext_decoder_cache decoder_layer layer_outputs next_caches r/r>zLlamaModel.forward\s2C1N--TXT_Tq$8$D $+Jj "+!6IIDK?L]  & #oom\JJ#7@BBD0:d!![" 6" 6M# 6!m%55!* t}  $ A A!*! #%"' ! ! !. !#.!-#2&7'#1(; ! ! ! *!,M R%28I3P11q%Q"  6=#3"55 -00   2 -!1 1 +4>''$  6#3355J utt]J@QSa$bttttt t&+&+%     r0 input_tensorc |jjdkr |d|vr|SdS||nd}t|t}|jjdkr#|s!|st j||||jrdS|j|j } }|j d} |r| } n/t|tj r |j dn|| zdz} ||| | || ||j d} |jjdkrB|@|j jd kr0|s.tj|j} t j| | } | S) Nr"rrr#)rbpast_key_values_length is_trainingrr3)sequence_length target_lengthr5rer batch_sizer)rOr)rmrvr r_ignore_causal_mask_sdparr5rerAget_max_cache_shaper'r5_prepare_4d_causal_attention_mask_with_cache_positionrVfinfomin_unmask_unattended)r+rr{rr8rrtusing_static_cacher5rerrr min_dtypes r/rozLlamaModel._update_causal_masks ; +/B B B)c^.C.C%%4 @O?Z?99;;;`a'EE ; +v 5 5>P 5Yj 5%>*'7 M   t$*L,?v&,Q/  +??AAMMnel;;<$R((%7!; PP +')#)!,Q   K , 6 6*%*f44%5  E**.I0CKQZ[[Kr0rrr5rerc ||dkr|}n+tj|j} tj||f| ||}|dkrtj|d}|tj|||ddkz}|ddddddf|ddd}|| }|j d} |ddddddd| f|ddddddfz} | dk} |ddddddd| f | | |ddddddd| f<|S) a Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to plcae the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. N) fill_valuer5rer)diagonalrnr3r) rSr'rrfulltriurnrruclonerA masked_fill) rrrr5rerrrrr mask_length padding_masks r/rz@LlamaModel._prepare_4d_causal_attention_mask_with_cache_positionsB  %.*<*<*>*>!*C*C(KK E**.I* -0Ye\bK!###jqAAA 5< fEEEH^H^_acdHeHee eK%dD!!!QQQ&67>>z1bRTUUK))//11 ,226 *111aaaL[L+@ANSTSTSTVZ\`bcbcbcScDdd +q0 5@AAAqqq,;,AV5W5c5c )66 AAAqqq!!!\k\12r0 NNNNNNNNNN)rDrErFrrr%r\r`rLLAMA_INPUTS_DOCSTRINGr'rrrrr rr4r rrr>ro staticmethodrr5rerrGrHs@r/rNrN:sE { !!!"""+*+ABB'+1537KO59$(,0/3&*59x x #x !.x u/0 x "%tE4E/F(F"GH x   12 x D>x $D>x 'tnx d^x !!12x  u-- .x x x CBx t? ?l? ?  ?  ????B5 555{ 5  5  5555\55555r0rNceZdZdgZfdZdZdZdZdZdZ dZ e e e ee  dd ejd eejdeejdeeeeejfdeejdeejdeedeedeedeedeejdedeeeffdZxZS)LlamaForCausalLMzlm_head.weightct|t||_|j|_t j|j|jd|_| dSNFr) r$r%rNr7rRrrr,lm_headrYrs r/r%zLlamaForCausalLM.__init__Tsj    ''  +y!3V5FUSSS  r0c|jjSrr7rSrBs r/r\z%LlamaForCausalLM.get_input_embeddings] z&&r0c||j_dSrrr^s r/r`z%LlamaForCausalLM.set_input_embeddings`"' r0c|jSrrrBs r/get_output_embeddingsz&LlamaForCausalLM.get_output_embeddingscs |r0c||_dSrr)r+new_embeddingss r/set_output_embeddingsz&LlamaForCausalLM.set_output_embeddingsfs % r0c||_dSrr7)r+decoders r/ set_decoderzLlamaForCausalLM.set_decoderis  r0c|jSrrrBs r/ get_decoderzLlamaForCausalLM.get_decoderls zr0) output_typerCNrrarrjr8rblabelsrrrcrdrnum_logits_to_keeprc ||n |jj}| | n |jj} | | n |jj} ||||||||| | |  }|d|jjdkrp|jj|j |jjzdfdt|jjD}tj |d}n%|dd| dddf}d}||j d |||jj d| }| s|f|ddz}||f|zn|St|||j|j|j S) a; Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. num_logits_to_keep (`int`, *optional*): Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. Returns: Example: ```python >>> from transformers import AutoTokenizer, LlamaForCausalLM >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```N) rarrjr8rbrrrcrdrrrrscFg|]}tj|Srr)rrr;lm_head_slicess r/rz,LlamaForCausalLM.forward..s*lllQah}nQ.?@@lllr0r3)logitsrrRlossrr8r;rjr)rOrrcrkr7rrr)rrRrr'rz loss_functionrr8r;rj)r+rarrjr8rbrrrrcrdrr loss_kwargsr3rroutputr;rs @@r/r>zLlamaForCausalLM.forwardos`2C1N--TXT_Tq$8$D $+Jj &1%rGrHs@r/rrQs*+'''(((&&&+*+ABB+AP_```'+1537KO59-1$(,0/3&*59"#Y Y #Y !.Y u/0 Y "%tE4E/F(F"GH Y   12 Y )*Y D>Y $D>Y 'tnY d^Y !!12Y  Y  u,, -Y Y Y a`CBY Y Y Y Y r0ra The LLaMa Model transformer with a sequence classification head on top (linear layer). [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). cveZdZfdZdZdZee ddee j dee j dee j dee e ee jfd ee jd ee j d eed eed eedeede eeffdZxZS)LlamaForSequenceClassificationct||j|_t||_t j|j|jd|_| dSr) r$r% num_labelsrNr7rrr,scorerYrs r/r%z'LlamaForSequenceClassification.__init__si     +'' Yv14?OOO  r0c|jjSrrrBs r/r\z3LlamaForSequenceClassification.get_input_embeddingsrr0c||j_dSrrr^s r/r`z3LlamaForSequenceClassification.set_input_embeddingsrr0Nrarrjr8rbrrrrcrdrc "| | n |jj} ||||||||| |  } | d} || } ||jd}n |jd}|jj|dkrt d|jjd}nv|rtj||jj ddz }||jdz}| | j }nd}| tj || j |f}d}||| |||j}| s|f| ddz}||f|zn|St||| j| j| j S)  labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Nrrjr8rbrrrcrdrrz=Cannot handle batch sizes > 1 if no padding token is defined.r3rn)rr pooled_logitsrOr)rOrkr7rrArQrr'eqrargmaxr6rernrrr8r;rj)r+rarrjr8rbrrrrcrdtransformer_outputsr;rrsequence_lengthsrrrs r/r>z&LlamaForSequenceClassification.forwards(&1%rGrHs@r/rrs '''(((+*+ABB151537KO59-1$(,0/3&*E E E,-E !.E u/0 E "%tE4E/F(F"GH E   12 E )*E D>E $D>E 'tnE d^E  u66 7E E E CBE E E E E r0rz The Llama Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). ceZdZdZfdZdZdZee dde e j de e j de e j d e e eee j fd e e j d e e j d e e j d e ede ede ede eeffdZxZS)LlamaForQuestionAnswering transformerct|t||_t j|jd|_|dS)Nr2) r$r%rNrrrr, qa_outputsrYrs r/r%z"LlamaForQuestionAnswering.__init__@sY    %f--)F$6:: r0c|jjSrrrSrBs r/r\z.LlamaForQuestionAnswering.get_input_embeddingsHs,,r0c||j_dSrrr^s r/r`z.LlamaForQuestionAnswering.set_input_embeddingsKs(-%%%r0Nrarrjr8rbstart_positions end_positionsrrcrdrc | | n |jj} |||||||| | } | d} || }|dd\}}|d}|d}d}|||j||||fi| }| s||f| ddz}||f|zn|St|||| j | j S) a start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. N)rrjr8rbrrcrdrrr3rsr2)r start_logits end_logitsr;rj) rOrkrrrsqueezerrrr;rj)r+rarrjr8rbrrrrcrdrr3sequence_outputrrrrrs r/r>z!LlamaForQuestionAnswering.forwardNsR2&1%rGrHs@r/rr5s{&---...+*+ABB156:37KO596:48,0/3&*: : E,-: !!23: u/0 : "%tE4E/F(F"GH :   12 : "%"23:   01: $D>: 'tn: d^:  u22 3: : : CB: : : : : r0rz The Llama Model transformer with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. ceZdZfdZdZdZeeee e e  dde e jde e jde e jd e ee jd e e jd e e jd e ed e ede ede edeee ffdZxZS)LlamaForTokenClassificationct||j|_t||_t |dd|j}nt |dd|j}nd}tj ||_ tj |j |j|_ |dS)Nclassifier_dropouthidden_dropoutg?)r$r%rrNr7rrrrDropoutrrr,rrY)r+rOrr.s r/r%z$LlamaForTokenClassification.__init__s     +'' 6/ 6 6 B!'!:   V-t 4 4 @!'!6  !$ z"455 Yv163DEE  r0c|jjSrrrBs r/r\z0LlamaForTokenClassification.get_input_embeddingsrr0c||j_dSrrr^s r/r`z0LlamaForTokenClassification.set_input_embeddingsrr0) checkpointrrCNrarrjr8rbrrrrcrdrc p| | n |jj} ||||||||| |  } | d} || } || } d}||| ||j}| s| f| ddz}||f|zn|St || | j| jS)rNrrr2)rrr;rj) rOrkr7rrrrr;rj)r+rarrjr8rbrrrrcrdr3rrrrs r/r>z#LlamaForTokenClassification.forwards2&1%rGrHs@r/rrs '''(((+*+ABB&)$151537=A59-1$(,0/3&*1 1 E,-1 !.1 u/0 1 "$u'8"9: 1   12 1 )*1 D>1 $D>1 'tn1 d^1  u++ ,1 1 1 CB 1 1 1 1 1 r0r)Nr)Krtypingrrrrr'torch.nn.functionalrrrtorch.utils.checkpoint activationsr cache_utilsr r r generationr modeling_attn_mask_utilsrmodeling_flash_attention_utilsrmodeling_outputsrrrrrmodeling_rope_utilsrmodeling_utilsr pytorch_utilsrutilsrrrrrrconfiguration_llamar get_loggerrDr[rrModuler!appendrJrrrrrrrrrr rr(r%LLAMA_START_DOCSTRINGr6rrNrrrrrr0r/rs!( //////////// !!!!!!;;;;;;;;;;))))))>>>>>>FFFFFF766666------111111-,,,,,  H % %0JJJJJ29JJJ(L)))T<T<T<T<T<29T<T<Tu9u9u9p`1`1`1`1`1`1`1`1H- MMMMM MMM`"Y???????? ?0HVYPPPPP%PP Pfy y y y y +_y y y x V V V V V %9V V V r M M M M M 4M M M ` N N N N N "6N N N N N r0