o wi;@sdddlmZmZmZddlZddlmmZddlmZddl m Z ddl m Z m Z ddlmZddlmZdd lmZdd lmZmZmZdd lmZmZdd lmZmZdd lmZm Z m!Z!ddl"m#Z#e rvddl$m%Z%ddl&m'Z'e!(e)Z*   d?deej+e,ej+dfdee-deej+deej+e-ffddZ.Gdddej/Z0Gdddej/Z1ddZ2d@dd Z3Gd!d"d"ej/Z4Gd#d$d$ej/Z5Gd%d&d&ej/Z6d'ej+d(e-dej+fd)d*Z7Gd+d,d,ej/Z8 -dAd.ej/d/ej+d0ej+d1ej+deej+d2e9d3e9fd4d5Z:Gd6d7d7eZ;eGd8d9d9eZgd>Z?dS)B)CallableOptionalUnionN)nn)ACT2FN)Cache DynamicCache)GenerationMixin)AttentionMaskConverter)GradientCheckpointingLayer)BaseModelOutputWithPastMoeCausalLMOutputWithPastMoeModelOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)auto_docstringis_torch_flex_attn_availablelogging)GraniteMoeConfig) BlockMask)make_flex_block_causal_mask 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://huggingface.co/papers/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).0 layer_gatecompute_devicer o/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/transformers/models/granitemoe/modeling_granitemoe.py Nz,load_balancing_loss_func..dim) isinstancetupledevicetorchcatr functionalsoftmaxtopkone_hotmeanfloatshapeexpandreshaper!sum unsqueeze)rrtop_krconcatenated_gate_logitsrouting_weights_selected_experts expert_masktokens_per_expertrouter_prob_per_expert batch_sizesequence_lengthnum_hidden_layersexpert_attention_mask router_per_expert_attention_mask overall_lossr r$r&load_balancing_loss_func,s>    rJcs.eZdZdfdd ZddZddZZS) GraniteMoeRMSNormư>cs&ttt||_||_dS)z@ GraniteMoeRMSNorm is equivalent to T5LayerNorm N)super__init__r Parameterr/onesweightvariance_epsilon)self hidden_sizeeps __class__r r&rNs  zGraniteMoeRMSNorm.__init__cCsJ|j}|tj}|djddd}|t||j}|j||S)Nrr+T)keepdim) dtyper!r/float32powr5rsqrtrRrQ)rS hidden_states input_dtypevariancer r r&forwards  zGraniteMoeRMSNorm.forwardcCst|jjd|jS)Nz, eps=)r-rQr7rRrSr r r& extra_reprszGraniteMoeRMSNorm.extra_repr)rL)__name__ __module__ __qualname__rNr`rb __classcell__r r rVr&rKsrKcs8eZdZddeffdd ZeeddZZ S)GraniteMoeRotaryEmbeddingNconfigcstt|dr|jdur|jd|jd|_nd|_|j|_|j|_||_ t |j|_ | |j |\}|_ |j d|dd|j|_dS)N rope_scaling rope_typetypedefaultinv_freqF) persistent)rMrNhasattrrigetrjmax_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrhr rope_init_fnattention_scalingregister_bufferrmoriginal_inv_freq)rSrhr.rmrVr r&rNs   z"GraniteMoeRotaryEmbedding.__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+rmpscpuF) device_typeenabledrr))rY)rmr6r8r7r!r.r,rkstrr/autocast transposer0cosrusinrY) rSx position_idsinv_freq_expandedposition_ids_expandedrzfreqsembrrr r r&r`s0&z!GraniteMoeRotaryEmbedding.forwardN) rcrdrerrNr/no_gradrr`rfr r rVr&rgs rgcCsH|dd|jddf}|d|jdddf}tj| |fddS)z*Rotates half the hidden dims of the input..Nr+rr))r7r/r0)rx1x2r 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)qkrrr unsqueeze_dimq_embedk_embedr r r&apply_rotary_pos_embs  rcs6eZdZdedededdffdd Zdd ZZS) GraniteMoeParallelExpertsr input_size output_sizerNcs6ttt||||_||_||_||_ dS)a Initialize the GraniteMoeParallelExperts 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) rMrNrrOr/emptyrQrrr)rSrrrrVr r&rNs  z"GraniteMoeParallelExperts.__init__cCsP|j|dd}g}t|jD]}|t|||j|qtj|dd}|S)a Forward pass of the GraniteMoeParallelExperts module. Args: inputs (Tensor): Input tensor. expert_size: Expert size information. Returns: Tensor: Output tensor. rr)) splitrangerappendFlinearrQr/r0)rSinputs expert_size input_list output_listiresultsr r r&r`s  z!GraniteMoeParallelExperts.forwardrcrdreintrNr`rfr r rVr&rsrcs2eZdZdededeffdd ZddZZS)GraniteMoeTopKGatingrrr<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)rMrNrrr<rLinearlayer)rSrrr<rVr r&rN s zGraniteMoeTopKGating.__init__cCs||}|j|jdd\}}tj|dd|}tj|d|j g|j |j d}| d|d}| d}|}|} | d\} } | j|jdd} |}|| } | | | ||fS)Nrr)rrYr.trunc) rounding_mode)rr6r3r<r/r2type_aszerossizerrYr.scatterlongr:tolistflattensortdiv)rSr]logits top_k_logits top_k_indices top_k_gatesrgatesr top_k_expertsr?index_sorted_experts batch_index batch_gatesr r r&r`szGraniteMoeTopKGating.forwardrr r rVr&r srcs.eZdZdZdeffdd ZddZZS) GraniteMoeMoEz A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts. Args: config: Configuration object with model hyperparameters. rhcslt|j|_|j|_t|j|_t|j |j|jd|_ t|j |j|j|_ t |j|j |j d|_dS)Nr)rrr<)rMrNrTrintermediate_sizer hidden_act activationrnum_local_experts input_linear output_linearrnum_experts_per_tokrouterrSrhrVr r&rNBs   zGraniteMoeMoE.__init__cCs|\}}}|d|}||\}}}}} ||} || |} | jddd} || d| d} || |} | |dddf} tj|||j f| j | j d}| d|| }| |||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)rr9rrchunkrrr/rrrYr. index_addview)rS layer_inputbszlengthemb_sizer?rrr router_logits expert_inputsr]chunked_hidden_statesexpert_outputsr layer_outputr r r&r`Qs   zGraniteMoeMoE.forward)rcrdre__doc__rrNr`rfr r rVr&r9srr]n_repcCs^|j\}}}}|dkr |S|dddddddddf|||||}||||||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) rN)r7r8r9)r]rbatchnum_key_value_headsslenhead_dimr r r& repeat_kvrs 0rcseZdZdZddedeeffdd Z      ddej d eej d eej d ee d e d eej dee ej ej fde ej eej ee ej ffddZZS)GraniteMoeAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNrh layer_idxcs t||_||_|durtd|jjd|j|_|j |_ |j |_ |j |j |_ |j |_ |j |j |_d|_|j|_|j |j |j krUtd|j d|j dtj|j |j |j |jd|_tj|j |j |j |jd|_tj|j |j |j |jd|_tj|j |j |jd|_dS)NzInstantiating 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.Tz?hidden_size must be divisible by num_heads (got `hidden_size`: z and `num_heads`: z).r)rMrNrhrlogger warning_oncerWrcattention_dropoutrTnum_attention_heads num_headsrrnum_key_value_groups is_causalattention_multiplierscaling ValueErrorrrattention_biasq_projk_projv_projo_projrSrhrrVr r&rNs2  zGraniteMoeAttention.__init__Fr]rrpast_key_value use_cachecache_positionposition_embeddingsrcKsF|\} } } ||} ||} ||}| | | |j|jdd} | | | |j|jdd} || | |j|jdd}|durF|nd\}}|durWt | | ||\} } |durl|||d}| | ||j |\} }t }|j jdkrzt|j j}||| | ||f|jsdn|j|jd|\}}|| | d}||}|||fS) Nrr)NN)rrreager)dropoutrr+)rrrrrrrr~rrupdatereager_attention_forwardrh_attn_implementationrtrainingrrr)rSr]rrrrrrkwargsrq_lenr? query_states key_states value_statesrr cache_kwargsattention_interface attn_output attn_weightsr r r&r`s>         zGraniteMoeAttention.forwardr)NNNFNN)rcrdrerrrrrNr/Tensor LongTensorrboolr-r`rfr r rVr&rs4# rrmodulequerykeyvaluerrc Kst||j}t||j} t||dd|} |dur3|ddddddd|jdf} | | } tjj| dtj d |j } tjj | ||j d} t| | } | dd} | | fS)Nrrr+)r*rY)prr)rrr/matmulr~r7rr1r2rZr!rYrr contiguous) rrrrrrrrrrr causal_maskrr r r&rs & rcseZdZdedeffdd Z        ddejdeejd eej d ee d ee d ee d eej dee dee ejejfde ej ee ej ej fffddZZS)GraniteMoeDecoderLayerrhrcsdt|j|_t||d|_|jdkrt||_t|j|j d|_ t|j|j d|_ |j |_ dS)N)rhrrrU) rMrNrTr self_attnrrblock_sparse_moerK rms_norm_epsinput_layernormpost_attention_layernormresidual_multiplierrrVr r&rNs    zGraniteMoeDecoderLayer.__init__NFr]rrroutput_attentionsrroutput_router_logitsrrc  Ks|} ||}|jd|||||||| d| \}} } | ||j}|} ||}||\}}| ||j}|f}|rA|| f7}|rH|| f7}|rO||f7}|S)a Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, query_sequence_length, key_sequence_length)` if default attention is used. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence output_router_logits (`bool`, *optional*): Whether or not to return the logits of all the routers. They are useful for computing the router loss, and should not be returned during inference. position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, with `head_dim` being the embedding dimension of each attention head. kwargs (`dict`, *optional*): Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code into the model )r]rrrrrrrNr )rrrrr)rSr]rrrrrrrrrresidualself_attn_weightspresent_key_valueroutputsr r r&r`s6&       zGraniteMoeDecoderLayer.forward)NNNFFNFN)rcrdrerrrNr/rrrrrr- FloatTensorr`rfr r rVr&r s>   r c@s@eZdZeZdZdZdgZdgZdZ dZ dZ dZ dZ ddZdS) GraniteMoePreTrainedModelmodelTr past_key_valuesFcCst|tjr |jjjd|jjd|jdur|jj dSdSt|tj rC|jjjd|jjd|j durA|jj|j  dSdSt|t rQ|jj ddSt|trc|jjjd|jjddSdS)Nr)r5stdg?)r,rrrQdatanormal_rhinitializer_rangerzero_ Embedding padding_idxrKfill_r)rSrr r r& _init_weightsYs      z'GraniteMoePreTrainedModel._init_weightsN)rcrdrer config_classbase_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_2_supports_sdpa_supports_cache_class_supports_quantized_cache_supports_static_cacher&r r r r&rLs rcs0eZdZdeffdd ZddZddZe           d"d ee j d ee j d ee j d ee e ee jfd ee jdeedeedeedeedeedee j de eeffddZ d#d e e j dfde j de j d e def ddZed e j dedede jde j def d d!ZZS)$GraniteMoeModelrhcstj|_j|_tjj|j|_t fddt j D|_ t jjd|_d|_j|_j|_j|_|j|j|_j|_j|_j|_|jdkr]tnd|_|dS)Ncsg|]}t|qSr )r )r"rrhr r&r'qr(z,GraniteMoeModel.__init__..r Frope)rMrN pad_token_idr$ vocab_sizerr#rT embed_tokens ModuleListrrFlayersrKrnormgradient_checkpointingembedding_multiplierrrrrq rope_thetaposition_embedding_typerg rotary_emb post_initrrVr2r&rNjs$  zGraniteMoeModel.__init__cC|jSrr6rar r r&get_input_embeddingsz$GraniteMoeModel.get_input_embeddingscC ||_dSrrArSrr r r&set_input_embeddings z$GraniteMoeModel.set_input_embeddingsN input_idsrrr inputs_embedsrroutput_hidden_statesr return_dictrrc  CsH|dur|n|jj}|dur|n|jj}|dur|n|jj}| dur$| n|jj} |du|duAr4td|jrC|jrC|rCt dd}|durL| |}||j }d} |rft |t sfd} t|}t d| dur|durr|nd} tj| | |jd|jd} |dur| d}|||| ||}|}d}|jdur|||}|rd nd}|rd nd}| rd nd}d}|jD]6}|r||f7}|||||||| | |d }|d}|r||rd nd}|r||df7}| r||d f7}q||}|r||f7}|r|nd}| r |}| std d||||fDSt|||||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) fill_valuerYr.r)diagonalrLr+r)r*r/rorpfullr.triurTr9r8cloner7r! masked_fill) rrErfrYrrDrr rs mask_length padding_maskr r r&rnDs,  $ 6  zEGraniteMoeModel._prepare_4d_causal_attention_mask_with_cache_position) NNNNNNNNNNN)F)rcrdrerrNrBrFrrr/rrrrlistrrr-r r`rU staticmethodrrYrnrfr r rVr&r1hs     |  Dr1c s"eZdZdgZdeffdd ZddZddZd d Zd d Z d dZ ddZ e             d%de ejde ejde ejde eeeejfde ejde ejde ede ede ede ede ede ejdeeejfd eeeffd!d"Zed#d$ZZS)&GraniteMoeForCausalLMzlm_head.weightrhcsXt|t||_|j|_tj|j|jdd|_|j |_ |j |_ |j |_ | dS)NFr)rMrNr1rr5rrrTlm_headrouter_aux_loss_coefrrrr?rrVr r&rNs   zGraniteMoeForCausalLM.__init__cCs|jjSrrr6rar r r&rBsz*GraniteMoeForCausalLM.get_input_embeddingscCs ||j_dSrrrEr r r&rFs z*GraniteMoeForCausalLM.set_input_embeddingscCr@rrrar r r&get_output_embeddingsrCz+GraniteMoeForCausalLM.get_output_embeddingscCrDrr)rSnew_embeddingsr r r&set_output_embeddingsrGz+GraniteMoeForCausalLM.set_output_embeddingscCrDrr)rSdecoderr r r& set_decoderrGz!GraniteMoeForCausalLM.set_decodercCr@rrrar r r& get_decoderrCz!GraniteMoeForCausalLM.get_decoderNrrHrrrrIlabelsrrrJrrKrlogits_to_keeprc Ks|dur|n|jj}| dur| n|jj} | dur| n|jj} | dur$| n|jj} |j|||||||| | | | d }|d}t| trGt| dn| }| |dd|ddf}||jj }d}|duru| }|j ||fd|jj i|}d}| rt| r|jn|d|j|j|}|dur||j||j7}| s|f|dd}| r|f|}|dur|f|S|St||||j|j|j|jdS)al 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]`. Example: ```python >>> from transformers import AutoTokenizer, GraniteMoeForCausalLM >>> model = GraniteMoeForCausalLM.from_pretrained("ibm/PowerMoE-3b") >>> tokenizer = AutoTokenizer.from_pretrained("ibm/PowerMoE-3b") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```N) rHrrrrIrrrJrrKrrr5r+r)lossaux_lossrrr]rPr)rhrrrJrQrr,rslicerlogits_scalingr6 loss_functionr5rJrrrrr!r.rrr]rP)rSrHrrrrIrrrrJrrKrrrrr] slice_indicesrrroutputr r r&r`st(  zGraniteMoeForCausalLM.forwardcs.d}|D]}|tfdd|Df7}q|S)Nr c3s$|] }|d|jVqdS)rN) index_selectr!r.)r" past_statebeam_idxr r&rNs"z7GraniteMoeForCausalLM._reorder_cache..)r-)rrreordered_past layer_pastr rr&_reorder_cache s z$GraniteMoeForCausalLM._reorder_cache) NNNNNNNNNNNNr)rcrdre_tied_weights_keysrrNrBrFrrrrrrr/rrrrr}rrrr-rr`r~rrfr r rVr&r}sl         lr)rr1r)NrN)Nr)r)@typingrrrr/torch.nn.functionalrr1r activationsr cache_utilsrr generationr modeling_attn_mask_utilsr modeling_layersr modeling_outputsr rrmodeling_rope_utilsrrmodeling_utilsrrutilsrrrconfiguration_granitemoer!torch.nn.attention.flex_attentionrintegrations.flex_attentionr get_loggerrcrrr-rrJModulerKrgrrrrrrrr6rr rr1r__all__r r r r&s           S# .09] [