o h @sdZddlZddlZddlmZmZmZmZddlZddl m m Z ddl Zddlm Z ddlmZmZmZddlmZddlmZmZdd lmZmZmZmZdd lmZmZmZdd l m!Z!dd l"m#Z#m$Z$dd l%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+ddl,m-Z-ddl.m/Z/e(rddl0m1Z1m2Z2ddl3m4Z4m5Z5m6Z6e-re$sddl7Zej89eZe*:e;ZdHdej?dej@deeAfddZB dIdejCdej@dejDdeAfddZEGd d!d!e jFZGe#HeGGd"d#d#e jFZIGd$d%d%eIZJGd&d'd'eIZKd(d)ZLdJd*d+ZMGd,d-d-e jFZNd.ej?d/eAd0ej?fd1d2ZOGd3d4d4e jFZPGd5d6d6ePZQGd7d8d8ePZRePeQeRd9ZSGd:d;d;e jFZTdd?d?e!ZVd@ZWe&d=eUGdAdBdBeVZXGdCdDdDeVZYe&dEeUGdFdGdGeVZZdS)Kz PyTorch LLaMA model.N)ListOptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)Cache DynamicCache)AttentionMaskConverter_prepare_4d_attention_mask!_prepare_4d_causal_attention_mask*_prepare_4d_causal_attention_mask_for_sdpa)BaseModelOutputWithPastCausalLMOutputWithPast SequenceClassifierOutputWithPast)PreTrainedModel)ALL_LAYERNORM_LAYERS#is_torch_greater_or_equal_than_1_13)add_start_docstrings%add_start_docstrings_to_model_forwardis_flash_attn_2_available#is_flash_attn_greater_or_equal_2_10loggingreplace_return_docstrings)is_torch_fx_available) LlamaConfig)flash_attn_funcflash_attn_varlen_func)index_first_axis pad_input unpad_inputr cCsX|jdtjd}tj|dd}|}ttj |dtjjdd}|||fS)NdimdtypeF)as_tupler)rr) sumtorchint32nonzeroflattenmaxitemFpadcumsum)attention_maskseqlens_in_batchindicesmax_seqlen_in_batch cu_seqlensr:^/var/www/html/ai/venv/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py_get_unpad_dataIs r<maskr)tgt_lencCstdt|||dS)NzCalling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_maskr=r)r>)warningswarnrr?r:r:r; _expand_maskUsrBinput_ids_shapedevicepast_key_values_lengthcCstdtj||||dS)NzCalling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_maskrCr)rDrE)r@rAr_make_causal_maskrFr:r:r;rG\s rGcs&eZdZdfdd ZddZZS) LlamaRMSNormư>cs&ttt||_||_dS)z; LlamaRMSNorm is equivalent to T5LayerNorm N)super__init__r Parameterr,onesweightvariance_epsilon)self hidden_sizeeps __class__r:r;rKhs  zLlamaRMSNorm.__init__cCsJ|j}|tj}|djddd}|t||j}|j||S)Nr&T)keepdim) r)tor,float32powmeanrsqrtrOrN)rP hidden_states input_dtypevariancer:r:r;forwardps  zLlamaRMSNorm.forward)rI__name__ __module__ __qualname__rKr_ __classcell__r:r:rSr;rHgsrHcs0eZdZd fdd ZddZd dd ZZS) LlamaRotaryEmbedding'Ncsrt||_||_||_d|jtd|jd||j}|j d|dd|j ||j j t ddS)N?rrUinv_freqF persistentseq_lenrDr))rJrKr(max_position_embeddingsbaser,arangefloatrWregister_buffer_set_cos_sin_cacherirDget_default_dtype)rPr(rnrorDrirSr:r;rK|s * zLlamaRotaryEmbedding.__init__cCst||_tj|j||jjd}t||j}tj||fdd}|jd| |dd|jd| |dddSNrDr)r&r( cos_cachedFrj sin_cached) max_seq_len_cachedr,rprir)outercatrrcosrWsinrPrmrDr)tfreqsembr:r:r;rss z'LlamaRotaryEmbedding._set_cos_sin_cachecCsN||jkr|j||j|jd|jd|j|jd|jd|j|jdfS)Nrl)r))rzrsrDr)rxrWry)rPxrmr:r:r;r_s zLlamaRotaryEmbedding.forward)rfrgNN)rarbrcrKrsr_rdr:r:rSr;re{s rec*eZdZdZd fdd Zdd ZZS) !LlamaLinearScalingRotaryEmbeddingz[LlamaRotaryEmbedding extended with linear scaling. 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Credits to the Reddit users /u/bloc97 and /u/emozillarfrgNrhcrrrrrSr:r;rKrz.LlamaDynamicNTKScalingRotaryEmbedding.__init__c Cs||_||jkr<|j|j||j|jd|j|jd}d|td|jd||j}|j d|ddtj|j||j j d}t ||j }tj ||fd d }|j d ||dd|j d ||dddS) NrrUrhrriFrjrvr&rwrxry)rzrnrorr(r,rprqrWrrrir)r{r|r}r~) rPrmrDr)rorirrrr:r:r;rss (z8LlamaDynamicNTKScalingRotaryEmbedding._set_cos_sin_cacherrr:r:rSr;rrrcCsH|dd|jddf}|d|jdddf}tj| |fddS)z*Rotates half the hidden dims of the input..Nr&rUrw)shaper,r|)rx1x2r:r:r; rotate_halfsrcCsL|||}|||}||t||}||t||}||fS)anApplies 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`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. 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)qkr}r~ position_ids unsqueeze_dimq_embedk_embedr:r:r;apply_rotary_pos_embs rcs$eZdZfddZddZZS)LlamaMLPcsrt||_|j|_|j|_tj|j|jdd|_tj|j|jdd|_tj|j|jdd|_ t |j |_ dSNFbias) rJrKconfigrQintermediate_sizerLinear gate_projup_proj down_projr hidden_actact_fnrPrrSr:r;rKs zLlamaMLP.__init__cs|jjdkrn|j|jj}|jjj|dd|jjj|dd|jjj|ddtj fddt |jjDdd}tj fddt |jjDdd}| ||j|ddfd dt |jjD}t |}|S|| ||}|S) Nrrrwcg|] }t|qSr:r2linear.0i)gate_proj_slicesrr:r; z$LlamaMLP.forward..r&crr:rr)up_proj_slicesrr:r;rrrUcs g|] }t||qSr:rr)down_proj_slicesintermediate_statesr:r;rs) rpretraining_tprrrNsplitrrr,r|rangerr+)rPrslicerrrr:)rrrrrr;r_s" &   zLlamaMLP.forwardr`r:r:rSr;rs  rr\n_repreturncCs^|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)rexpandreshape)r\rbatchnum_key_value_headsslenhead_dimr:r:r; repeat_kvs 0rcseZdZdZddedeeffdd ZddZd e j d ed efd d Z     dde j dee j dee j dee dededee j ee j eee j ffddZZS)LlamaAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNr layer_idxcs6t||_||_|durtd|jjd|j|_|j |_ |j |_ |j |j |_ |j |_ |j |j |_|j|_|j|_d|_|j |j |j krYtd|j d|j dtj|j |j |j |jd|_tj|j |j |j |jd|_tj|j |j |j |jd|_tj|j |j |j |jd|_|dS)NzInstantiating z without passing `layer_idx` is not recommended and will 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)rJrKrrlogger warning_oncerTraattention_dropoutrQnum_attention_heads num_headsrrnum_key_value_groupsrn rope_theta is_causal ValueErrorrrattention_biasq_projk_projv_projo_proj _init_roperPrrrSr:r;rK s6   zLlamaAttention.__init__cCs|jjdurt|j|j|jd|_dS|jjd}|jjd}|dkr1t|j|j||jd|_dS|dkrCt|j|j||jd|_dSt d|)N)rnrotypefactorr)rnrrodynamiczUnknown RoPE scaling type ) r rope_scalingrerrnr rotary_embrrr)rP scaling_typerr:r:r;rAs.     zLlamaAttention._init_ropetensorrmbszcCs ||||j|jddS)NrrU)viewrr transpose contiguous)rPrrmrr:r:r;_shape\s zLlamaAttention._shapeFr\r5rpast_key_valueoutput_attentions use_cacherc sd|vr td\}} } |jjdkr|j|j|jj} |jjj |j |j|jjdd|j jj | dd|j jj | ddfddt |jjD} tj| dd} fd dt |jjD} tj| dd} fd dt |jjD}tj|dd}n|} | } | }| || |j |jdd } | || |j|jdd } ||| |j|jdd }| jd }|dur|jdurtd |jjd||||j7}|j||d\}}t| | |||\} } |dur||d}|| ||j|\} }t| |j} t||j}t| | d dt|j}|||j | |fkr>td||j | |fd||durb||d| |fkr^td|d| |fd|||}t j!j"|dtj#d$| j%}t j!j&||j'|j(d}t||||j | |jfkrtd||j | |jfddd )*|| |j+|jjdkrj |j+|jjd d|j,jj |j+|jjddt-fddt |jjDn|,|sd}||fS)N padding_maskrPassing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`rrrwcg|] }t|qSr:rr)r\ query_slicesr:r;rxrz*LlamaAttention.forward..r&crr:rr)r\ key_slicesr:r;r{rcrr:rr)r\ value_slicesr:r;r~rrUzFThe cache structure has changed since version v4.36. If you are using zv for auto-regressive decoding with k/v caching, please make sure to initialize the attention class with a layer index.rmr~r}r z$Attention weights should be of size , but is !Attention mask should be of size r')ptrainingz `attn_output` should be of size cs g|] }t||qSr:rr) attn_output o_proj_slicesr:r;rs ).r@rAsizerrrrrrNrrrrrr,r|rrrrrrTraget_usable_lengthrrupdaterrmatmulmathsqrtr functionalsoftmaxrXrWr)dropoutrrrrrQrr+)rPr\r5rrrrkwargsrq_len_key_value_slicing query_states key_states value_states kv_seq_lenr}r~ cache_kwargs attn_weightsr:)rr\rrrrr;r__s              "  zLlamaAttention.forwardrNNNFF)rarbrcrr rintrKrr,Tensorr LongTensorr boolrr_rdr:r:rSr;rs2! rcseZdZdZfddZ     ddejdeejdeejd ee d e d e d e ejeejee ejffd dZ dddZ ddZZS)LlamaFlashAttention2aF Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. cs tj|i|t |_dSr)rJrKr_flash_attn_uses_top_left_mask)rPargsrrSr:r;rKszLlamaFlashAttention2.__init__NFr\r5rrrrrcKsd|vrtd|d}d}|\}} } ||} ||} ||} | || |j|j  dd} | || |j |j  dd} | || |j |j  dd} | j d}|durb|| ||j7}|j| |d\}}t| | |||\} } |dur||d}|| | |j|\} } | dd} | dd} | dd} |jr|jnd }| j}|tjkrt|jd r|jj}n|jjj}td |d | |} | |} | |} |j| | | || |d }||| |j !}|"|}|sd}|||fS)NrrFrrUrrr_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. We will cast back the input in .)r)#r@rApoprrrrrrrrrrrrrrrrrr)r,rXhasattrrrrNrrrW_flash_attention_forwardrrQrr)rPr\r5rrrrrrrrrrrrr}r~r dropout_rater] target_dtyperrr:r:r;r_s\                    zLlamaFlashAttention2.forwardrc Cs|js|j}n|jo |dk}|durE|jd} ||||||\}}}} } } | \} }| \}}t|||| ||||||d }t|| | |}|St||||||d}|S)a Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. Args: query_states (`torch.Tensor`): Input query states to be passed to Flash Attention API key_states (`torch.Tensor`): Input key states to be passed to Flash Attention API value_states (`torch.Tensor`): Input value states to be passed to Flash Attention API attention_mask (`torch.Tensor`): The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the position of padding tokens and 1 for the position of non-padding tokens. dropout (`int`, *optional*): Attention dropout softmax_scale (`float`, *optional*): The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) rNr) cu_seqlens_q cu_seqlens_k max_seqlen_q max_seqlen_k dropout_p softmax_scalecausal)rr)r rr _upad_inputr"r$r!)rPrrrr5 query_lengthrrr batch_size indices_q cu_seq_lens max_seq_lensrrmax_seqlen_in_batch_qmax_seqlen_in_batch_kattn_output_unpadrr:r:r;r0s8    z-LlamaFlashAttention2._flash_attention_forwardcCst|\}}}|j\} } } } t|| | | | |}t|| | | | |}|| kr>t|| | |j| |}|} |}|}n2|dkr\d}tj| dtj|jd} | dd}| d}n|dd| df}t ||\}}} }||||| |f||ffS)Nrr)rDr&) r<rr#rrr,rpr-rDsqueezer%)rP query_layer key_layer value_layerr5r indices_krr$rrrrrr#r r:r:r;rks>  z LlamaFlashAttention2._upad_inputr)rN)rarbrcrrKr,rrr r r rr_rrrdr:r:rSr;r s4   Z ;r csxeZdZdZ     d dejdeejdeejdeede d e d e ejeejee ejfffd d Z Z S)LlamaSdpaAttentionz Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. NFr\r5rrrrrcs|rtdtj||||||dS|\}}} ||} ||} ||} | |||j |j  dd} | |||j |j  dd} | |||j |j  dd} | j d} |dure| || |j7} |j| | d\}}t| | |||\} } |dur||d}|| | |j|\} } t| |j} t| |j} |dur||d|| fkrtd|d|| fd |tjjj| | | ||jr|jnd |jo|duo|dkd }| dd}||||j}| |}|d|fS) NaLlamaModel is using LlamaSdpaAttention, 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.r\r5rrrrrrUrrrrrr) attn_maskrr)!rrrJr_rrrrrrrrrrrrrrrrrrr,rrscaled_dot_product_attentionrrrrrrQr)rPr\r5rrrrrrrrrrrr}r~rrrSr:r;r_s\           zLlamaSdpaAttention.forwardr) rarbrcrr,rrr r r rr_rdr:r:rSr;r,s, r,)eagerflash_attention_2sdpacseZdZdedeffdd Z     ddejdeejd eej d ee ejd ee d ee d e ej ee ej ej fffddZ ZS)LlamaDecoderLayerrrcsXt|j|_t|j||d|_t||_t|j|j d|_ t|j|j d|_ dS)N)rrrR) rJrKrQLLAMA_ATTENTION_CLASSES_attn_implementation self_attnrmlprH rms_norm_epsinput_layernormpost_attention_layernormrrSr:r;rKs  zLlamaDecoderLayer.__init__NFr\r5rrrrrc Ksd|vr td|}||}|jd||||||d|\}} } ||}|}||}||}||}|f} |r@| | f7} |rG| | 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 rrr-Nr:)r@rAr:r7r;r8) rPr\r5rrrrrresidualself_attn_weightspresent_key_valueoutputsr:r:r;r_s6       zLlamaDecoderLayer.forwardr)rarbrcr rrKr,rrr rr  FloatTensorr_rdr:r:rSr;r3s,   r3aJ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`LlamaConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. zSThe bare LLaMA Model outputting raw hidden-states without any specific head on top.c@s6eZdZeZdZdZdgZdZdZ dZ dZ ddZ dS)LlamaPreTrainedModelmodelTr3past_key_valuescCs|jj}t|tjr"|jjjd|d|jdur |jj dSdSt|tj rA|jjjd|d|j durC|jj|j  dSdSdS)Nr)rZstd) rinitializer_range isinstancerrrNdatanormal_rzero_ Embedding padding_idx)rPmodulerDr:r:r; _init_weightsPs    z"LlamaPreTrainedModel._init_weightsN) rarbrcr config_classbase_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_2_supports_sdpa_supports_cache_classrMr:r:r:r;rABs rAaD Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance; - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. 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`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. cseZdZdZdeffdd ZddZddZee  dd e j d e e j d e e j d e ee jde e jde ede ede ede edeeeffddZZS) LlamaModelz Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] Args: config: LlamaConfig rcstj|_j|_tjj|j|_t fddt j D|_ j dk|_j dk|_tjjd|_d|_|dS)Ncsg|]}t|qSr:)r3)rrrr:r;rsz'LlamaModel.__init__..r2r1r4F)rJrK pad_token_idrK vocab_sizerrJrQ embed_tokens ModuleListrnum_hidden_layerslayersr6 _use_sdpa_use_flash_attention_2rHr9normgradient_checkpointing post_initrrSrWr;rKs    zLlamaModel.__init__cC|jSrrZrPr:r:r;get_input_embeddingszLlamaModel.get_input_embeddingscC ||_dSrrdrPvaluer:r:r;set_input_embeddings zLlamaModel.set_input_embeddingsN input_idsr5rrC inputs_embedsrroutput_hidden_states return_dictrc  Cs|dur|n|jj}|dur|n|jj}|dur|n|jj}| dur$| n|jj} |dur4|dur4td|durB|jdd\} } n|durP|jdd\} } ntdd} |rjt|t } | ret |}| | } |dur|duru|j n|j }t j| | | t j|d}|d}|dur||}|jr|durd|vr|nd}n|jr|st|| | f|| }n t|| | f|| }|}|jr|jr|rtdd}|rdnd}|rdnd}d}|jD]@}|r||f7}|jr|jr||j||||||}n |||||||d }|d}|r||rdnd }|r||d f7}q||}|r*||f7}d}|r8| r6|n|}| sHtd d ||||fDSt ||||d S)NzDYou cannot specify both input_ids and inputs_embeds at the same timerUz5You have to specify either input_ids or inputs_embedsrr&zZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...Fr:)r5rrrrrcss|] }|dur|VqdSrr:)rvr:r:r; @sz%LlamaModel.forward..)last_hidden_staterCr\ attentions)!rrroruse_return_dictrrrFr r from_legacy_cacherrDr,rplongrrZr_r^rrrarrrr]_gradient_checkpointing_func__call__r`to_legacy_cachetupler)rPrmr5rrCrnrrrorpr seq_lengthrEuse_legacy_cacherDr\all_hidden_statesall_self_attnsnext_decoder_cache decoder_layer layer_outputs next_cacher:r:r;r_s                  zLlamaModel.forward) NNNNNNNNN)rarbrcrr rKrfrkrLLAMA_INPUTS_DOCSTRINGr,r rrrr@r rrrr_rdr:r:rSr;rVsH    rVcseZdZdgZfddZddZddZdd Zd d Zd d Z ddZ e e e eed          d#dejdeejdeejdeeejdeejdeejdeedeedeedeedeeeffddZ d$dd Zed!d"ZZS)%LlamaForCausalLMzlm_head.weightcs@t|t||_|j|_tj|j|jdd|_| dSr) rJrKrVrBrYrrrQlm_headrbrrSr:r;rKLs   zLlamaForCausalLM.__init__cC|jjSrrBrZrer:r:r;rfUz%LlamaForCausalLM.get_input_embeddingscC ||j_dSrrrir:r:r;rkX z%LlamaForCausalLM.set_input_embeddingscCrcrrrer:r:r;get_output_embeddings[rgz&LlamaForCausalLM.get_output_embeddingscCrhrr)rPnew_embeddingsr:r:r;set_output_embeddings^rlz&LlamaForCausalLM.set_output_embeddingscCrhrrB)rPdecoderr:r:r; set_decoderarlzLlamaForCausalLM.set_decodercCrcrrrer:r:r; get_decoderdrgzLlamaForCausalLM.get_decoder) output_typerNNrmr5rrCrnlabelsrrrorprc  sz|dur|n|jj}| dur| n|jj} | dur| n|jj} |j|||||||| | d } | d|jjdkrZ|jjj|j |jjddfddt |jjD} t j | dd} n|} | } d} |dur| d ddddf}|d ddf}t}|d|jj }|d}||j}|||} | s| f| dd}| dur| f|S|St| | | j| j| jd S) a Args: 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]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, LlamaForCausalLM >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) >>> 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) rmr5rrCrnrrrorprrrwcrr:rrr\lm_head_slicesr:r;rrz,LlamaForCausalLM.forward..r&.losslogitsrCr\rt)rrrorurBrrrNrrYrr,r|rqrrrrWrDrrCr\rt)rPrmr5rrCrnrrrrorpr?rr shift_logits shift_labelsloss_fctoutputr:rr;r_gsR(     zLlamaForCausalLM.forwardc Ksz|durnt|tr|}|j}|}n |ddjd}}d}|durA|jd|jdkrA|dd|jd| df}n||jdkrR|dd|df}|durn|durn||jd|krn|dd| df}|dd} |dur| dur|dd} | |dkd|r| dd|jd df} |dur|durd|i} nd|i} | | ||d|d | S) NrrUrrr&rnrmr)rrCrr5) rFr get_seq_length seen_tokensget_max_lengthrgetrwr4 masked_fill_r) rPrmrCr5rnr cache_length past_lengthmax_cache_lengthr model_inputsr:r:r;prepare_inputs_for_generations@  "  z.LlamaForCausalLM.prepare_inputs_for_generationcs.d}|D]}|tfdd|Df7}q|S)Nr:c3s$|] }|d|jVqdS)rN) index_selectrWrD)r past_statebeam_idxr:r;rrs"z2LlamaForCausalLM._reorder_cache..)r{)rCrreordered_past layer_pastr:rr;_reorder_caches zLlamaForCausalLM._reorder_cache NNNNNNNNNN)NNN)rarbrc_tied_weights_keysrKrfrkrrrrrrrr_CONFIG_FOR_DOCr,r rrrr@r rrr_r staticmethodrrdr:r:rSr;rIs`        \ 8ra The LLaMa Model transformer with a sequence classification head on top (linear layer). [`LlamaForSequenceClassification`] 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. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). cseZdZfddZddZddZee          ddej d e ej d e ej d e e ej d e ej d e ej de ede ede ede edeeeffddZZS)LlamaForSequenceClassificationcs@t||j|_t||_tj|j|jdd|_| dSr) rJrK num_labelsrVrBrrrQscorerbrrSr:r;rKs   z'LlamaForSequenceClassification.__init__cCrrrrer:r:r;rfrz3LlamaForSequenceClassification.get_input_embeddingscCrrrrir:r:r;rk"rz3LlamaForSequenceClassification.set_input_embeddingsNrmr5rrCrnrrrrorprc  Cs| dur| n|jj} |j|||||||| | d } | d} || } |dur+|jd}n|jd}|jjdur>|dkr>td|jjdurGd}n|dur_t||jj dd | j }nd}| tj || j d|f}d}|dur| | j }|jjdur|jdkrd|j_n|jdkr|jtjks|jtj krd |j_nd |j_|jjdkrt}|jdkr|||}n+|||}n%|jjd krt}||d|j|d}n|jjd krt}|||}| s|f| dd}|dur|f|S|St||| j| j| jd S) a labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). N)r5rrCrnrrrorprrz=Cannot handle batch sizes > 1 if no padding token is defined.r&)rD regressionsingle_label_classificationmulti_label_classificationr)rrurBrrrXrr,eqrargmaxrWrDrp problem_typerr)rwr r'rrrrrCr\rt)rPrmr5rrCrnrrrrorptransformer_outputsr\rrsequence_lengths pooled_logitsrrrr:r:r;r_%sr        "       z&LlamaForSequenceClassification.forwardr)rarbrcrKrfrkrrr,r rrrr@r rrrr_rdr:r:rSr;rsL       rr)r)r)[rrr@typingrrrrr,torch.nn.functionalrrr2torch.utils.checkpointtorch.nnrrr activationsr cache_utilsr r modeling_attn_mask_utilsrrrrmodeling_outputsrrrmodeling_utilsr pytorch_utilsrrutilsrrrrrrutils.import_utilsrconfiguration_llamar flash_attnr!r"flash_attn.bert_paddingr#r$r%torch.fxfxwrap get_loggerrarrr<rr)rrBSizerDrGModulerHappendrerrrrrrrr r,r5r3LLAMA_START_DOCSTRINGrArrVrrr:r:r:r;s           $ " ,KPIF$>