U ,-e@stdZddlmZddlmZmZmZmZddlZddl m m Z ddl Zddlm Z ddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZmZmZmZddlm Z ddl!m"Z"ddl#m$Z$e%e&Z'dZ(ddgZ)eGdddeZ*eGdddeZ+dEddZ,ddZ-dFddZ.gfd d!Z/Gd"d#d#e j0Z1Gd$d%d%e j2Z3dGej4ej5ej6e7d&d'd(Z8dHej9ej5ee7d)d*d+Z:Gd,d-d-e j;Zd0d1Z?d2d3Z@Gd4d5d5e j;ZAGd6d7d7e j;ZBGd8d9d9e j;ZCGd:d;d;e j;ZDdd?d?eZFd@ZGed=eEGdAdBdBeFZHGdCdDdDeFZIdS)Iz PyTorch Idefics model.) dataclass)ListOptionalTupleUnionN)nn)CrossEntropyLoss)PreTrainedModel)ACT2FN) ModelOutput)PretrainedConfig)ALL_LAYERNORM_LAYERS)add_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings) IdeficsConfig)IdeficsPerceiverResampler)IdeficsVisionTransformerrzHuggingFaceM4/idefics-9bzHuggingFaceM4/idefics-80bc@s|eZdZUdZdZejed<dZe e e ejed<dZ e e ejed<dZ e e ejed<dZ e e ejed<dS)IdeficsBaseModelOutputWithPasta Base class for Idefics model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): 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)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. 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. 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)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver Nlast_hidden_statepast_key_values hidden_states attentionsimage_hidden_states)__name__ __module__ __qualname____doc__rtorch FloatTensor__annotations__rrrrrrr$r$m/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/idefics/modeling_idefics.pyr9s $rc@seZdZUdZdZeejed<dZ ejed<dZ ee ejed<dZ ee ejed<dZee ejed<dZee ejed<dS) IdeficsCausalLMOutputWithPasta Base class for Idefics causal language model (or autoregressive) outputs. Args: 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 (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): 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)`) 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. 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. 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)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver Nlosslogitsrrrr)rrrr r'rr!r"r#r(rrrrrrr$r$r$r%r&fs  r&FcKsBt|jdddd|d|j}|d|}|dd|d<|dd|d<|dd|d<|dd|d<d|kr|d}|d||d<|dk r|d||d <|ddk r|dd||d<|ddk r|dd||d<nF|ddk r|dd||d<n"|ddk r:|dd||d<||fS) Nrr pixel_valuesimage_encoder_embeddingsperceiver_embeddingsimage_attention_masktoken_type_idsattention_mask) r!arangeshapeviewrepeattodevice index_selectget) input_idsZ expand_sizeis_encoder_decoderr/Zencoder_outputs model_kwargsZexpanded_return_idxr.r$r$r%expand_inputs_for_generations< ,    r;cCsd|kr|j|d<nd|d<d|krT|d}tj||dddfdgdd|d<d|kr|d}tj|||jddfgdd|d<d|kr|d}|dddddfd}||d<|j|d <|S) Nrr.r)dimr/rrr-r)rr!cat unsqueezeZnew_onesr1r)outputsr:r.r/r-Z last_maskr$r$r%"update_model_kwargs_for_generations$ (  rAc Ks|dd}|rD|dddfd}|dk rD|dddfd}|dd}|dd}|dk r|dkr|dd}||dkd|r|dddfd}|dd}|dd}|d d}|d d} |d d } |||d ||||||| | d S)Nr.r)r/ position_idsrrr*r+r,r-interpolate_pos_encodingF use_cache) r8rrDrBr/r.r*r+r,r-rC)r7r?longcumsum masked_fill_) r8rkwargsr.r/rBr*r+r,r-rCr$r$r%prepare_inputs_for_generations:        rIcsftjtjtjdfdd|D}|D]4|rVtfdd|DrVdq,dq,|S)N) LayerNormLinear Embeddingcsg|] }|qSr$r$).0m)mappingr$r% sz freeze_model..c3s|]}t|VqdSN) isinstance)rMt)moduler$r% szfreeze_model..TF)rrJrKrLmodulesanyrequires_grad_)modelmodule_exceptionsZmodule_exceptions_mappedr$)rOrTr% freeze_models   r[csDeZdZdZd eeddfdd ZddZed d d Z Z S) IdeficsDecoupledEmbeddinga Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0, then it will create `num_additional_embeddings` additional parameters that are always trained. If `num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`. FN)partially_freezereturnc  s|dk r$||kr$td|d|tjf|||||d|||_||_||_||_|rj|jd|jdkrt j |j|||d|_ dS)a) Args: num_embeddings (`int`): Size of the dictionary of embeddings num_additional_embeddings (`int`): Number of additional embeddings. Only useful when you `partially_freeze=True`. embedding_dim (`int`): The size of each embedding vector partially_freeze: (`bool`, *optional*, defaults to `False`): If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen. padding_idx (`int`, *optional*): The padding index (needs to be less than num_embeddings) Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`, `max_norm` or `norm_type`. We are not supporting these. Nz/padding_idx must be within num_embeddings. Got z and )num_embeddings embedding_dimr5dtype padding_idxFr)r_r`r5ra) ValueErrorsuper__init__r_rbnum_additional_embeddingsr]weightrXrrLadditional_embedding) selfr_rfr`r]r5rarbrH __class__r$r%res0  z"IdeficsDecoupledEmbedding.__init__cCsj|jdkrt||jS|}t||jk}||}|||j}d||<t||j}|||<|S)a we have 2 embeddings, with different indices - one pretrained self.weight and another self.additional_embedding.weight that is being trained. in order to make a lookup of the input ids, we: 1. find out the indices of the entries belonging to the 2nd embedding 2. extract those values while subtracting the size of the first embedding (num_embeddings), since the 2nd embedding starts from 0 and not num_embeddings 3. perform the 2nd embedding lookup 4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index 5. perform the 1st embedding lookup 6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup note: for the 1st embedding lookup we could have looked up only the low indices and not do the padding, but then we have to create a new tensor and populate it with 2 tensors that are spread out across various indices - i.e. not a simple concat - I haven't benchmarked the complex case if it's any faster, given that seqlens are usually relatively short it's probably not faster or if faster not by much - but might be a good idea to measure. r) rfFZ embeddingrgcloner!wherer_rh)rir8Zadditional_vocab_indicesZinput_ids_additional_vocabZadditional_embeddingsZ full_vectorr$r$r%forwardNs z!IdeficsDecoupledEmbedding.forwardr^cCsd|j|j|j|jS)NzVnum_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={})formatr_rfr`r]rir$r$r% extra_reprus z$IdeficsDecoupledEmbedding.extra_repr)FNNN) rrrr rboolrerostrrs __classcell__r$r$rjr%r\s 5'r\csTeZdZdZdeeeeeddfdd Zejejdd d Z e d d d Z Z S)IdeficsDecoupledLineara Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0, then it will create `out_additional_features * in_features` additional parameters that are always trained. If `out_additional_features=0`, then the module defaults back to the regular behavior of `nn.Linear`. rTN) in_features out_featuresout_additional_featuresbiasr]r^csnt|||||||_||_||_||_|rL|jd|rL|jd|dkrjt j |||||d|_ dS)aG out_additional_features: int. Number of additional trainable dimensions. Only makes sense when `partially_freeze=True`. partially_freeze: bool. If True, the regular `weight` will be frozen and extra parameters (if any) will be trainable. If False, default to the regular behavior of nn.Linear. Fr)rxryr{r5raN) rdrerzr]rxryrgrXr{rrK additional_fc)rirxryrzr{r]r5rarjr$r%res"  zIdeficsDecoupledLinear.__init__)inputr^cCs:t||j|j}|jdkr6||}t||fd}|S)Nrr))rlZlinearrgr{rzr|r!r>)rir}outputZadditional_featuresr$r$r%ros   zIdeficsDecoupledLinear.forwardrpcCs d|j|j|j|jdk |jS)z=Overwriting `nn.Linear.extra_repr` to include new parameters.zYin_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}N)rqrxryrzr{r]rrr$r$r%rssz!IdeficsDecoupledLinear.extra_repr)rTTNN) rrrr intrtrer!Tensorrorursrvr$r$rjr%rw~s  $ rw)input_ids_shaperar5past_key_values_lengthcCs|\}}tj||ft|j|d}tj|d|d}|||d|ddkd||}|dkrtj tj ||||d|gdd}|ddddddf |d|||S)zB Make causal mask used for bi-directional self-attention. r5r)rrrar5r<N) r!fullfinfominr0sizerGr2r4r>zerosexpand)rrar5rbsztgt_lenmaskZ mask_condr$r$r%_make_causal_masks"  r)rrarcCsj|\}}|dk r|n|}|ddddddf|d|||}d|}||tjt|jS)z_ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. Nr?)rrr4Z masked_fillr!rtrr)rrarrZsrc_lenZ expanded_maskZ inverted_maskr$r$r% _expand_masks  *rcs&eZdZdfdd ZddZZS)IdeficsRMSNormư>cs&ttt||_||_dS)z= IdeficsRMSNorm is equivalent to T5LayerNorm N)rdrer Parameterr!onesrgvariance_epsilon)ri hidden_sizeepsrjr$r%res zIdeficsRMSNorm.__init__cCs\|tjdjddd}|t||j}|jjtj tj fkrR||jj}|j|S)Nr)T)Zkeepdim) r4r!Zfloat32powmeanZrsqrtrrgraZfloat16Zbfloat16)rirZvariancer$r$r%ros zIdeficsRMSNorm.forward)rrrrrerorvr$r$rjr%rsrcs(eZdZdfdd Zd ddZZS) IdeficsEmbedding'Nc std|td|d||}|d|||_tj|j|jj |jj d}t d||j}tj ||fdd}|jd | ddddddfd d |jd |ddddddfd d dS) Nrrrinv_freqr5rai,j->ijr)r< cos_cachedF persistent sin_cached)rdrer!r0floatr4register_buffermax_seq_len_cachedrr5raeinsumr>cossin) rir=Zmax_position_embeddingsbaser5rrSfreqsembrjr$r%res $ (zIdeficsEmbedding.__init__cCs||jkr||_tj|j|j|jjd}td||j}tj||fdd|j}|j d| ddddddfdd|j d| ddddddfdd|j ddddd|d fj|jd |j ddddd|d fj|jd fS) Nrrr)r<rFrr.)ra)rr!r0r5rrarr>r4rrrrr)rixseq_lenrSrrr$r$r%ros ((&&zIdeficsEmbedding.forward)rrN)Nrr$r$rjr%rsrcCsH|dd|jddf}|d|jdddf}tj| |fddS)z*Rotates half the hidden dims of the input..Nr)rr<)r1r!r>)rx1Zx2r$r$r% rotate_halfsrcCs|ddddddf}|d|jdd|jd}t||jddddd|}t||jddddd|}||t||}||t||}||fS)Nrr rr)r3r1r!gatherr)qkrrrBZgather_indicesZq_embedZk_embedr$r$r%apply_rotary_pos_embs  rcs.eZdZeeedfdd ZddZZS) IdeficsMLPrintermediate_size hidden_actcsNttj||dd|_tj||dd|_tj||dd|_t||_dS)NFr{) rdrerrK gate_proj down_projup_projr act_fn)rirrrrjr$r%re+s  zIdeficsMLP.__init__cCs ||||||SrQ)rrrr)rirr$r$r%ro7szIdeficsMLP.forward)rrrrrurerorvr$r$rjr%r*s  rcseZdZdZdeeeeeedfdd Ze j eedd d Z de j e e j e e j e e j e ee j eeee j e e j e ee j fd d d ZZS)IdeficsAttentionz=Multi-headed attention from 'Attention Is All You Need' paperFN)r num_headsdropoutis_cross_attentionconfigqk_layer_normscs|t||_||_|||_||_|j||jkrNtd|jd|d||_tt j dshtd|jrt|j ds|jn|j j }t j |j||jdd|_t j |||jdd|_t j |||jdd|_nNt j |j||jdd|_t j |j||jdd|_t j |j||jdd|_t j ||j|dd|_t|j|_||_|jrxt|j|jd |_t|j|jd |_dS) Nz?hidden_size must be divisible by num_heads (got `hidden_size`: z and `num_heads`: z).scaled_dot_product_attentionz)this model requires pytorch 2.0 or higher embed_dimFrr)rdrerrhead_dimrrcrhasattrr functional vision_configrrKq_projk_projv_projo_projr rotary_embrr rms_norm_eps q_layer_norm k_layer_norm)rirrrrrrZ kv_input_dimrjr$r%re?sf     zIdeficsAttention.__init__)tensorrrcCs ||||j|jddS)Nrr)r2rr transpose contiguous)rirrrr$r$r%_shapeszIdeficsAttention._shape)rkey_value_statesr/rBpast_key_valueoutput_attentionsrDr^cCsn|jp |dk }|\} } } ||| | |j|jdd} |s||| | |j|jdd} ||| | |j|jdd}nR|\} }} ||| ||j|jdd} ||| ||j|jdd}| j d}|dk r||dj d7}|s0|j |t || d\}}t | | |||\} } |dk rft j|d| gdd} t j|d|gdd}|rt| |fnd}|jr|| } || } |dk r|| d| |fkrtd| d| |fd|tjj| | |||jd }|| |j| |jfkr*td | |j| |jfd||dd}|| | |j}||}d}|rdtd |||fS) Nrrr)rr<z!Attention mask should be of size z , but is )Z attn_maskZ dropout_pz `attn_output` should be of size z^attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead)rrrr2rrrrrr1rmaxrr!r>rrrrcrrrrZreshaperrlogger warning_once)rirrr/rBrrrDrrZq_len_Z query_statesZ key_statesZ value_statesZkv_lenZ kv_seq_lenrrZ attn_outputZ attn_weightsr$r$r%ros` ""$"        zIdeficsAttention.forward)rFNF)NNNNFF)rrrr rrrtr rer!rrr LongTensorrrorvr$r$rjr%r<s<E rc sxeZdZedfdd Zd ejeejeejee ejee ee e ej ee ej ej ffdddZ Z S) IdeficsDecoderLayerrcspt|j|_t|j|j|j|d|_t|j|j|j d|_ t |j|j d|_ t |j|j d|_|j|_dS)N)rrrrrr)rdrerrnum_attention_headsr self_attnrrrmlprrinput_layernormpost_attention_layernormrirrjr$r%res  zIdeficsDecoderLayer.__init__NF)rr/rBrrrDr^c Cs|}||}|j||||||d\}}} tjj||j|jd}||}|}||}||}tjj||j|jd}||}|f} |r| |f7} |r| | 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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative 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. 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 )rr/rBrrrDptraining)rrrrrrrr) rirr/rBrrrDresidualself_attn_weightspresent_key_valuer@r$r$r%ros.      zIdeficsDecoderLayer.forward)NNNFF)rrrrrer!rrrrrtr"rorvr$r$rjr%rs rcseZdZedfdd Zd ejeejeejeejeeeeee ejeee ej ee ej ej ffd ddZ Z S) IdeficsGatedCrossAttentionLayerrcst|j|_t|j|jd|j||jd|_t|j|j |j d|_ t |j|j d|_t |j|j d|_|j|_t|_t|_|jdkr|jdkrttdd|j|_ttdd|j|_nB|jdkrttd|_ttd|_ntd |jd nV|jd kr|jdkr^ttdd|j|_ttdd|j|_nD|jdkrttd|_ttd|_ntd |jd n|jd krX|jdkrttjd |jdd|jfd|_ttjd |jdd|jfd|_nT|jdkrDttjd |jdd|_ttjd |jdd|_ntd |jd nt d|jdt!|drt!|dstddS)NT)rrrrrrrrrZvectorrrz Unknown value for `alpha_type` ()r>gaussianrandomnormalr)rstdrzAlpha initialization scheme z not yet implemented!alpha_cross_attn alpha_densez+Alpha parameters not initialized correctly!)"rdrerrrrr cross_attnrrrrrrrrrrZTanhact_cross_attn act_denseZalpha_initializerZ alpha_typerr!rrrrcrrZalphas_initializer_rangeNotImplementedErrorrrrjr$r%re$sj            z(IdeficsGatedCrossAttentionLayer.__init__NF) rr/rr-rrDr no_imagesr^c Cs|dkrtd|dk r td|} ||}|j||||d\}} } tjj||j|jd}|rddnd} | | | |j |}|} | |}| |}tjj||j|jd}| | |j|}|f} |r| | f7} |r| | 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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative 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. 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 no_images (`bool`, *optional*, defaults to `False`): If `True` the vision part is ignored Nzt`image_hidden_states` is required for Idefics cross attention module which are visual features to be conditioned on.zMPast key value states are not implemented for Idefics cross attention module.)rrr/rrrr)rcrrrrrrrrrrrrrr)rirr/rr-rrDrrrrrZgater@r$r$r%roes8       z'IdeficsGatedCrossAttentionLayer.forward)NNNFFNF) rrrrrer!rrrtrr"rorvr$r$rjr%r#s&D raL 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 ([`IdeficsConfig`]): 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@s2eZdZeZdZdZddgZddZd dd Z d S) IdeficsPreTrainedModelrYTrrcCs||jj}t|tjr>|jjjd|d|jdk rx|jj n:t|tj rx|jjjd|d|j dk rx|jj|j  dS)Nr)rr) rZinitializer_rangerRrrKrgdataZnormal_r{Zzero_rLrb)rirTrr$r$r% _init_weightss    z$IdeficsPreTrainedModel._init_weightsFcCst|tr||_dSrQ)rR IdeficsModelgradient_checkpointing)rirTvaluer$r$r%_set_gradient_checkpointings z2IdeficsPreTrainedModel._set_gradient_checkpointingN)F) rrrr config_classZbase_model_prefixZsupports_gradient_checkpointingZ_no_split_modulesrrr$r$r$r%rs raI 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 `decoder_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 (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): 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)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_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. cseZdZdZedfdd ZdddZgfdd Zgfd d Zd d Z ddZ ddZ e e dejeejeejeeejeejeejeejeejeejeeeeeeeeeeeeefdddZZS)rz Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`] Args: config: IdeficsConfig rcst|_j|_j|_tjjjj |jd|_ j j |_ j |_ t j |_jrj}tj j|j|j|j|j|_tfddtjD|_j|_j|j}tfddt|D|_d|_t jj!d|_"d|_|#|$dS)N)r_rfr`r]rbcsg|] }tqSr$)rrMrrr$r%rP?sz)IdeficsModel.__init__..csg|] }tqSr$)rrrr$r%rPDsFr)%rdrerZ pad_token_idrb vocab_sizer\additional_vocab_sizerfreeze_text_layers embed_tokensrZ image_sizer vision_model use_resamplerperceiver_configrrZresampler_depthZresampler_n_headsZresampler_head_dimZresampler_n_latentsperceiver_resamplerrZ ModuleListrangeZnum_hidden_layerslayerscross_layer_intervalgated_cross_attn_layersrrrnorm post_initfreeze_relevant_params)rirr Znum_cross_layersrjrr%re!sF     zIdeficsModel.__init__NcCs:|dkr|j}|jr ||j|jr6t|j|jddSN)rZ)rr Zfreeze_text_module_exceptionsfreeze_vision_layersr[r Zfreeze_vision_module_exceptionsrr$r$r%rPs  z#IdeficsModel.freeze_relevant_paramscCs"|j|jfD]}t||dq dSr)rrr[)rirZrTr$r$r%r ZszIdeficsModel.freeze_text_layerscCst|j|ddSr)r[r )rirZr$r$r%r^sz!IdeficsModel.freeze_vision_layerscCs|jSrQr rrr$r$r%get_input_embeddingsasz!IdeficsModel.get_input_embeddingscCs ||_dSrQrrirr$r$r%set_input_embeddingsdsz!IdeficsModel.set_input_embeddingscCs`d}|ddkr$t||j|j|d}|dk r\t||j|dd|j}|dkrT|n||}|S)Nr)r)r5r)r)rrar5rr4)rir/Z input_shape inputs_embedsrZcombined_attention_maskZexpanded_attn_maskr$r$r%_prepare_decoder_attention_maskhs z,IdeficsModel._prepare_decoder_attention_maskF)r8r/rBrrr*r+r,r-rDroutput_hidden_statesrC return_dictr^c(CsJ|dk r|jn|j}| dk r | n|jj} | dk r4| n|jj} | dk rH| n|jj} |dk r\|n|jj}|dk r~|dk r~tdn2|dk r|j\}}n|dk r|j\}}}ntd|}d}|dk r|ddjd}||}|dk r|dkr| dd}| |dkdn\|dkr^|dk r,|jn|j}t j |||t j|d}| dd|}n|d|}d}|||fddkrtd n|dk rtt |dk}|j|j|d}|jdd\}}|j||f|jdd}|j|| d j}n>|dk rB|\}}}}|j|j|jd}|||||}|jjr|dkrx||}|d|d}}n|\}}}}|}n*|dkr|d|d}}ntd |||||}| d}| d} | ddd|} | ||||} |dk rD|\}}}||f}| dkr8t j||d } || } nd} |dkr\||}|dkr~t j||ft j|jd}||||f||}|}|j r|j!r| rt"#d d} | rdnd}| rdnd} | rdnd}!t$|j%D]\}"}#| r||f7}|dk r||"nd}$dd}%|j rz|j!rzd}$| rLt"#d d} t j&j''|%|#||||$|| | | ||"|j(|j)}&n&|%|#||||$|| | | ||"|j(|j)d }&|&d}| r|!|&| rdndf7}!| r| |&df7} q|*|}| r||f7}| r|!nd}'|||||}|s8t+dd||'|| |fDSt,||'|| |dS)NzTYou cannot specify both decoder_input_ids and decoder_inputs_embeds at the same timezEYou have to specify either decoder_input_ids or decoder_inputs_embedsrrr)rrFz_Exactly 1 of pixel_values, image_encoder_embeddings or perceiver_embeddings has to be not-None.)r*rCzBIf `perceiver_embeddings` are passed, use_resampler should be TruerzZ`use_cache=True` is incompatible with gradient checkpointing. 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Ztie_word_embeddingsTrryr_rzrfN) r1rgetattrrrgrfrzAssertionErrorrhr|rr_ry)riZoutput_embeddingsZinput_embeddingsr$r$r% tie_weightss   z%IdeficsForVisionText2Text.tie_weights) output_typerF)r8r/rBrrr*r+r,r-labelsrDrrrCrr^cCsx| dk r | n|jj} | dk r | n|jj} |dk r4|n|jj}|j||||||||| | | | ||d}|d}||}d}| dk r,|dk r|dddf}|dddddf|dk}| dddf|dk}n.|dddddf}| dddf}t}||d| d|d}|s\|f|dd}|dk rX|f|S|St |||j |j |j |jdS)a Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, IdeficsForVisionText2Text >>> model = IdeficsForVisionText2Text.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) >>> prompt = "Hey, are you consciours? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." ```N)r8r/rBrrr*r+r,r-rDrrrCrr.rr))r'r(rrrr)rrrr$rYr.rrr2rr&rrrr)rir8r/rBrrr*r+r,r-r;rDrrrCrr@rr(r'Zshift_attention_maskZ shift_logitsZ shift_labelsZloss_fctr~r$r$r%rosV.  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