qiY2ddlmZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z dd lmZmZdd lmZmZdd lmZmZdd lmZdd lmZmZmZddlmZmZm Z ddl!m"Z"ddl#m$Z$m%Z%ddl&m'Z'm(Z(er ddl)Z)ddl)m*Z*Gdde*jVZ,dZ-de)j\de/de)j\fdZ0 d8de*jVde)j\de)j\d e)j\d!e)j\dzd"e1d#e1d$eefd%Z2d9d&Z3e e3Gd'd(e*jVZ4Gd)d*e*jVZ5Gd+d,e Z6eGd-d.eZ7Gd/d0e7Z8Gd1d2e*jVZ9ed34Gd5d6e7e Z:gd7Z;y):)Callable)Optional)ACT2FN)Cache)GenerationMixin)use_kernelized_func)GradientCheckpointingLayer)BaseModelOutputWithPoolingCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringis_torch_available)can_return_tuplemaybe_autocastmerge_with_config_defaults)capture_outputs) AutoModelAutoModelForCausalLM) GlmAsrConfigGlmAsrEncoderConfigN)nnc eZdZUejed<d deffd Ze d dedzde dde dzde d e ffd Z ejed ZxZS)GlmAsrRotaryEmbeddinginv_freqNconfigct||j|_|j|_||_|j j d|_|j}|jdk7rt|j}||j |\}|_ |jd|d|jd|jdy)N rope_typedefaultr"F) persistentoriginal_inv_freq) super__init__max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr#rope_parametersr%compute_default_rope_parametersr attention_scalingregister_bufferclone)selfr#device rope_init_fnr" __class__s \/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/glmasr/modeling_glmasr.pyr*zGlmAsrRotaryEmbedding.__init__0s "("@"@$*$B$B! 44[A!%!E!E >>Y &.t~~>L+7 V+L($( ZeD 0(..2BuUr4z torch.deviceseq_lenreturnz torch.Tensorcn|jd}|jjdd}t|ddxs|j|jz}t ||z}d}d|t jd|dt jj|t j |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_thetapartial_rotary_factorg?head_dimNrrdtype)r4r@) r.getgetattr hidden_sizenum_attention_headsinttorcharangeint64tofloat) r#r4r9baser=r>dimattention_factorr"s r7r/z5GlmAsrRotaryEmbedding.compute_default_rope_parameters@s&%%l3 & 6 6 : :;RTW X6:t4h8J8JfNhNh8h(223 U\\!S!5;;?BB&X]XcXcBdgjj k )))r8cN|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) NrrmpscpuF) device_typeenabledrrLr?)r"rJexpandshaperIr4 isinstancetypestrr transposerFcatcosr0sinr@) r3x position_idsinv_freq_expandedposition_ids_expandedrRfreqsembr\r]s r7forwardzGlmAsrRotaryEmbedding.forward`sR!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$N)NNN)__name__ __module__ __qualname__rFTensor__annotations__rr* staticmethodrrEtuplerJr/no_gradrrd __classcell__r6s@r7r!r!-sllV|V &*+/"*t#*(*t* ~u$ % **>U]]_ <