o i@sddlmZddlmZmZmZddlZddlmZddl m Z ddl m Z ddl mZmZddlmZdd lmZdd lmZdd lmZdd lmZmZdd lmZmZddlmZm Z ddl!m"Z"ddl#m$Z$m%Z%m&Z&m'Z'm(Z(ddl)m*Z*ddl+m,Z,ddl-m.Z.m/Z/ddl0m1Z1e(2e3Z4ee&ddGddde$Z5edGdddej6Z7Gdddej6Z8Gd d!d!ej6Z9d"d#Z:dLd$d%Z;d&ej +dMd,ej6d-ejd>eCZDGd?d@d@ej6ZEe&dAdGdBdCdCeCeZFGdDdEdEej6ZGe&GdFdGdGeCZHe&dHdGdIdJdJeCe1ZIgdKZJdS)N) dataclass)CallableOptionalUnionN)check_model_inputs)ACT2FN)Cache DynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack) ModelOutputTransformersKwargsauto_docstringcan_return_tuplelogging)deprecate_kwarg) AutoModel) CsmConfigCsmDepthDecoderConfig)CsmGenerationMixinz: Base class for the model autoregressive outputs. ) custom_introc@seZdZUdZdZeejed<dZ eejed<dZ ee ed<dZ ee ejdfed<dZee ejdfed<dZeejed <dZeejed <dZee ed <dZee ejdfed <dZee ejdfed <dZeejed<dS)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$rtorch FloatTensor__annotations__r%r&r r'tupler(r)r*r+r,r-r.r7r7X/home/ubuntu/.local/lib/python3.10/site-packages/transformers/models/csm/modeling_csm.pyr#2s r#RMSNormcs.eZdZdfdd ZddZddZZS) CsmRMSNormư>cs&ttt||_||_dS)z9 CsmRMSNorm is equivalent to T5LayerNorm N)super__init__nn Parameterr3onesweightvariance_epsilon)self hidden_sizeeps __class__r7r8r=ds  zCsmRMSNorm.__init__cCsJ|j}|tj}|djddd}|t||j}|j||S)NrT)keepdim) dtypetor3float32powmeanrsqrtrBrA)rCr' input_dtypevariancer7r7r8forwardls  zCsmRMSNorm.forwardcCst|jjd|jS)Nz, eps=)r6rAshaperBrCr7r7r8 extra_reprsszCsmRMSNorm.extra_repr)r;)r/r0r1r=rRrU __classcell__r7r7rFr8r:bsr:csDeZdZUejed<ddeffdd Zee ddZ Z S) CsmRotaryEmbeddinginv_freqNconfigcstt|drt|jtr|jd|jd|_nd|_|j|_ |j|_ ||_ t |j|_ | |j |\}|_|jd|dd|j|_dS)N rope_scaling rope_typetypedefaultrXF persistent)r<r=hasattr isinstancerZdictgetr[max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrYr rope_init_fnattention_scalingregister_bufferrXoriginal_inv_freq)rCrYdevicerXrFr7r8r=zs   zCsmRotaryEmbedding.__init__c Cs|jddddf|jddd|j}|dddddf}t|jjtr6|jjdkr6|jjnd}t j |dd+|| dd}t j ||fdd }| |j}||j} Wdn1smwY|j|jd | j|jd fS) NrrHrmpscpuF) device_typeenabledrdim)rJ)rXfloatexpandrSrKrkrar\strr3autocast transposecatcosrhsinrJ) rCx position_idsinv_freq_expandedposition_ids_expandedrnfreqsembrxryr7r7r8rRs0&zCsmRotaryEmbedding.forwardN) r/r0r1r3Tensorr5rr=no_gradrrRrVr7r7rFr8rWws rWc$eZdZfddZddZZS)CsmMLPcsxt||_|j|_|j|_tj|j|j|jd|_tj|j|j|jd|_ tj|j|j|jd|_ t |j |_ dS)Nbias)r<r=rYrDintermediate_sizer>Linearmlp_bias gate_projup_proj down_projr hidden_actact_fnrCrYrFr7r8r=s zCsmMLP.__init__cCs$||||||}|Sr)rrrr)rCrzrr7r7r8rRs zCsmMLP.forwardr/r0r1r=rRrVr7r7rFr8rs  rcCsH|dd|jddf}|d|jdddf}tj| |fddS)z*Rotates half the hidden dims of the input..NrHrrp)rSr3rw)rzx1x2r7r7r8 rotate_halfsrcCsD||}||}||t||}||t||}||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. ) unsqueezer)qkrxryr{ unsqueeze_dimq_embedk_embedr7r7r8apply_rotary_pos_embs  rr'n_repreturncCs^|j\}}}}|dkr |S|dddddddddf|||||}||||||S)z This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). 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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 rrr{r&rrrrrN.)r%r0r shift_labels)r$r%r&r'r(r7) rrarslicer/r loss_functionrYrrr&r'r()rCr rrr{r&rr0rrr1routputsr' slice_indicesr%r$r2r7r7r8rRsJ   & z"CsmDepthDecoderForCausalLM.forwardc  sHtj|||||fi|}|dddk}|s|d|d|S)Nrrrr{)r<prepare_inputs_for_generationpop) rCr r&rrrr model_inputsis_first_generation_steprFr7r8r7cs   z8CsmDepthDecoderForCausalLM.prepare_inputs_for_generation) NNNNNNNNNrNNNN)r/r0r1_tied_weights_keys_tp_plan_pp_planr=rrrr3rr4rrr listrrrrr6rrRr7rVr7r7rFr8r. sr       Er.cr)CsmBackboneModelEmbeddingscsDtt|j|j|j|_|jdt |j|jdddS)Naudio_tokens_offsetsFr^) r<r=r>rrrrDembed_audio_tokensrir3rrrFr7r8r={s  z#CsmBackboneModelEmbeddings.__init__cCs |||j}|jdd}|S)Nrrp)rBrAsum)rCr rr7r7r8rRs z"CsmBackboneModelEmbeddings.forwardrr7r7rFr8r@zs r@cseZdZfddZee       ddeejdeej deejdee deej d eejd ee d e ed efd dZZS)CsmBackboneModelcsvtj|_j|_t|_tfddt j D|_ t j jd|_td|_d|_|dS)Ncrr7rrrr7r8rrz-CsmBackboneModel.__init__..rrF)r<r=rrrr@rr>rrrrr:rDrrrWr r r rrFrr8r=s    zCsmBackboneModel.__init__Nr rr{r&rrrrrc Ks|du|duAr td|dur||}|r!|dur!t|jd}|dur=|dur-|nd} tj| | |jd|jd}|durF| d}t |j|||||d} |} | | |} |j d|jj D]} | | f| |||| d|} qb|| } t| |d S) a& input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks) or (batch_size, sequence_length)`): 1. (batch_size, sequence_length): corresponds to the input sequence prepared with the processor from the text prompt. Such input requires `input_values` to be provided so that audio can be encoded in codebook tokens and then merged with the text tokens. 2. (batch_size, sequence_length, num_codebooks): codebook tokens generated during the autoregressive decoding. Such input is not meant to be used by end users. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Nz:You must specify exactly one of input_ids or inputs_embedsrrrrr)rr{r&rrr)rrr rYrr3rrSrkrr r rrrr)rCr rr{r&rrrrrrr'rr$r7r7r8rRsP      zCsmBackboneModel.forward)NNNNNNN)r/r0r1r=rrrr3rrr r4rrrrrRrVr7r7rFr8rDs<   rDz 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. cseZdZddgZfddZddZddZd d Zefd d Z fd dZ    d$de e j de e j de e j de e j de e j f ddZ    d$de jde ede e jde e jde e jf fdd Zee           d%de e jde e j de e j de e j de e jde eeee jfde e jde e jde ede e jd eee j fd!eedeeeffd"d#ZZS)&CsmForConditionalGenerationz5backbone_model.embed_tokens.embed_audio_tokens.weightz'depth_decoder.model.embed_tokens.weightcspt||j|_tj|j|jdd|_t|j|j|_ t ||_ t |j|_t|j|_|dS)NFr)r<r=rr>rrDlm_headrtext_vocab_sizeembed_text_tokensrD _from_configbackbone_modelr.depth_decoder_config depth_decoderr from_config codec_config codec_modelr rrFr7r8r=s   z$CsmForConditionalGeneration.__init__cCs|jjSrrJrrTr7r7r8get_input_embeddingssz0CsmForConditionalGeneration.get_input_embeddingscCs ||j_dSrrP)rCrr7r7r8set_input_embeddingss z0CsmForConditionalGeneration.set_input_embeddingscCs(|jjr||jjj|jjjdSdSr)rYtie_codebooks_embeddings_tie_or_clone_weightsrJrrBrLrrTr7r7r8 _tie_weightss z(CsmForConditionalGeneration._tie_weightscs|ddrtj|i|\}}n tj|i|}dtfddt|jD}t|jjddi||D] }t |j|q?d|vrR||fS|S)Noutput_loading_infoFdepth_decoder_cs(i|]\}}|r|d|qSr) startswith)rattrrprefix prefix_lenr7r8 s  z?CsmForConditionalGeneration.from_pretrained.._from_model_config) rcr<from_pretrainedlenvarsgeneration_configitemsrLrdelattr)clsargsrr loading_infodepth_decoder_attrsrYrFrZr8r_s   z+CsmForConditionalGeneration.from_pretrainedcsVd}|jj}|dd|D] \}}t|j|||qtj|i|dS)NrWtransformers_version)rLrb to_diff_dictr8rcsetattrr<save_pretrained)rCrfrr[rhrYrrFr7r8rls   z+CsmForConditionalGeneration.save_pretrainedNr  input_valuesinput_values_cutoffsr0rcsF||}|durtj|d}||dk}||dk}tj||jd t |d}|| dk}t jg}t ||D]?\} } | | dk} t| jddD]+} | | } | | d} | d| | f}|j| d}|jdd}||dqUqBtdd |Dtfd d |D}|j|}Wdn1swY|jj}||k}|j|}||||<tjdd|jjf|jtjd |jj}|j|d}||jj k}|!|"d||<|dur| d!dd|jj}||||<|||<|d kj#dd}d||d|dddf<|}||dS)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. NrrrrrHr.css|]}|jdVqdS)rN)rSrelr7r7r8 YszQCsmForConditionalGeneration._merge_input_ids_with_input_values..c s,g|]}tj|ddd|jdfqS)r)r>rpadrSrpmax_audio_framesr7r8r[s,zRCsmForConditionalGeneration._merge_input_ids_with_input_values..)rkrJiTas_tuple)rr0)$rHr>rrsdiffr3rmaxrkrsr`rrziprrSrOencode audio_codesrvappendr,get_audio_codes_maskrYaudio_token_idrJrr@rlongcodebook_eos_token_idsqueezeaudio_eos_token_idrepeatrCnonzero)rCr rmrnr0r audio_lengthsinput_values_maskaudio_tokens_listbatch_input_valuesbatch_input_values_cutoffsr start_idxend_idx audio_batch codec_outputs codebook_idsbatched_audio_token_idsaudio_codes_maskraudio_token_mask audio_embedsaudio_eos_frame_idsaudio_eos_embedsaudio_eos_token_masklabels_expanded depth_decoder_ignore_frames_idxsr7rtr8"_merge_input_ids_with_input_values)s\            z>CsmForConditionalGeneration._merge_input_ids_with_input_valuesr&rrrc  stjd |||||d|}|dur>|jdkr>|ddur>|j||d|d|dd}||d|ddd|S) N)r r&rrrrrrmrnr0)r rmrnr0)rr0r r7)r<r7ndimrcrr) rCr r&rrrrr9 merged_inputsrFr7r8r7{s(  z9CsmForConditionalGeneration.prepare_inputs_for_generationrr{rr1rc  Ks|dur|jdkr|||||} | d}| d}d}|jd|||||| | d| }|d}t| tr:t| dn| }||dd|ddf}d}d}d}d}|dur|dddddf}|jd|||jj d| }|ddddddfd kj d d }||d d|jj df}t j j|d dd}|jdd}||d|ddddf}||}|jd||| d|d| }|j}||}t|||||j|j|j|dur|jnd|dur|jnd|dur|jnd|dur|jd Sdd 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() ```Nrrr0)r rr{r&rrrr)r%r0rrrxrHrp.ro)rTrv)r rr return_dictr0) r$r.r)r%r&r'r(r*r+r,r-r7)rrrJrarr3rFr4rYrallrr>rrsrrLr$r#r&r'r(r%)rCr rmrrnr{r&rr0rrr1rrbackbone_outputsbackbone_hidden_statesr6backbone_logitsr$r.r)depth_decoder_outputsbackbone_labels train_maskdepth_decoder_input_ids train_idxsbackbone_last_hidden_statesdepth_decoder_labelsr7r7r8rRsS  (   z#CsmForConditionalGeneration.forwardr;) NNNNNNNNNNr)r/r0r1r<r=rQrRrU classmethodr_rlrr3rrrr r4r7rrrr?rrrrr6r#rRrVr7r7rFr8rEs     U      rE)rrDrr.rEr%)r)K dataclassesrtypingrrrr3torch.nnr>transformers.utils.genericr activationsr cache_utilsr r generationr integrationsr masking_utilsr modeling_layersrmodeling_outputsrrmodeling_rope_utilsrrmodeling_utilsrrprocessing_utilsrutilsrrrrrutils.deprecationrautorconfiguration_csmrr generation_csmr! get_loggerr/rr#Moduler:rWrrrrrrrrrrrrrrr.r@rDrE__all__r7r7r7r8s             *$  G.jiY U