i{>ddlmZddlmZmZddlZddlmZddlmZ ddl m Z ddl m Z mZdd lmZdd lmZmZdd lmZdd lmZdd lmZddlmZmZddlmZmZddl m!Z!m"Z"ddl#m$Z$ddl%m&Z&m'Z'm(Z(m)Z)m*Z*ddl+m,Z,m-Z-ddl.m/Z/ddl0m1Z1ddl2m3Z3e*jhe5Z6GddedZ7GddejpZ9dZ:dejvd ed)e>d*e$e&fd+Z?dHd,Z@ee@Gd-d.ejpZAGd/d0ej jpZBd1ejvd2ed?eZJe'Gd@dAe"ZKe'GdBdCeKZLe'GdDdEeKeZMgdFZNy)I)Callable)Optional TypedDictN)nn)initialization)ACT2FN)Cache DynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernelized_func)lazy_load_kernel)create_causal_mask)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tupleis_torchdynamo_compilinglogging)maybe_autocastmerge_with_config_defaults)resolve_internal_import)capture_outputs) BambaConfigceZdZUdZej ed<ej ed<eed<eed<ejed<y)BambaFlashAttentionKwargsaU Keyword arguments for advanced Flash Attention, causal-conv1d, and mamba_ssm kernel usage. Use cases include padding-free training and fewer `torch.compile` graph breaks. cu_seq_lens_q (`torch.LongTensor`): Gets cumulative sequence length for query state. cu_seq_lens_k (`torch.LongTensor`): Gets cumulative sequence length for key state. max_length_q (`int`): Maximum sequence length for query state. max_length_k (`int`): Maximum sequence length for key state. seq_idx (`torch.IntTensor`): Index of each packed sequence. cu_seq_lens_q cu_seq_lens_k max_length_q max_length_kseq_idxN) __name__ __module__ __qualname____doc__torch LongTensor__annotations__int IntTensory/var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/transformers/models/bamba/modeling_bamba.pyr%r%6s7 ###### __r5r%F)totalc eZdZUejed<d deffd Ze d dedzde dde dzde d e ffd Z ejed ZxZS)BambaRotaryEmbeddinginv_freqNconfigct||j|_|j|_||_|j j d|_|j}|jdk7rt|j}||j |\}|_ |jd|d|jd|jdy)N rope_typedefaultr:F) persistentoriginal_inv_freq) super__init__max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr;rope_parametersr=compute_default_rope_parametersrattention_scalingregister_bufferclone)selfr;device rope_init_fnr: __class__s r6rBzBambaRotaryEmbedding.__init__Qs "("@"@$*$B$B! 44[A!%!E!E >>Y &.t~~>L+7 V+L($( ZeD 0(..2BuUr5rLz torch.deviceseq_lenreturnz torch.Tensorc|jd}t|ddxs|j|jz}d}d|t j d|dtj j|tj|z zz }||fS) a Computes the inverse frequencies according to the original RoPE implementation Args: config ([`~transformers.PreTrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. Unused for this type of RoPE. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). rope_thetahead_dimNg?rdtyperLrV) rFgetattr hidden_sizenum_attention_headsr/arangeint64tofloat)r;rLrObasedimattention_factorr:s r6rGz4BambaRotaryEmbedding.compute_default_rope_parametersas&%%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjj k )))r5cN|jddddfjj|jdddj |j }|dddddfj}t |j jtr/|j jdk7r|j jnd}t|d5|j|jzjdd}tj||fd }|j|jz}|j|jz} dddj |j  j |j fS#1swYAxYw) Nrr"mpscpuF) device_typeenabledrTr`rU)r:r^expandshaper]rL isinstancetypestrr transposer/catcosrHsinrV) rKx position_idsinv_freq_expandedposition_ids_expandedrffreqsembrprqs r6forwardzBambaRotaryEmbedding.forwardsR!MM$4-8>>@GG HZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E!((--[`J`ahhmmfk  U C 5&,,.1F1L1L1NNYYZ[]^_E))UEN3C'')d444C'')d444C  5 vvAGGv$cff177f&;;;  5 5s BFF$NNNN)r+r,r-r/Tensorr1r#rB staticmethodrr2tupler^rGno_gradrrx __classcell__rNs@r6r9r9NsllV{V %)+/"*d"*(*t* ~u$ % **:U]]_ <