gFdZddlZddlmZmZmZmZddlZddlZddlm Z ddl m Z ddl m Z ddlmZmZmZdd lmZdd lmZdd lmZmZmZmZdd lmZdd lmZddlm Z m!Z!m"Z"m#Z#m$Z$ddl%m&Z&e#j'e(Z)dZ*dZ+Gdde j,Z-Gdde-Z.Gdde-Z/dZ0d0dZ1Gdde j,Z2Gdde j,Z3Gdd e j,Z4d!Z5e!d"e5Gd#d$eZ6d%Z7e!d"e5Gd&d'e6Z8Gd(d)e6eZ9e!d*e5Gd+d,e6Z:e!d-e5Gd.d/e6Z;dS)1zPyTorch Persimmon model.N)ListOptionalTupleUnion)nn)CrossEntropyLoss)ACT2FN)Cache DynamicCache StaticCache)GenerationMixin)AttentionMaskConverter)BaseModelOutputWithPastCausalLMOutputWithPast SequenceClassifierOutputWithPastTokenClassifierOutput)ROPE_INIT_FUNCTIONS)PreTrainedModel)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings)PersimmonConfigzadept/persimmon-8b-basercveZdZ d deeffd ZdZejd Z xZ S) PersimmonRotaryEmbeddingN'?defaultconfigcXti|_|=td|||||d|_||_||_||_n_|j9|j d|j d|_nd|_|j |_|j |_||_ t|j|_ |j |j |fi|j\}|_|d|d|j|_dS) Nz`PersimmonRotaryEmbedding` 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_embeddingsr%typer"inv_freqF persistent)super__init__ rope_kwargslogger warning_oncer%max_seq_len_cachedoriginal_max_seq_len rope_scalinggetr)r#r rope_init_fnattention_scalingregister_bufferr+original_inv_freq) selfr'r)r(devicescaling_factorr%r#r+ __class__s l/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/persimmon/modeling_persimmon.pyr/z!PersimmonRotaryEmbedding.__init__<sC  >   R    '(+B   D 'DN&=D #(?D % %".!'!4!8!8fFYF]F]^dFeFe!f!f!*&,&DD #(.(FD % /?+<4+>@@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)Nrr Nr!r"N) __name__ __module__ __qualname__rrr/rFrCno_gradrb __classcell__r>s@r?rr;s  $ ,0*/*/)*/*/*/*/*/*/X@@@$U]__<<_<<<<s r?r/z.PersimmonLinearScalingRotaryEmbedding.__init__sO y   '{$)&)))))r@rcrdre__doc__r/rgrhs@r?rjrjs>ii*********r@rjc"eZdZdZfdZxZS))PersimmonDynamicNTKScalingRotaryEmbeddingzqPersimmonRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozillacxtdd|d<tj|i|dS)Nz`PersimmonDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use `PersimmonRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to __init__).rHr%rmrns r?r/z2PersimmonDynamicNTKScalingRotaryEmbedding.__init__sN    ({$)&)))))r@rqrhs@r?rtrts>{{*********r@rtc|dd|jddzf}|d|jddzdf}tj| |fdS)z*Rotates half the hidden dims of the input..NrJrOrP)rTrCrY)r]x1x2s r? rotate_halfrys] 3"!'"+"" " #B 3 q "" " #B 9rc2YB ' ' ''r@c||}||}||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. ) unsqueezery)qkrZr[rE unsqueeze_dimq_embedk_embeds r?apply_rotary_pos_embrsc( -- & &C -- & &C3w;q>>C/0G3w;q>>C/0G G r@c$eZdZfdZdZxZS) PersimmonMLPcttj|j|j|_tj|j|j|_t|j |_ dSN) r.r/rLinear hidden_sizeintermediate_size dense_h_to_4h dense_4h_to_hr hidden_actactr;r#r>s r?r/zPersimmonMLP.__init__s` Yv'96;STTYv'?ASTT&+,r@c||}||}||}|Sr)rrr)r; hidden_statess r?rbzPersimmonMLP.forwards?**=99 // **=99 r@)rcrdrer/rbrgrhs@r?rrsG----- r@rceZdZdZddedeeffd Zdej de ej ej ej ffdZ dd ej d eej d eej d ee dededeej dee ej ej fde ej eej ee ej ffdZxZS)PersimmonAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNr# layer_idxct||_||_|(td|jjd|j|_|j |_ |j|j z|_ |j |_ t|j |jz|_d|_|j |j z|jkr t#d|jd|j dt%j|jd|jzd|_t%j|j |j z|jd|_|j|_|jr\t%j|j|j z|jd |_t%j|j|j z|jd |_t%j|j|_t;|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.Tz?hidden_size must be divisible by num_heads (got `hidden_size`: z and `num_heads`: z).r bias)epselementwise_affiner#)r.r/r#rr1r2r>rcrnum_attention_heads num_headshead_dim rope_thetaintpartial_rotary_factor rotary_ndims is_causal ValueErrorrrquery_key_valuedense qk_layernorm LayerNormlayer_norm_eps q_layernorm k_layernormDropoutattention_dropoutr rotary_embr;r#rr>s r?r/zPersimmonAttention.__init__s  "     ,!8,,,    "-3(DN:  + 0L LMM MDN *t/? ? ?8RVRb88%)^888  "y)91t?O;OVZ[[[Yt~ =t?OVZ[[[ "/   !|"dn4&:Odh   D  "|"dn4&:Odh   D "$F,D!E!E2$+FFFr@ fused_qkvreturnc|j\}}}||||jd|j}|ddddf|ddddf|ddddffS)a Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory storage as `fused_qkv` Args: fused_qkv (`torch.tensor`): [batch_size, seq_length, num_heads * 3 * head_dim] Returns: query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim] value: [batch_size, seq_length, num_heads, head_dim] r .rNrrO)rTviewrr)r;r batch_size seq_lengththree_times_hidden_sizes r? _split_headszPersimmonAttention._split_headssk;D/7 J 7NN:z4>1dm\\ a#YsAqqqy%99S!QQQY;OOOr@Frattention_maskrEpast_key_valueoutput_attentions use_cachecache_positionposition_embeddingsc |\} } } ||} || \} }}|jr*|| } ||}| dd} |dd}|dd}|4td| ||\}}n|\}}| dd|j f| d|j df}}|dd|j f|d|j df}}t||||\}}tj ||fd} tj ||fd}|,|||j |d}||||j|\}}tj| |ddt#j|jz }|$|ddddddd|jd f}||z}t*j|tjd | j}||}tj||}|| |j| |jfkr5t;d | |j| |jfd ||dd}|| | |j }|!|}|sd}|||fS) NrrOaYThe 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..rJrP)r[rZpartial_rotation_sizerr )rQr'z `attn_output` should be of size z , but is )"sizerrrrrrXr1r2rrrrCrYupdatermatmulmathsqrtrrTr functionalsoftmaxfloat32r\rQrrr contiguousreshaperr)r;rrrErrrrrbszq_len_r query_states key_states value_statesrZr[ query_rot query_passkey_rotkey_pass cache_kwargs attn_weights causal_mask attn_outputs r?rbzPersimmonAttention.forwards&**,, UA((77 483D3DY3O3O0z<   6++L99L))*55J$--a33 #--a33 ))!Q//  &   G    |\BBHC*HC 1 111 2 d/111 2 s/d/// 0 sD-/// 0 2)Wc3OO 7y)Z!8bAAA Y2;;;  %)-):"0 L (6'<'>     #t~udm!L L L)CPTP]3^))$$&&))  "++Aq11<<>> !))#ud6FGG jj--   LL.88r@rNNNFFNN)rcrdrerrrrrr/rCTensorrr LongTensorr boolrbrgrhs@r?rrsGG#G#G#G8C=#G#G#G#G#G#GJPelPuU\5Q5RR S[9[9[9[9[9[9[9[9r@rcdeZdZdedeffd Z ddejdeejdeej d ee ejd ee d ee d eej d ee ejejfde ej ee ej ej fffdZ xZS)PersimmonDecoderLayerr#rct|j|_t|||_t ||_tj|j|j |_ tj|j|j |_ tj |j |_dS)N)r#rr)r.r/rr self_attnrmlprrrinput_layernormpost_attention_layernormrhidden_dropoutdropoutrs r?r/zPersimmonDecoderLayer.__init__ws !-+6YOOO''!|F,>FDYZZZ(* V5GVMb(c(c(c%z&"788 r@NFrrrErrrrrrc 2|} ||}|||||||||\}} } | |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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. position_ids (`torch.LongTensor` of shape `({0})`, *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_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states 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`). 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. )rrrErrrrr)rrrrr) r;rrrErrrrrresidualself_attn_weightspresent_key_valueoutputss r?rbzPersimmonDecoderLayer.forwardsF!,,];; ?Cnn')%)/) 3?M ? ? ; (*;!=0 !55mDD //  ]33 %0  "  , )+ +G  , )+ +Gr@r)rcrdrerrr/rCrrrrr FloatTensorrbrgrhs@r?rrvsG99399999926378<,1$)59KODD|D!.Du/0 D !u|!45 D $D> DD>D!!12D&eEL%,,F&GHD u (51BEDU1U+V"WW XDDDDDDDDr@raN 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 ([`PersimmonConfig`]): 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. zWThe bare Persimmon Model outputting raw hidden-states without any specific head on top.c6eZdZeZdZdZdgZdZdZ dZ dZ dZ dS)PersimmonPreTrainedModelmodelTrpast_key_valuesc|jj}t|tjrJ|jjd||j |jj dSdSt|tj rS|jjd||j -|jj|j  dSdSdS)N)meanstd) r#initializer_rangerUrrweightdatanormal_rzero_ Embedding padding_idx)r;modulers r? _init_weightsz&PersimmonPreTrainedModel._init_weightssk+ fbi ( ( ? M  & &CS & 9 9 9{&  &&((((('&  - - ? M  & &CS & 9 9 9!- "6#56<<>>>>> ? ?--r@N) rcrdrer config_classbase_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_cache_class_supports_quantized_cache_supports_static_cacherr@r?rrsW #L&*#01"3  $! ? ? ? ? ?r@ra 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 `decoder_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. ceZdZdZdeffd ZdZdZee  dde j de e j d e e j d e ee jd 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)PersimmonModelz Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PersimmonDecoderLayer`] Args: config: PersimmonConfig r#ctj|_j|_t jjj|j|_t j fdtj D|_ t j jj|_t!|_d|_|dS)Nc0g|]}t|Sr)r).0rr#s r? z+PersimmonModel.__init__..Ps$ g g g) "69 5 5 g g gr@rrF)r.r/ pad_token_idr vocab_sizerrr embed_tokens ModuleListrangenum_hidden_layerslayersrrfinal_layernormrrgradient_checkpointing post_initrs `r?r/zPersimmonModel.__init__Is    !. +L):FFDYZZZ2&AAA&+# r@c|jSrr r;s r?get_input_embeddingsz#PersimmonModel.get_input_embeddingsZs   r@c||_dSrrr;values r?set_input_embeddingsz#PersimmonModel.set_input_embeddings]s!r@N input_idsrrEr inputs_embedsrroutput_hidden_states return_dictrrc ||n |jj}||n |jj}||n |jj}| | n |jj} |du|duz rt d|jr%|jr|rt dd}d} |rVt|tsAd} |t}n.tj |}t d|||}| B||nd} t!j| | |jdz|j} || d}|||| ||} |}|||}|rd nd}|rd nd}d}|jD]r}|r||fz }|jr*|jr#||j|| ||||| | }n||| ||||| | }|d}|r ||rd nd}|r ||dfz }s||}|r||fz }|r|nd}| r|}| st9d ||||fDSt;|||| S)Nz:You must specify exactly one of input_ids or inputs_embedszZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...FTzWe detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class (https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)rrrIr)rrErrrrrrOc3K|]}||V dSrr)rvs r? z)PersimmonModel.forward..s(ttqfgfsfsfsfsfsttr@)last_hidden_staterr attentions)r#rrruse_return_dictrrtrainingr1r2rUr r from_legacy_cacher get_seq_lengthrCarangerTr<r{_update_causal_maskrr_gradient_checkpointing_func__call__rto_legacy_cachetupler)r;rrrErrrrrrrreturn_legacy_cachepast_seen_tokensrrrall_hidden_statesall_self_attnsnext_decoder_cache decoder_layer layer_outputs next_caches r?rbzPersimmonModel.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,,];;   2 -!1 1 +4>''$  6#3355J utt]J@QSa$bttttt t&+&+%     r@ input_tensorc |jjdkr |d|vr|SdS||nd}t|t}|jjdkr#|s!|st j||||jrdS|j|j } }|j d} |r| } n/t|tj r |j dn|| zdz} ||| | || ||j d} |jjdkrB|@|j jd kr0|s.tj|j} t j| | } | S) Nflash_attention_2rrsdpa)rpast_key_values_length is_trainingrrJ)sequence_length target_lengthrQr<rrcuda)r#_attn_implementationr)rUr r_ignore_causal_mask_sdpar'rQr<rTget_max_cache_shaperCr5_prepare_4d_causal_attention_mask_with_cache_positionr*finfomin_unmask_unattended)r;rr8rrrr1using_static_cacherQr<r>r?r min_dtypes r?r+z"PersimmonModel._update_causal_masks ; +/B B B)c^.C.C%%4 @O?Z?99;;;`a'EE ; +v 5 5>P 5Yj 5%>*'7 M   t$*L,?v&,Q/  +??AAMMnel;;<$R((%7!; PP +')#)!,Q   K , 6 6*%*f44%5  E**.I0CKQZ[[Kr@r>r?rQr<rc ||dkr|}n+tj|j} tj||f| ||}|dkrtj|d}|tj|||ddkz}|ddddddf|ddd}|| }|j d} |ddddddd| f|ddddddfz} | dk} |ddddddd| f | | |ddddddd| f<|S) a Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to plcae the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. N) fill_valuerQr<r)diagonalrIrJr) r'rCrErFfulltriur*rrSclonerT masked_fill) rr>r?rQr<rrrprrI mask_length padding_masks r?rDzDPersimmonModel._prepare_4d_causal_attention_mask_with_cache_position sD  %.*<*<*>*>!*C*C(KK E**.I* -0Ye\bK!###jqAAA 5< fEEEH^H^_acdHeHee eK%dD!!!QQQ&67>>z1bRTUUK))//11 ,226 *111aaaL[L+@ANSTSTSTVZ\`bcbcbcScDdd +q0 5@AAAqqq,;,AV5W5c5c )66 AAAqqq!!!\k\12r@ NNNNNNNNNN)rcrdrerrrr/rrrPERSIMMON_INPUTS_DOCSTRINGrCrrrrrrrrrrbr r+ staticmethodrrQr<rDrgrhs@r?rr=s; "!!!"""+*+EFF'+1537=A59$(,0/3&*59{ { #{ !.{ u/0 { "$u'8"9: {   12 { D>{ $D>{ 'tn{ d^{ !!12{  u-- .{ { { GF{ |? ?l? ?  ?  ????B5 555{ 5  5  5555\55555r@rceZdZdgZfdZdZdZdZdZdZ dZ e e e ee  dd ejd eejdeejdeeejdeejdeejdeedeedeedeedeejdedeeeffdZxZS)PersimmonForCausalLMzlm_head.weightct|t||_|j|_t j|j|jd|_| dSNFr) r.r/rrr rrrlm_headrrs r?r/zPersimmonForCausalLM.__init__^sj    #F++  +y!3V5FUSSS  r@c|jjSrrr rs r?rz)PersimmonForCausalLM.get_input_embeddingsh z&&r@c||j_dSrr]rs r?rz)PersimmonForCausalLM.set_input_embeddingsl"' r@c|jSrr[rs r?get_output_embeddingsz*PersimmonForCausalLM.get_output_embeddingsps |r@c||_dSrrb)r;new_embeddingss r?set_output_embeddingsz*PersimmonForCausalLM.set_output_embeddingsts % r@c||_dSrr)r;decoders r? set_decoderz PersimmonForCausalLM.set_decoderxs  r@c|jSrrhrs r? get_decoderz PersimmonForCausalLM.get_decoder|s zr@) output_typerNrrrrErrlabelsrrrrrnum_logits_to_keeprc ||n |jj}| | n |jj} | | n |jj} ||||||||| | |  } | d}||dd| dddf}d}||dddddf}|dddf}t}|d|jj }|d}| |j }|||}| s|f| ddz}||f|zn|St||| j | j| jS)u2 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, PersimmonForCausalLM >>> model = PersimmonForCausalLM.from_pretrained("adept/persimmon-8b-base") >>> tokenizer = AutoTokenizer.from_pretrained("adept/persimmon-8b-base") >>> prompt = "human: Hey, what should I eat for dinner?" >>> 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] 'human: Hey, what should I eat for dinner?\n\ncat: 🐱\n\nhuman: 😐\n\n' ```N) rrrErrrrrrrr.rJrlosslogitsrrr%)r#rrr&rr[rrrr r\r<rrrr%)r;rrrErrrnrrrrrrorrrsrr shift_logits shift_labelsloss_fctoutputs r?rbzPersimmonForCausalLM.forwards`2C1N--TXT_Tq$8$D $+Jj &1%??L8L,77D DY,F'+'7D7V##V C%#3!/)     r@) NNNNNNNNNNNr)rcrdre_tied_weights_keysr/rrrcrfrjrlrrUrr_CONFIG_FOR_DOCrCrrrrrrrrrrbrgrhs@r?rXrXZs*+'''(((&&&+*+EFF+AP_```'+1537=A59-1$(,0/3&*59"#] ] #] !.] u/0 ] "$u'8"9: ]   12 ] )*] D>] $D>] 'tn] d^] !!12]  ]  u,, -] ] ] a`GF] ] ] ] ] r@rXa The Persimmon transformer with a sequence classification head on top (linear layer). [`PersimmonForSequenceClassification`] 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)"PersimmonForSequenceClassificationct||j|_t||_t j|j|jd|_| dSrZ) r.r/ num_labelsrrrrrscorerrs r?r/z+PersimmonForSequenceClassification.__init__si     +#F++ Yv14?OOO  r@c|jjSrr]rs r?rz7PersimmonForSequenceClassification.get_input_embeddingsr^r@c||j_dSrr]rs r?rz7PersimmonForSequenceClassification.set_input_embeddingsr`r@NrrrErrrnrrrrrc "| | 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). NrrErrrrrrrrz=Cannot handle batch sizes > 1 if no padding token is defined.rJrI)rsrn pooled_logitsr#rq)r#r&rr~rTr rrCeqrargmaxr\r<r* loss_functionrrrr%)r;rrrErrrnrrrrtransformer_outputsrrsrsequence_lengthsrrrrws r?rbz*PersimmonForSequenceClassification.forwards(&1%E $D>E 'tnE d^E  u66 7E E E GFE E E E E r@r{z The Persimmon Model transformer with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. ceZdZfdZdZdZeeee e e  dde e jde e jde e jd e ee jd e e jd e e jd e ed e ede ede edeee ffdZxZS)PersimmonForTokenClassificationct||j|_t||_t |dd|j}nt |dd|j}nd}tj ||_ tj |j |j|_ |dS)Nclassifier_dropoutrg?)r.r/r}rrgetattrrrrrrrrr~r)r;r#rr>s r?r/z(PersimmonForTokenClassification.__init__Ss     +#F++ 6/ 6 6 B!'!:   V-t 4 4 @!'!6  !$ z"455 Yv163DEE  r@c|jjSrr]rs r?rz4PersimmonForTokenClassification.get_input_embeddingscr^r@c||j_dSrr]rs r?rz4PersimmonForTokenClassification.set_input_embeddingsfr`r@) checkpointrmrNrrrErrrnrrrrrc p| | n |jj} ||||||||| |  } | d} || } || } d}||| ||j}| s| f| ddz}||f|zn|St || | j| jS)rNrrrO)rrrsrr%) r#r&rrr~rrrr%)r;rrrErrrnrrrrrsequence_outputrsrrrws r?rbz'PersimmonForTokenClassification.forwardis2&1%1 $D>1 'tn1 d^1  u++ ,1 1 1 GF 1 1 1 1 1 r@r)Nr)rs( //////////// %%%%%%!!!!!!;;;;;;;;;;))))))>>>>>> 766666------544444  H % %/#T<T<T<T<TN N N N N r@