i_ddlmZddlmZddlmZddlZddlmZddl m Z ddl m Z ddlmZdd lmZmZdd lmZdd lmZmZmZdd lmZdd lmZddlmZmZddl m!Z!m"Z"ddl#m$Z$m%Z%ddl&m'Z'ddl(m)Z)m*Z*m+Z+m,Z,m-Z-ddl.m/Z/ddl0m1Z1ddl2m3Z3ddl4m5Z5m6Z6ddl7m8Z8e-jre:Z;ee+dGdde)Z<edGdd ejzZ>Gd!d"ejzZ?Gd#d$ejzZ@d%ZAed&dKd'ZBd(ejd)eDd*ejfd+ZE dLd,ejzd-ejd.ejd/ejd0ejdzd1eFd2eFd3e'e*fd4ZGeeBGd5d6ejzZHGd7d8eZIe+d9e+Gd:d;e%ZJe+Gd<d=eJZKGd>d?ejzZLe+d@GdAdBeJeZMGdCdDejzZNe+GdEdFeJZOe+dGGdHdIeJe8ZPgdJZQy)M)Callable) dataclass)OptionalN)check_model_inputs)initialization)ACT2FN)Cache DynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hubuse_kernelized_func)create_causal_mask)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack) ModelOutputTransformersKwargsauto_docstringcan_return_tuplelogging)maybe_autocast)is_torchdynamo_compiling) AutoModel) CsmConfigCsmDepthDecoderConfig)CsmGenerationMixinz: Base class for the model autoregressive outputs. ) custom_introceZdZUdZdZej dzed<dZej dzed<dZ e dzed<dZ e ej dfdzed<dZ e ej dfdzed<dZej dzed <dZej dzed <dZe dzed <dZe ej dfdzed <dZe ej dfdzed <dZej dzed<y)CsmOutputWithPasta loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. depth_decoder_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction) of the depth decoder model. depth_decoder_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the depth decoder (scores for each vocabulary token before SoftMax). depth_decoder_past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). depth_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. depth_decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. backbone_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction) of the backbone model. Nlosslogitspast_key_values. hidden_states attentionsdepth_decoder_lossdepth_decoder_logitsdepth_decoder_past_key_valuesdepth_decoder_hidden_statesdepth_decoder_attentions backbone_loss)__name__ __module__ __qualname____doc__r)torch FloatTensor__annotations__r*r+r r,tupler-r.r/r0r1r2r3n/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/transformers/models/csm/modeling_csm.pyr(r(4s8&*D%  d ")'+FE   $+$(OUT\(:>M5**C/047>7;Je'',-4;37))D0759%++d2926!54<6HLu'8'8#'=!>!ELEIeE$5$5s$:;dBI.2M5$$t+2r=r(RMSNormc,eZdZdfd ZdZdZxZS) CsmRMSNormct|tjt j ||_||_y)z9 CsmRMSNorm is equivalent to T5LayerNorm N)super__init__nn Parameterr8onesweightvariance_epsilon)self hidden_sizeeps __class__s r>rDzCsmRMSNorm.__init__fs1 ll5::k#:; #r=c"|j}|jtj}|j dj dd}|tj ||jzz}|j|j|zS)Nr T)keepdim) dtypetor8float32powmeanrsqrtrIrH)rJr, input_dtypevariances r>forwardzCsmRMSNorm.forwardnsy#)) %((7  $$Q',,R,>% Ht?T?T4T(UU {{]--k:::r=c^t|jjd|jS)Nz, eps=)r;rHshaperIrJs r> extra_reprzCsmRMSNorm.extra_reprus* ))*+6$2G2G1HIIr=)gư>)r4r5r6rDrYr] __classcell__rMs@r>rArAds$;Jr=rAc eZdZUejed<d deffd Ze d dedzde dde dzde d e ffd Z ejed ZxZS)CsmRotaryEmbeddinginv_freqNconfigct||j|_|j|_||_|j j d|_|j}|jdk7rt|j}||j |\}|_ |jd|d|jd|jdy)N rope_typedefaultrbF persistentoriginal_inv_freq) rCrDmax_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrcrope_parametersrecompute_default_rope_parametersrattention_scalingregister_bufferclone)rJrcdevice rope_init_fnrbrMs r>rDzCsmRotaryEmbedding.__init__|s "("@"@$*$B$B! 44[A!%!E!E >>Y &.t~~>L+7 V+L($( ZeD 0(..2BuUr=rrz 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?rr rQrrrQ) rmgetattrrKnum_attention_headsr8arangeint64rRfloat)rcrrrtbasedimattention_factorrbs r>rnz2CsmRotaryEmbedding.compute_default_rope_parameterss&%%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjj k )))r=cN|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) NrrOr"mpscpuF) device_typeenabledr rry)rbrexpandr[rRrr isinstancetypestrr transposer8catcosrosinrQ) rJx position_idsinv_freq_expandedposition_ids_expandedrfreqsembrrs r>rYzCsmRotaryEmbedding.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$N)NNN)r4r5r6r8Tensorr:r#rD staticmethodrintr;rrnno_gradrrYr^r_s@r>raraysllVyV #'+/"*D *(*t* ~u$ % **:U]]_ < rDzCsmMLP.__init__s  !--!'!9!94#3#3T5K5KRXRaRabyy!1!143I3IPVP_P_` 4#9#94;K;KRXRaRabV../ r=c|j|j|j||j|z}|Sr)rrrr)rJrrs r>rYzCsmMLP.forwards6NN4;;t~~a/@#ADLLQRO#ST r=r4r5r6rDrYr^r_s@r>rrs 0r=rc|dd|jddzf}|d|jddzdf}tj| |fdS)z*Rotates half the hidden dims of the input..NrOr r)r[r8r)rx1x2s r> rotate_halfrsZ 3"!''"+"" " #B 3 q " " #B 99rc2YB ''r=rotary_pos_embc|j|}|j|}||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. 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)qkrr unsqueeze_dimq_embedk_embeds r>apply_rotary_pos_embrsY& -- &C -- &C3w;q>C/0G3w;q>C/0G G r=r,n_repruc|j\}}}}|dk(r|S|dddddddddfj|||||}|j|||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) r"N)r[rreshape)r,rbatchnum_key_value_headsslenrxs r> repeat_kvrso 2?1D1D.E h z!!Qa"23::5BUW\^bdlmM  (;e(CT8 TTr=modulequerykeyvalueattention_maskscalingdropoutkwargsc Tt||j}t||j} tj||j dd|z} |#|ddddddd|j df} | | z} t jj| dtjj|j} t jj| ||j} tj| | } | j ddj} | | fS)Nr rrO)rrQ)ptrainingr")rnum_key_value_groupsr8matmulrr[rE functionalsoftmaxrSrRrQrr contiguous) rrrrrrrr key_states value_states attn_weights causal_mask attn_outputs r>eager_attention_forwardrs3 ; ;rDzCsmAttention.__init__sM  " F4F4F&JdJd4de $*$>$>&B\B\$\!}}d* !'!9!9ii    : :T]] JQWQfQf  ii    : :T]] JQWQfQf  ii    : :T]] JQWQfQf  ii  & & 68J8JQWQfQf  r=Nr,position_embeddingsrr+cache_positionrruc |jdd}g|d|j}|j|j|j dd} |j |j|j dd} |j |j|j dd} |\} } t| | | | \} } |'| | |d}|j| | |j|\} } tj|jjt}||| | | |f|jsdn |j |j"d|\}}|j$g|dj'}|j)|}||fS)NrOr"r )rrr)rr)r[rxrviewrrrrupdaterr get_interfacerc_attn_implementationrrrrrrr)rJr,rrr+rr input_shape hidden_shape query_statesrrrr cache_kwargsattention_interfacerrs r>rYzCsmAttention.forward,s$))#2. 88b8$--8 {{=166|DNNqRST [[/44\BLLQPQR {{=166|DNNqRST &S#7 jRUWZ#[ j  &#&snUL'6'='=j,X\XfXfht'u $J (?(M(M KK , ,.E) %8      % $}}C$2H2HLL %  % ! \*k));;;;FFH kk+. L((r=NNNN)r4r5r6r7r#rrDr8rr;r LongTensorrrrYr^r_s@r>rrsG y S 4IM.2(,26 ))||))#5<<#=>E)) t+ ))  )) ((4/ ))+,)) u||U\\) *))r=rc"eZdZdedeffd Z ddejdejdzdejdzde dzd e dzd ejdzd e ejejfdzd e e d ejfdZxZS)CsmDecoderLayerrcrc t||j|_t|||_t ||_t|j|j|_ t|j|j|_ y)N)rcrrL) rCrDrKr self_attnrmlprA rms_norm_epsinput_layernormpost_attention_layernormrs r>rDzCsmDecoderLayer.__init__Ysk !--%VyI&>)&*<*<&BUBUV(263E3E6K^K^(_%r=Nr,rrr+ use_cacherrrruc |} |j|}|jd|||||||d|\}} | |z}|} |j|}|j|}| |z}|S)N)r,rrr+rrrr<)rrrr) rJr,rrr+rrrrresidual_s r>rYzCsmDecoderLayer.forwardcs!,,]; )4>>  ')%+) 3     q!=0 !55mD /  =0 r=)NNNFNN)r4r5r6r#rrDr8rrr boolr;rrrYr^r_s@r>rrXs`y`S`/304(,!&26HL|| t+&&-    $; ((4/#5<<#=>E+, r=rz[ The bare Csm Model outputting raw hidden-states without any specific head on top. ceZdZUeed<dZdZdZdgZdgZ dZ dZ dZ dZ eedZej$fdZxZS) CsmPreTrainedModelrcmodel)audiotextTrr+)r,r-ct||t|trV|j}t |dz D]8}t j|jd|jj:yt|tr_t j|jtj|jj|jj zyy)Nr"r)rUstd)rC _init_weightsrCsmCodebooksHead num_codebooksrangeinitnormal_rHrcinitializer_rangeCsmBackboneModelEmbeddingscopy_audio_tokens_offsetsr8r} vocab_size)rJrr irMs r>rz CsmPreTrainedModel._init_weightss f% f. /"00M=1,- Y V]]$++:W:WX Y  : ; JJv22ELLAZAZ4[^b^i^i^t^t4t urrst (&*#*+#4"5N""&(" U]]_vvr=rc4eZdZUeed<fdZee ddejdzdejdzdejdzdejdzde dzd ejdzd e dzd ejdzd eed eezfdZxZS)CsmDepthDecoderModelrcc rt|||j|_|j|_t j |j|jz|j|_ t jt|jDcgc]}t||c}|_t|j |j"|_t'||_d|_t j,|j|j d|_|j1ycc}w)NrrcFr)rCrD pad_token_id padding_idxrrE Embeddingr backbone_hidden_size embed_tokens ModuleListr num_hidden_layersrlayersrArKrnormra rotary_embgradient_checkpointingrinputs_embeds_projector post_initrs r>rDzCsmDepthDecoderModel.__init__s  !.. ++LL&*>*>ARAR*RU[UpUpqmmAFvG_G_A` aI_VY / a  v11v7J7JK ,F;&+#')yy1L1LfN`N`gl'm$  bsD4N input_idsbackbone_last_hidden_staterrr+ inputs_embedsrrrruc |!tstjdd}|du|duz r td|r|t |j }|i||j nd} ||jdn|jd} | |jn |j} tj| | | z| }|rtj|dz d} | |jz}|j||z}|ddk(}| ||dddf<n!ts|rtjd |j|}t!|j ||||| }|}|j#d}|j%|| }|j&d|j j(D]}||f||||||d | }|j+|}t-||r| Sd S)aJ backbone_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, backbone_hidden_size)`, *optional*): The last hidden state of the backbone model. Such input is required when the first codebook token (the one generated by the backbone model) is provided in the `input_ids` argument. NzCustom `position_ids` were provided but will be ignored. CSM depth decoder automatically determines position_ids from `cache_position` and as it requires them to be identical across the batch, the provided position_ids will be ignored.z;You must specify exactly one of input_ids or inputs_embeds.r rr"rr)minzvWhen the first codebook token is provided, `backbone_last_hidden_state` should also be provided for correct inference.rc input_embedsrrr+rr)rrr+rrrlast_hidden_stater+)rlogger warning_once ValueErrorr rcget_seq_lengthr[rrr8r}clamprr%warningr,rrr*r(r'r)r)rJr.r/rrr+r0rrrpast_seen_tokensinputs_seq_lengthrr codebook_idxsoffsetinput_ids_are_first_codebookrr,r decoder_layers r>rYzCsmDepthDecoderModel.forwards4&  #,D,F   M  L  -t"; <Z[ [ 0*$++>O  !CRC^==?de :G:S 3 3A 6YbYhYhijYk -:-F]))IL\L\F"\\*:B  r=)NNNNNNNN)r4r5r6r$r:rDrrr8rr9rr rrrr;rrYr^r_s@r>rrs !! .2?C.204(,26!%26R ##d*R %*$5$5$<R  t+ R &&- R  R ((4/R $;R ((4/R +,R  ( (R R r=rc&eZdZfdZddZxZS)rct|||_tjt j |jdz |||_y)Nr")rCrDr rErFr8emptyrH)rJrKr rrMs r>rDzCsmCodebooksHead.__init__s? *ll5;;t/A/AA/E{T^#_` r=c |2|jd}|jtj|}n|dz }|j|}t |jdDcgc]9}t j j|dd|ddf||j;}}tj|d}|Scc}w)Nr"rr) r[rHr8r}r rErlinearTstack)rJr,r seq_lengthcodebook_weightrA codebook_idxs r>rYzCsmCodebooksHead.forwards  !&,,Q/J"kk%,,z*BCO*Q.M"kk-8O!&o&;&;A&> ?  MM q,/A!BOT`DaDcDc d   Mq9   s#>B<rrr_s@r>rrsa r=ra$ The CsmDepthDecoder Model transformer, with a [`CsmCodebooksHead`] on top, which can be seen a position-specific language modeling head, allowing to use a different linear layer for each codebook (e.g. position 0 is the first codebook and uses the first codebook head, etc.) ceZdZdZdZdZfdZee dde jdzde jdzde jdzde jdzde dzde jdzd e jdzd edzd e jdzd ee jzd eedeezfdZ dde jde dzde jdzde jdzd e jdzf fd ZxZS)CsmDepthDecoderForCausalLMNct||t||_|j|_t |j |j|j|_|jyr) rCrDrrrrrKr codebooks_headr-rs r>rDz#CsmDepthDecoderForCausalLM.__init__7sY  )&1  ++.v/A/A6CWCWY_YjYjk r=r.r/rrr+r0labelsrrlogits_to_keeprruc |jd|||||||| d| } | d} t| tr | dk(r tdd}nt| d}n| }|j | dd|ddf| | |nd}|j }d}|B|dddfj }|j d|d|jj|d| }t||| j| j| jS) a backbone_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, backbone_hidden_size)`, *optional*): The last hidden state of the backbone model. Such input is required when the first codebook token (the one generated by the backbone model) is provided in the `input_ids` argument. 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]`. )r.r/rrr+r0rrrr"N.)r*rSr shift_labels)r)r*r+r,r-r<) rrrslicerRr loss_functionrcrrr+r,r-)rJr.r/rrr+r0rSrrrTroutputsr, slice_indicesr*r)rVs r>rYz"CsmDepthDecoderForCausalLM.forward@s;2$**  'A)%+')      nc *" %a %~ot < *M$$ !]A- .Q_Qk}0Mqu ""$  !#qr'?557L%4%%dt{{7M7M\hlrD&#33!//))   r=c t ||||||fi|}|dddk(}|s|jd|jd|S)Nrrr/r)rCprepare_inputs_for_generationpop) rJr.r+rr0rr model_inputsis_first_generation_steprMs r>r\z8CsmDepthDecoderForCausalLM.prepare_inputs_for_generationsgw<  ~ Y_ $00@#A!#D#I '   9 : (r=) NNNNNNNNNrr)r4r5r6_tied_weights_keys_tp_plan_pp_planrDrrr8rr9rr rrrrr;rrYr\r^r_s@r>rPrP+sHH.2?C.204(,26*.!%26-.@ ##d*@ %*$5$5$<@  t+ @ &&- @  @ ((4/@   4'@ $;@ ((4/@ ell*@ +,@  ' '@ @ J)-262626 ##((4/  ((4/  ((4/ r=rPc$eZdZfdZdZxZS)rct|tj|j|j z|j |_|jdtj|j|j zdy)NrFrg) rCrDrEr#r rrKembed_audio_tokensrpr8r}rs r>rDz#CsmBackboneModelEmbeddings.__init__sn "$,,0D0DvGXGX0X[a[m[m"n  "ELL1E1E$FIZIZ$Zgl  r=cf|j||jz}|jd}|S)Nr r)rersum)rJr.r5s r>rYz"CsmBackboneModelEmbeddings.forwards6..y4;T;T/TU #''A'. r=rr_s@r>rrs  r=rceZdZfdZee d dejdzdejdzdejdzde dzdejdzdejdzd e dzd e e d efd ZxZS)CsmBackboneModelc t|||j|_|j|_t ||_tjt|jDcgc]}t||c}|_ t|j|j|_t#||_d|_|j)ycc}w)Nrr F)rCrDr!r"rrr%rEr&r r'rr(rArKrr)rar*r+r-rs r>rDzCsmBackboneModel.__init__s  !.. ++6v>mmAFvG_G_A` aI_VY / a  v11v7J7JK ,F;&+#  bs)CNr.rrr+r0rrrruc D|du|duz r td||j|}|r|t|j}|E||j nd} t j |jd|j| z}||jd}t|j|||||} |} |j| |} |jd|jjD]} | | f| | ||||d |} |j| } t| | S) a& input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks) or (batch_size, sequence_length)`): 1. (batch_size, sequence_length): corresponds to the input sequence prepared with the processor from the text prompt. Such input requires `input_values` to be provided so that audio can be encoded in codebook tokens and then merged with the text tokens. 2. (batch_size, sequence_length, num_codebooks): codebook tokens generated during the autoregressive decoding. Such input is not meant to be used by end users. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Nz:You must specify exactly one of input_ids or inputs_embedsr rr"r2r4r6)rrrr+rrr7)r;r%r rcr<r8r}r[rrrrr*r(r'r)r)rJr.rrr+r0rrrr?rr,rrDs r>rYzCsmBackboneModel.forwards]2  -t"; <YZ Z  *.*;*;I*FM 0*$++>O  !CRC^==?de  ]003Mriris .2.204(,2626!%E ##d*E  t+E &&- E  E ((4/ E ((4/E $;E +,E  !E E r=riz The Csm model consists of two llama-like auto-regressive transformer models: a backbone model that predicts the first codebook token and a depth decoder that predicts the other codebook tokens. ceZdZddiZfdZdZdZefdZfdZ dd e jdzd e jdzd e jdzd e jdzd e jdzf dZ dd e jdedzde jdzde jdzde jdzf fd Zee dd e jdzd e jdzde jdzd e jdzde jdzdedzde jdzd e jdzdedzde jdzdee jzdeed eezfdZxZS)CsmForConditionalGenerationz5backbone_model.embed_tokens.embed_audio_tokens.weightz'depth_decoder.model.embed_tokens.weightct|||j|_tj|j |jd|_tj|j|j |_ tj||_ tj|j|_t!j"|j$|_|j)y)NFr)rCrDrrErrKlm_headr#text_vocab_sizeembed_text_tokensri _from_configbackbone_modelrPdepth_decoder_config depth_decoderr! from_config codec_config codec_modelr-rs r>rDz$CsmForConditionalGeneration.__init__s   ++yy!3!3V5F5FUS !#f.D.DfFXFX!Y.;;FC7DDVE`E`a$001D1DE r=c.|jjSrrsr%r\s r>get_input_embeddingsz0CsmForConditionalGeneration.get_input_embeddingss""///r=c&||j_yrrz)rJrs r>set_input_embeddingsz0CsmForConditionalGeneration.set_input_embeddingss+0(r=c|jddrt ||i|\}}nt ||i|}d}t|}t |j j Dcic]\}}|j|r||d|} }}t |jj jddi| | D]}t|j ||zd|vr|fS|Scc}}w)Noutput_loading_infoFdepth_decoder__from_model_config) getrCfrom_pretrainedlenvarsgeneration_configitems startswithrurdelattr) clsargsrr loading_infoprefix prefix_lenattrrdepth_decoder_attrsrMs r>rz+CsmForConditionalGeneration.from_pretraineds ::+U 3"''"94"J6"J E<G+Trz+CsmForConditionalGeneration.save_pretrained9s|!"00BBOOQ 6=.446 BKD% D**FTM5 A B 00r=Nr. input_valuesinput_values_cutoffsrSruc|j|}|Utjj|d}||dk\j }||dkD}t j |j|jjt|d}||jdk}t j5g}t||D]\} } | | dk\} t| jddz D]r} | | } | | dz} | d| | f}|j j#|jd}|j$j'dd}|j)|dttd|D}t j*|Dcgc]6}tjj|ddd||jdz f8c}}|j j-|}ddd|j.j0}||k(}|j2j5}|||<t j6dd|j.j8f|jt j: |j.j<z}|j2j5|j?d}||j.j@k(}|jC|jEd||<|j|jdjCdd|j.j8}||||<|||<|d k(jGd }d ||d|dddf<|}||dScc}w#1swYxYw)a Merges the input_ids and input_values to produce a single inputs_embeds tensor: 1 - Infers the codec model on the input_values to retrieve codebook token. 2 - Embeds codebook tokens and places them at the correct positions in the inputs_embeds tensor. 3 - If labels are provided, expands them to match codebook dimensions and position the target codebook tokens in the inputs_embeds tensor. Args: input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`): The input ids to embed. input_values (`torch.Tensor` of shape `(batch_size, channels, audio_sequence_length)`): The audio input values to embed. input_values_cutoffs (`torch.Tensor` of shape `(batch_size, max_num_audio)`): The cutoffs of the audio input values relative to its batch index, padded with -1 when no audio. Nr"rrr2rOr".c3:K|]}|jdyw)rN)r[).0els r> zQCsmForConditionalGeneration._merge_input_ids_with_input_values..ss&Orrxx{&OsrziTas_tuple)r0rS)$rqrErpaddiffr8r}maxrrrrrrzipr r[rxencode audio_codesrappendrKget_audio_codes_maskrcaudio_token_idrsr%rGr longcodebook_eos_token_idsqueezeaudio_eos_token_idrepeatrgnonzero)rJr.rrrSr0 audio_lengthsinput_values_maskaudio_tokens_listbatch_input_valuesbatch_input_values_cutoffsr start_idxend_idx audio_batch codec_outputs codebook_idsmax_audio_framesrbatched_audio_token_idsaudio_codes_maskraudio_token_mask audio_embedsaudio_eos_frame_idsaudio_eos_embedsaudio_eos_token_masklabels_expanded depth_decoder_ignore_frames_idxss r>"_merge_input_ids_with_input_valuesz>CsmForConditionalGeneration._merge_input_ids_with_input_valuesCs*..y9  ##%==#4#45I6#R 01E1JKPPRM)-!*;q$A "O9O&P (,(8(8(?(? @U@UVW@X(Y '4'@'@'J'J1b'Q )00aA BB$'&O=N&O#O */++`qrZ\R]]&&rAq!5EQR 5S+TUr+'$(#3#3#H#HIZ#[ ! \$"[[77N(N: ..;;r+rr0rc 0t |d |||||d|}|}|jdk(rn|jd]|j ||jd|jd|jd}|j |d|ddd|S) N)r.r+rr0rr r0rrrS)r.rrrS)r0rSr.r<)rCr\ndimrrr) rJr.r+rr0rrr^ merged_inputsrMs r>r\z9CsmForConditionalGeneration.prepare_inputs_for_generationsw< +)')      Y^^q%8\=M=Mo=^=f CC##ZZ7%+ZZ0F%Gzz(+ DM   "/"@MZbLcrvw r=rrrTrc |/|jdk(r |j||||} | d}| d}d}|jd|||||| | d| }|d}t| tr t | dn| }|j |dd|ddf}d}d}d}d}||dddddf}|jd|||jjd| }|ddddddfd k(jd }||d d|jjdz f}tjj|d d}|jd}||d|ddz ddf}||}|j d||| d|d| }|j"}||z}t%|||||j&|j(|j*| |j,nd| |j&nd| |j(nd||j* Sd S)a input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks) or (batch_size, sequence_length)`): 1. (batch_size, sequence_length): corresponds to the input sequence prepared with the processor from the text prompt. Such input requires `input_values` to be provided so that audio can be encoded in codebook tokens and then merged with the text tokens. 2. (batch_size, sequence_length, num_codebooks): codebook tokens generated during the autoregressive decoding. Such input is not meant to be used by end users. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) input_values_cutoffs (`torch.Tensor` of shape `(batch_size, max_num_audio)`, *optional*): Specify the end positions of audio segments within each batch entry, relative to the concatenated audio input. If a batch entry has fewer segments than the maximum, it is padded with -1. For example, in a batch of 2 sequences where the first contains 2 audio segments of length l1, and the second contains 1 audio segment of length l2, the input_values_cutoffs would be: [[l1, 2 * l1], [l2, -1]]. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[config.audio_token_id, -100, -101]`. Requires targeted `input_values` to be provided as audio tokens will be inferred from it using the `codec_model`. - `config.audio_token_id` indicates an audio frames (considering sequence length elements as frames) - `-100` will be ignored in the loss computation - `-101` indicates the audio frame will be used only for the backbone model (using the first codebook token as labels) Such labels can be prepared using `output_labels=True` when calling [`CsmProcessor`]. logits_to_keep (`int` or `torch.Tensor`, *optional*): Kept for compatibility. Does not support another value than: 1. `0`, which is equivalent to keeping all logits, used in the training regime 2. `1`, which is equivalent to keeping only the last logit, used in the generation regime Example: ```python >>> import torch >>> from transformers import CsmForConditionalGeneration, AutoProcessor >>> from datasets import load_dataset, Audio >>> model_id = "sesame/csm-1b" >>> torch_device = "cuda" if torch.cuda.is_available() else "cpu" >>> processor = AutoProcessor.from_pretrained(model_id) >>> ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train") >>> # ensure the audio is 24kHz >>> ds = ds.cast_column("audio", Audio(sampling_rate=24000)) >>> conversation = [] >>> # prepare a conversation with text and corresponding audio >>> for text, audio, speaker_id in zip(ds[:4]["text"], ds[:4]["audio"], ds[:4]["speaker_id"]): ... conversation.append( ... { ... "role": f"{speaker_id}", ... "content": [{"type": "text", "text": text}, {"type": "audio", "path": audio["array"]}], ... } ... ) >>> inputs = processor.apply_chat_template( ... conversation, ... tokenize=True, ... return_dict=True, ... output_labels=True, ... ).to(torch_device) >>> model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=torch_device) >>> output = model(**inputs) >>> output.loss.backward() ```Nr r0rS)r.rrr+r0rrr)r*rSrr"rrOr.r)rTr)r.r/r return_dictrS) r)r3r.r*r+r,r-r/r0r1r2r<)rrrsrrrWrorXrcrallr rErrrrur)r(r+r,r-r*)rJr.rrrrr+r0rSrrrTrrbackbone_outputsbackbone_hidden_statesrZbackbone_logitsr)r3r.depth_decoder_outputsbackbone_labels train_maskdepth_decoder_input_ids train_idxsbackbone_last_hidden_statesdepth_decoder_labelss r>rYz#CsmForConditionalGeneration.forwardsf  Y^^q%8 CC<)=vM*/:M"8,FI.4..  )%+')    "2!!48B>SV8W~ot4]k ,,'=aPQ>Q'RS ! $  $Q1WoO.D..&4;;KaKaekM "!Q(+t388R8@@J&,Z&8>] @Y@Y\]@]>]9]&^ #&(mm&7&78OQW_`&7&a ##++T+:J*@APZ[\P]`aPacdAd*e '#)*#5 $6D$6$6%1+F# + %  % !"7!;!;  #55D '1",<<*88'22AVAb!6!=!=hl$0+@*O*O$0)>(K(KI^Ij%:%E%E  qu  r=r) NNNNNNNNNNr)r4r5r6r`rDr{r} classmethodrrr8rrrr r9r\rrrrrrr;r(rYr^r_s@r>rmrmsd @Aj0141*.,048&* PB<<$&PBllT)PB$llT1 PB  t# PB   PBj)-262626 ##((4/  ((4/  ((4/ >.2,0.24804(,26*.!%26-.[ ##d*[ llT)[  t+ [ $llT1 [ &&- [ [ ((4/[   4'[ $;[ ((4/[ ell*[ +,[  " "[ [ r=rm)rrirrPrm)r")r)Rcollections.abcr dataclassesrtypingrr8torch.nnrEtransformers.utils.genericrrr  activationsr cache_utilsr r generationr integrationsr rr masking_utilsrmodeling_layersrmodeling_outputsrrmodeling_rope_utilsrrmodeling_utilsrrprocessing_utilsrutilsrrrrr utils.genericrutils.import_utilsrautor!configuration_csmr#r$generation_csmr% get_loggerr4r9r(ModulerArarrrrrrrrrrrrrrPrrirm__all__r<r=r>rs*%! 9&!.)ff/9OKF&__+:?.   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