% &h/DddlmZddlmZmZmZmZddlZddlZddlm Z ddl m Z m Z ddl mZddlmZmZdd lmZdd lmZmZd d lmZmZmZmZd dlmZej<eZ GddeZ!GddeZ"GddeZ#GddeeZ$GddeZ%y))partial)ListOptionalTupleUnionN)nn)Cache DynamicCache)FlashAttentionKwargs)BaseModelOutputWithPastCausalLMOutputWithPast)Unpack) LossKwargslogging)LlamaAttentionLlamaDecoderLayerLlamaForCausalLM LlamaModel) GraniteConfigc4eZdZdZddedeeffd ZxZS)GraniteAttentionz=Multi-headed attention from 'Attention Is All You Need' paperconfig layer_idxcHt||||j|_yN)super__init__attention_multiplierscalingselfrr __class__s /var/www/pru.catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/granite/modular_granite.pyr zGraniteAttention.__init__&s +22 r) __name__ __module__ __qualname____doc__rrintr __classcell__r%s@r&rr#s"G3}3#33r'rcfeZdZdedeffd Z ddejdeejdeejdee dee d ee d eejd ee ejejfd e ejee ejejfffd ZxZS)GraniteDecoderLayerrrclt||||j|_t|||_y)N)rr)rr residual_multiplierr self_attnr#s r&r zGraniteDecoderLayer.__init__,s. +#)#=#= )9Mr' hidden_statesattention_mask position_idspast_key_valueoutput_attentions use_cachecache_positionposition_embeddingsreturnc |} |j|}|jd||||||||d| \}} | ||jzz}|} |j|}|j |}| ||jzz}|f} |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 )r4r5r6r7r8r9r:r;)input_layernormr3r2post_attention_layernormmlp) r$r4r5r6r7r8r9r:r;kwargsresidualself_attn_weightsoutputss r&forwardzGraniteDecoderLayer.forward1sD!,,]; ,:4>> , ')%)/) 3 ,  , ( (!=43K3K#KK !55mD /  =43K3K#KK  "  )+ +Gr')NNNFFNN)r(r)r*rr,r torchTensorr LongTensorr boolr FloatTensorrFr-r.s@r&r0r0+sN}NN2637*.,1$)59KO?||?!.?u//0 ? ! ? $D> ?D>?!!1!12?&eELL%,,,F&GH? u  (51B1BEDUDU1U+V"WW X?r'r0c eZdZdeffd Z ddeejdeejdeejdee deejdee d ee d ee d eejd e e d efdZxZS) GraniteModelrc t|||j|_tjt |j Dcgc]}t||c}|_ycc}wr) rr embedding_multiplierr ModuleListrangenum_hidden_layersr0layersr#s r&r zGraniteModel.__init__tsR  $*$?$?!mmEJ6KcKcEd e  3 e  esA( input_idsr5r6past_key_values inputs_embedsr9r8output_hidden_statesr:flash_attn_kwargsr<c 0||n|jj}||n|jj}||n|jj}|du|duz r t d|j r%|j r|rtjdd}||j|}||jz}|r | t}| F||jnd} tj| | |jdz|j } || j#d}|j%||| ||} |} |j'| |}|rdnd}|rdnd}|j(d|jj*D]r}|r|| fz }|j r:|j r.|j-t/|j0fi| | | ||||| | }n|| f| ||||| |d| }|d} |sj||dfz }t|j3| } |r|| fz }t5| |r|nd|| S) Nz:You must specify exactly one of input_ids or inputs_embedszX`use_cache=True` is incompatible with gradient checkpointing. 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