o Zh @sdZddlZddlmZmZmZmZddlZddlZddlm Z ddl m Z ddl mZddlmZmZdd lmZdd lmZdd lmZmZdd lmZmZmZdd lmZmZddl m!Z!ddl"m#Z#m$Z$m%Z%m&Z&ddl'm(Z(e%rddl)m*Z*ddl+m,Z,erddlm-Z-e&.e/Z0   d@deej1eej1dfdee2deej1deej1e2ffddZ3Gddde j4Z5Gddde j4Z6Gd d!d!e j4Z7Gd"d#d#e j4Z8Gd$d%d%e j4Z9Gd&d'd'e j4Z:d(d)Z;dAd*d+ZGd0d1d1e=Z?e=e?e>d2Z@Gd3d4d4e j4ZAe#Gd5d6d6e!ZBe#Gd7d8d8eBZCGd9d:d:eBeZDe#d;d<Gd=d>d>eBZEgd?ZFdS)BzPyTorch JetMoe model.N)ListOptionalTupleUnion)nn) functional)ACT2FN)Cache DynamicCache)GenerationMixin)AttentionMaskConverter)!flash_attn_supports_top_left_maskis_flash_attn_available)MoeCausalLMOutputWithPastMoeModelOutputWithPast SequenceClassifierOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)PreTrainedModel)auto_docstringcan_return_tupleis_torch_flex_attn_availablelogging) JetMoeConfig) BlockMask)make_flex_block_causal_mask)_flash_attention_forward gate_logits num_expertsattention_maskreturncs|dus t|ts dSt|tr#|djtjfdd|Ddd}tjjj|dd}tj||dd\}}tjj ||}|durStj | dd} tj |dd} ng|j \} } |j d| | } |dddddddf | | | ||fd||}tj| |ddtj|dd} |ddddddf | | | |fd|}tj||ddtj|dd} t| | d}||S)a Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between experts is too unbalanced. Args: gate_logits: Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of shape [batch_size X sequence_length, num_experts]. num_experts: Number of experts top_k: The number of experts to route per-token, can be also interpreted as the `top-k` routing parameter. attention_mask (`torch.Tensor`, *optional*): The attention_mask used in forward function shape [batch_size X sequence_length] if not None. Returns: The auxiliary loss. Nrcsg|]}|qS)to).0Z layer_gateZcompute_devicer$Y/var/www/auris/lib/python3.10/site-packages/transformers/models/jetmoe/modeling_jetmoe.py Tz,load_balancing_loss_func..dim) isinstancetupledevicetorchcatrrsoftmaxtopkZone_hotmeanfloatshapeexpandreshaper%sum unsqueeze)r r!top_kr"Zconcatenated_gate_logitsZrouting_weights_Zselected_expertsZ expert_maskZtokens_per_expertZrouter_prob_per_expert batch_sizesequence_lengthnum_hidden_layersZexpert_attention_maskZ router_per_expert_attention_maskZ overall_lossr$r'r(load_balancing_loss_func2s>    rAcs6eZdZdedededdffdd Zdd ZZS) JetMoeParallelExpertsr! input_size output_sizer#Ncs6ttt||||_||_||_||_ dS)a Initialize the JetMoeParallelExperts module. The experts weights are stored in [num_experts, output_size, input_size] format. Such that it's compatible with many MoE libraries, such as [Megablock](https://github.com/databricks/megablocks) and [ScatterMoE](https://github.com/shawntan/scattermoe), as well as the [MoE kernel](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/fused_moe.py) used in vllm. Args: num_experts (int): Number of experts. input_size (int): Size of the input. output_size (int): Size of the output. N) super__init__r Parameterr1emptyweightr!rCrD)selfr!rCrD __class__r$r(rFs  zJetMoeParallelExperts.__init__cCsP|j|dd}g}t|jD]}|t|||j|qtj|dd}|S)a Forward pass of the JetMoeParallelExperts module. Args: inputs (Tensor): Input tensor. expert_size: Expert size information. Returns: Tensor: Output tensor. rr+) splitranger!appendFZlinearrIr1r2)rJZinputs expert_sizeZ input_listZ output_listiresultsr$r$r(forwards  zJetMoeParallelExperts.forward__name__ __module__ __qualname__intrFrT __classcell__r$r$rKr(rBsrBcs2eZdZdededeffdd ZddZZS)JetMoeTopKGatingrCr!r<cs2t||_||_||_tj||dd|_dS)a Initialize the top-k gating mechanism. Args: input_size (`int`): Size of the input. num_experts (`int`): Number of experts. top_k (`int`): Number of top experts to select. FbiasN)rErFr!rCr<rLinearlayer)rJrCr!r<rKr$r(rFs zJetMoeTopKGating.__init__cCs||}|j|jdd\}}tj|dd|}tj|d|j g|j |j d}| d|d}| d}|}|} | d\} } | j|jdd} |}|| } | | | ||fS)Nrr+rdtyper0trunc)Z rounding_mode)r_r6r4r<r1r3Ztype_aszerossizer!rar0Zscatterlongr:tolistflattensortdiv)rJ hidden_stateslogitsZ top_k_logitsZ top_k_indicesZ top_k_gatesrcZgatesrQZ top_k_expertsr=index_sorted_experts batch_index batch_gatesr$r$r(rTszJetMoeTopKGating.forwardrUr$r$rKr(r[sr[cs.eZdZdZdeffdd ZddZZS) JetMoeMoEz A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts. Args: config: Configuration object with model hyperparameters. configcstt||j|_|j|_t|j|_t j t |j|_ t|j|j|jd|_t|j|j|j|_t|j|j|jd|_dS)NrrCr!r<)rErorF hidden_sizerCZintermediate_sizer Zactivation_function activationr1rrGrHr]rBnum_local_experts input_linear output_linearr[num_experts_per_tokrouterrJrprKr$r(rFs  zJetMoeMoE.__init__cCs|\}}}|d|}||\}}}}} ||} || |} | jddd} || d| d} || |} | |dddf} tj|||j f| j | j d}| d|| }| |||j }||j}|| fS)a Forward pass of the mixture of experts layer. Args: layer_input (Tensor): Input tensor. Returns: Tensor: Output tensor. Tensor: Router logits. r-rr+rrNr`)rdr9rxruchunkrsrvr1rcrCrar0 index_addviewr])rJ layer_inputbszlengthemb_sizer=rmrnrQ router_logits expert_inputsrjZchunked_hidden_statesexpert_outputsrc layer_outputr$r$r(rTs    zJetMoeMoE.forward)rVrWrX__doc__rrFrTrZr$r$rKr(rosrocs>eZdZdZdeffdd ZddZddZd d ZZ S) JetMoeMoAz A Sparsely gated mixture of attention layer with pairs of query- and output-projections as experts. Args: config: Configuration object with model hyperparameters. rpcstt||j|_|j|_|j|j|_|j |_ t j t |j|_t|j|j|j|_t|j|j|j|_t|j|j|j d|_dS)Nrq)rErrFrtr!rrrC kv_channelsnum_key_value_headsrwr<r1rrGrHr]rBrurvr[rxryrKr$r(rF%s zJetMoeMoA.__init__cCs|\}}}|d|}||\}}}}} ||||f} ||} || |} tj|||j|jf| j| j d} | d|| }| |||jd}|| | fS)z Map inputs to attention experts according to routing decision and compute query projection inside each experts. r-r`r) rdr9rxrur1rcr<rrrar0r{r|)rJr}r~rrrlrmrnrQr topo_inforrrcrr$r$r(map7s    z JetMoeMoA.mapcCs|\}}}}|d|}|\}}} } ||} || | } | | dddf} tj|||jf| j| jd} | d|| }| |||j}||j }|S)zu Compute output projection inside each attention experts and merge the outputs of different experts. r-Nr`r) rdr9rvr1rcrCrar0r{r|r])rJr}rr~rkrrrlrmrnrQrrrcrr$r$r(reduceNs    zJetMoeMoA.reducecCstd)Nz-This module doesn't support call and forward.)NotImplementedError)rJr}r$r$r(rTdzJetMoeMoA.forward) rVrWrXrrrFrrrTrZr$r$rKr(rs rcs.eZdZdfdd ZddZddZZS) JetMoeRMSNormư>cs&ttt||_||_dS)z< JetMoeRMSNorm is equivalent to T5LayerNorm N)rErFrrGr1ZonesrIvariance_epsilon)rJrrepsrKr$r(rFjs  zJetMoeRMSNorm.__init__cCsJ|j}|tj}|djddd}|t||j}|j||S)Nrr-T)Zkeepdim) rar%r1float32powr5ZrsqrtrrI)rJrj input_dtypeZvariancer$r$r(rTrs  zJetMoeRMSNorm.forwardcCst|jjd|jS)Nz, eps=)r/rIr7rrJr$r$r( extra_repryszJetMoeRMSNorm.extra_repr)r)rVrWrXrFrTrrZr$r$rKr(risrcs8eZdZddeffdd ZeeddZZ S)JetMoeRotaryEmbeddingNrpcstt|dr|jdur|jd|jd|_nd|_|j|_|j|_||_ t |j|_ | |j |\}|_ |j d|dd|j|_dS)N rope_scaling rope_typetypedefaultinv_freqF) persistent)rErFhasattrrgetrZmax_position_embeddingsZmax_seq_len_cachedZoriginal_max_seq_lenrprZ rope_init_fnattention_scalingZregister_bufferrZoriginal_inv_freq)rJrpr0rrKr$r(rFs   zJetMoeRotaryEmbedding.__init__c Cs|jddddf|jddd|j}|dddddf}t|jjtr6|jjdkr6|jjnd}t j |dd+|| dd}t j ||fdd }| |j}||j} Wdn1smwY|j|jd | j|jd fS) Nrr-rZmpscpuF) device_typeenabledrr+)ra)rr6r8r7r%r0r.rstrr1Zautocast transposer2cosrsinra) rJx position_idsZinv_freq_expandedZposition_ids_expandedrZfreqsZembrrr$r$r(rTs0&zJetMoeRotaryEmbedding.forwardN) rVrWrXrrFr1Zno_gradrrTrZr$r$rKr(r~s rcCsH|dd|jddf}|d|jdddf}tj| |fddS)z*Rotates half the hidden dims of the input..Nr-rr+)r7r1r2)rx1Zx2r$r$r( rotate_halfsrcCsD||}||}||t||}||t||}||fS)aApplies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. )r;r)qrrrrZ unsqueeze_dimZq_embedZk_embedr$r$r(apply_rotary_pos_embs  rcseZdZdZddedeeffdd Z      ddej d eej d eej d ee d e d e deej de ej eej ee ej ffddZZS)JetMoeAttentionzH Multi-headed attention from 'Attention Is All You Need' paper. Nrp layer_idxcst||_||_d|_|durtd|jjd|j |_ |j |_ |j |j |_|j |_ |j|_|j |_t||_tjj|j|jddd|_t||_dS)z Initialize the JetMoeAttention module. Args: config: Configuration object with model hyperparameters. layer_idx: Index of the layer in the model. TNzInstantiating z without passing a `layer_idx` is not recommended and will lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` when creating this class.rFr\)rErFrpr is_causallogger warning_oncerLrVrwr<attention_dropoutrrkv_projection_sizeZnum_attention_heads num_headshead_dimrexpertsr1rr^rrkv_projr rotary_embrJrprrKr$r(rFs"  zJetMoeAttention.__init__Frjr"rpast_key_valueoutput_attentions use_cachecache_positionr#cCs"|\}} } |j|\} } } ||jddd\}}| || |j|jdd} ||| |j |jdd}||| |j |jdd}| ||\}}t | |||\} }|durl|||d}| |||j |\}}|d|jdd}|d|jdd}t| |ddt|j}|dur|ddddddd|jdf}||}tjj|dtjd| j}tjj||j|jd }t||}|||j| |jfkrtd ||j| |jfd ||dd}| || |j|j!}|j"|| }||| d}|s d}|||| fS) Nrr-r+rrrrr)r,ra)ptrainingz `attn_output` should be of size z , but is )#rdrrrrzr|rrrrrrupdaterrepeatr<r1matmulmathsqrtr7rrr3rr%radropoutrr ValueError contiguousr9rr)rJrjr"rrrrrr~q_lenr= query_statesrr key_states value_statesrr cache_kwargs attn_weights causal_mask attn_outputr$r$r(rTsB   &  zJetMoeAttention.forwardrNNNFFN)rVrWrXrrrrYrFr1Tensor LongTensorr boolrrTrZr$r$rKr(rs4% rcseZdZdZ      ddejdeejdeejdeede d e d eejd e ejeejee ejeejfffd d Z Z S)JetMoeSdpaAttentionz JetMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `JetMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. NFrjr"rrrrrr#c s |rtdtj|||||||dS|\}} } |j|\} } } ||jddd\}}| || |j |j  dd} | || |j |j  dd}| || |j |j  dd}|||\}}t| |||\} }|dur|||d}||||j|\}}|d|jdd}|d|jdd}|}|dur|ddddddd|jdf}| jjd kr|dur| } |}|}|dur| dkrd nd }tjjj| ||||jr|jnd |d }| dd}||| |j|j}|j || }| || d}|d|| fS)NaJetMoeModel is using JetMoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.rjr"rrrrrrr-r+rrrcudaTF)Z attn_maskZ dropout_pr)!rrrErTrdrrrrzr|rrrrrrrrrr<r7r0rrr1rrZscaled_dot_product_attentionrrr9rr)rJrjr"rrrrrr~rr=rrrrrrrrrrrrKr$r(rT/s^   &  zJetMoeSdpaAttention.forwardr) rVrWrXrr1rrrr rrrTrZr$r$rKr(r's2 & rcseZdZfddZ      ddeejdeejdeejdeed ee d ee d eejd e e ej e ej fee ej e ej e ej d ffffddZ ZS)JetMoeFlashAttention2cstj|i|t|_dSr)rErFr_flash_attn_uses_top_left_mask)rJargskwargsrKr$r(rFs zJetMoeFlashAttention2.__init__NFrjr"rrrrrr#.c Csd}|\}} } |j|\} } } ||jddd\}}| || |j|jdd} ||| |j |jdd}||| |j |jdd}| ||\}}t | |||\} }|durn|||d}| |||j |\}}|d|jdd}|d|jdd}| dd} |dd}|dd}|jr|jnd}| j}|tjkrtrt}nt|jd r|jj}n|jjj}td |d | |} ||}||}t| |||| ||j|j d |}|!|| |j|j"}|j#|| }||| | }|sd}|||| fS) a Forward pass of the JetMoeAttention module. Args: hidden_states (Optional[torch.FloatTensor]): Input hidden states. attention_mask (Optional[torch.FloatTensor]): Attention mask. layer_past (Optional[Tuple[torch.Tensor]]): Past layer state. use_cache (Optional[bool]): Whether to use cached states. output_attentions (Optional[bool]): Whether to output attention weights. cache_position (Optional[torch.LongTensor]): Position of the cache. Returns: Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Optional[Tuple[...]]]: Tuple containing outputs. Frr-r+rNrr_pre_quantization_dtypezThe input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. 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N) rErFrrrinput_layernormJETMOE_ATTENTION_CLASSES_attn_implementationself_attentionpost_attention_layernormromlprrKr$r(rFs   zJetMoeBlock.__init__Frjrrr"routput_router_logitsrrr#.c  Cs|j||||||||d\} } } } || }|||\} }|| }|f}|r/|| f7}|r6|| f7}|r>|| |f7}|S)Nr)rrrr)rJrjrrr"rrrrrZself_attn_weightsZpresent_key_valueZattn_router_logitsZx_mlpZmlp_router_logitsoutputsr$r$r(rT s(     zJetMoeBlock.forwardr)NNNFFFN)rVrWrXrrrYrFr1rrrrrrrTrZr$r$rKr(rs8  ( rc@s8eZdZeZdZdZdgZdgZdZ dZ dZ ddZ dS) JetMoePreTrainedModelZ transformerFrpast_key_valuesTcCst|tjfr!|jjjd|jjd|jdur|jj dSdSt|tj rD|jjjd|jjd|j durB|jj|j  dSdSt|t rR|jj ddSt|trd|jjjd|jjddSt|trq|jj dSt|tr~|jj dSdS)zInitialize the weights.r)r5ZstdNg?)r.rr^rIdataZnormal_rpZinitializer_ranger]Zzero_ Embedding padding_idxrZfill_rBrro)rJmoduler$r$r( _init_weights<s&       z#JetMoePreTrainedModel._init_weightsN) rVrWrXrZ config_classZbase_model_prefixZsupports_gradient_checkpointingZ_no_split_modulesZ_skip_keys_device_placementZ_supports_flash_attn_2Z_supports_sdpaZ_supports_cache_classrr$r$r$r(r1s rcs&eZdZdZdeffdd ZddZddZee d"d e e j d e e j d e e j d e eeee jfde e jde ede ede ede ede e j defddZ d#d ee j dfde j de j d edef ddZed e j dedede jde j def d d!ZZS)$ JetMoeModelz Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JetMoeBlock`] Args: config: JetMoeConfig rpcs~tj|_j|_tjj|j|_t fddt j D|_ j |_ tjjd|_d|_|dS)Ncsg|]}t|qSr$)r)r&rrpr$r(r)br*z(JetMoeModel.__init__..)rF)rErF pad_token_idr vocab_sizerrrr embed_tokensZ ModuleListrNr@layersrrZ rms_norm_epsnormgradient_checkpointing post_initryrKrr(rF\s   zJetMoeModel.__init__cC|jSrrrr$r$r(get_input_embeddingskz JetMoeModel.get_input_embeddingscC ||_dSrrrJvaluer$r$r(set_input_embeddingso z JetMoeModel.set_input_embeddingsN input_idsr"rr inputs_embedsrroutput_hidden_statesrrr#c  Cs|dur|n|jj}|dur|n|jj}| dur| n|jj} |dur$|n|jj}|du|duAr4td|jrC|jrC|rCt dd}|durL| |}d} |rit |t sid} |dur_t }n t |}t d| dur|duru|nd} tj| | |jd|jd} |dur| d}|dur|jd kr|r|jd} |ddd f| k}|rtd |||| ||}|}|rd nd}|rd nd}| rd nd}d}|jD]Q}|r||f7}|jr|jr|j|j|||||| |dd }n ||||||| |d}|d}|r||r dnd}|r||df7}| r%||d|d f7}q||}|r3||f7}|r8|nd}| rA|}t|||||dS)Nz:You must specify exactly one of input_ids or inputs_embedszX`use_cache=True` is incompatible with gradient checkpointing. 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Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. N)Z fill_valuerar0r)Zdiagonalr r-r)r,r1rrfullr0Ztriurr9r8cloner7r%Z masked_fill) r"r?rrarr>rrrZ mask_lengthZ padding_maskr$r$r(r>s,  $ 6  zAJetMoeModel._prepare_4d_causal_attention_mask_with_cache_position) NNNNNNNNNN)F)rVrWrXrrrFrrrrrr1rrrr rrrrrTr staticmethodrYrarrZr$r$rKr(rRs       DrcseZdZdgZfddZddZddZdd Zd d Zd d Z ddZ e e            d!de ejde ejde ejde eejde ejde ejde ede ede ede ede ejdeeejfdefdd ZZS)"JetMoeForCausalLMzlm_head.weightcsPt|t||_|j|_|j|_tj|j|jdd|_ |j |_ | dSNFr\) rErFrmodelr aux_loss_coefrr^rrlm_headZtie_word_embeddingsrryrKr$r(rFzs   zJetMoeForCausalLM.__init__cC|jjSrr"rrr$r$r(rrz&JetMoeForCausalLM.get_input_embeddingscC ||j_dSrr&rr$r$r(r z&JetMoeForCausalLM.set_input_embeddingscCrrr$rr$r$r(get_output_embeddingsrz'JetMoeForCausalLM.get_output_embeddingscCrrr))rJZnew_embeddingsr$r$r(set_output_embeddingsrz'JetMoeForCausalLM.set_output_embeddingscCrrr")rJdecoderr$r$r( set_decoderrzJetMoeForCausalLM.set_decodercCrrr,rr$r$r( get_decoderrzJetMoeForCausalLM.get_decoderNrr r"rrr labelsrrr rrlogits_to_keepr#c  Ks\|dur|n|jj}| dur| n|jj} |j|||||||| | d }|j}t| tr0t| dn| }||dd|ddf}d}|dur| }|dddddf }|dddf }| d|jj }| d}| |j}|j||fd|jj i| }d}| rt|j|j|j|}|dur||j| |j7}t||||j|j|j|jdS)a 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]`. N) r r"rrr rrr r.r-rr)lossaux_lossrkrrjrr)rprr r"r r.rYslicer$r6rr|rr%r0 loss_functionrArr!rwr#rrrjr)rJr r"rrr r0rrr rrr1rrrjZ slice_indicesrkr2Z shift_logitsZ shift_labelsr3r$r$r(rTsh   zJetMoeForCausalLM.forward) NNNNNNNNNNNr)rVrWrXZ_tied_weights_keysrFrrr*r+r.r/rrrr1rrrrrrrYrrTrZr$r$rKr(r wsd        r a The JetMoe Model transformer with a sequence classification head on top (linear layer). [`JetMoeForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. 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