o Zha@sddlZddlZddlZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZddlZddlZddlmZddlmZddlmZddlmZddlmZdd lmZmZm Z m!Z!m"Z"m#Z#m$Z$dd l%m&Z&dd l'm(Z(m)Z)m*Z*m+Z+dd l,m-Z-dd l.m/Z/ddl0m1Z1m2Z2ddl3m4Z4ddl5m6Z6ddl7m8Z8m9Z9m:Z:m;Z;mm?Z?m@Z@ddlAmBZBmCZCmDZDddlEmFZFmGZGmHZHmIZImJZJmKZKmLZLmMZMmNZNmOZOddl%mPZPmQZQmRZRmSZSddlTmUZUmVZVmWZWmXZXmYZYmZZZm[Z[m\Z\m]Z]m^Z^m_Z_m`Z`maZambZbmcZcmdZdmeZemfZfmgZgmhZhmiZimjZjmkZkmlZlmmZmmnZnddlompZpmqZqmrZrmsZsmtZtmuZumvZver9ddlwmxZxddlymzZzddl{m|Z|e=}e~Ze9rJddlmZmZgdZeGddde8ZeGd d!d!e8ZeGd"d#d#e8ZeGd$d%d%e8ZeZeZeZeZeZeZeZeZeZeZeeefZeeefZeeefZeeefZeeefZeeefZeeefZeeefZGd&d'd'Zd(d)ZdPd+d,Zd-ejd.ejd/ejd0ejd1ed2ed3ejfd4d5Zd6ed7efd8d9Zd:ee8e fd7ed6ed;e&d3e ee8e ff de e8d;e&d3e8fd?d@ZdAedB dCdfdDejdEejdFedGedHed3ejf dIdJZdKe edLe eejfdMejd3ejfdNdOZdS)QN) dataclass) TYPE_CHECKINGAnyCallableDictListOptionalTupleUnion) file_exists)version)nn) functional)Cache DynamicCacheEncoderDecoderCacheHybridChunkedCacheOffloadedCacheOffloadedHybridCacheQuantizedCacheConfig)PretrainedConfig)check_python_requirementsget_cached_module_fileget_class_in_moduleresolve_trust_remote_code)is_deepspeed_zero3_enabled)is_fsdp_managed_module)CausalLMOutputWithPastSeq2SeqLMOutput)isin_mps_friendly)ExtensionsTrie) ModelOutputis_accelerate_availableis_hqq_availableis_optimum_quanto_availableis_torchdynamo_exportinglogging)DisjunctiveConstraintPhrasalConstraint) BeamScorerBeamSearchScorerConstrainedBeamSearchScorer) AssistantVocabTranslatorCacheAssistedCandidateGenerator-AssistedCandidateGeneratorDifferentTokenizersCandidateGeneratorEarlyExitCandidateGeneratorPromptLookupCandidateGenerator%UniversalSpeculativeDecodingGenerator_crop_past_key_values_prepare_attention_mask_prepare_token_type_ids) NEED_SETUP_CACHE_CLASSES_MAPPINGQUANT_BACKEND_CLASSES_MAPPINGGenerationConfigGenerationMode)#EncoderNoRepeatNGramLogitsProcessor'EncoderRepetitionPenaltyLogitsProcessorEpsilonLogitsWarperEtaLogitsWarperExponentialDecayLengthPenaltyForcedBOSTokenLogitsProcessorForcedEOSTokenLogitsProcessorHammingDiversityLogitsProcessorInfNanRemoveLogitsProcessorLogitNormalizationLogitsProcessorListMinLengthLogitsProcessor!MinNewTokensLengthLogitsProcessorMinPLogitsWarperNoBadWordsLogitsProcessorNoRepeatNGramLogitsProcessor PrefixConstrainedLogitsProcessor RepetitionPenaltyLogitsProcessorSequenceBiasLogitsProcessor$SuppressTokensAtBeginLogitsProcessorSuppressTokensLogitsProcessorTemperatureLogitsWarperTopKLogitsWarperTopPLogitsWarperTypicalLogitsWarper.UnbatchedClassifierFreeGuidanceLogitsProcessor)ConfidenceCriteriaEosTokenCriteriaMaxLengthCriteriaMaxTimeCriteriaStoppingCriteriaStoppingCriteriaListStopStringCriteria)PreTrainedModel)PreTrainedTokenizerBase) BaseStreamer)AlignDevicesHookadd_hook_to_module)past_key_values cache_paramsstatememspast_buckets_statesc@seZdZUdZejed<dZee ej ed<dZ ee ej ed<dZ ee e ej ed<dZ ee e ej ed<dZee e e ej ed<dS) GenerateDecoderOnlyOutputaw Outputs of decoder-only generation models, when using non-beam methods. Args: sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`): The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`. scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True`): Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size, config.vocab_size)`. logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`): Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size, config.vocab_size)`. attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`): Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`. hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`): Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`. past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True`): Returns the model cache, used to speed up decoding. Different models have a different cache format, check the model's documentation. Usually, a [`~cache_utils.Cache`] instance. sequencesNscoreslogits attentions hidden_statesrb)__name__ __module__ __qualname____doc__torch LongTensor__annotations__rirr FloatTensorrjrkrlrbruruL/var/www/auris/lib/python3.10/site-packages/transformers/generation/utils.pyrgs  "rgc@seZdZUdZejed<dZee ej ed<dZ ee ej ed<dZ ee ej ed<dZ ee ej ed<dZee e ej ed<dZee e ej ed <dZee e ej ed <dZee e e ej ed <dS) GenerateEncoderDecoderOutputaI Outputs of encoder-decoder generation models, when using non-beam methods. Args: sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`): The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`. scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True`): Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size, config.vocab_size)`. logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`): Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size, config.vocab_size)`. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`): Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`. cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`): Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`. decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`): Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`. past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Returns the model cache, used to speed up decoding. Different models have a different cache format, check the model's documentation. Usually, a [`~cache_utils.Cache`] instance. rhNrirjencoder_attentionsencoder_hidden_statesdecoder_attentionscross_attentionsdecoder_hidden_statesrb)rmrnrorprqrrrsrirr rtrjrxryrzr{r|rbrurururvrws  #"rwc@seZdZUdZejed<dZeej ed<dZ ee ej ed<dZ ee ej ed<dZ eejed<dZee e ej ed<dZee e ej ed <dZee e e ej ed <dS) GenerateBeamDecoderOnlyOutputa Outputs of decoder-only generation models, when using beam methods. Args: sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`): The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`. sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True`): Final beam scores of the generated `sequences`. scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True`): Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`. logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`): Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`. beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True`): Beam indices of generated token id at each generation step. `torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`. attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`): Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`. hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`): Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`. past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True`): Returns the model cache, used to speed up decoding. Different models have a different cache format, check the model's documentation. Usually, a [`~cache_utils.Cache`] instance. rhNsequences_scoresrirj beam_indicesrkrlrb)rmrnrorprqrrrsr~rrtrir rjrrkrlrbrurururvr}s  "r}c@seZdZUdZejed<dZeej ed<dZ ee ej ed<dZ ee ej ed<dZ eejed<dZee ej ed<dZee ej ed <dZee e ej ed <dZee e ej ed <dZee e ej ed <dZee e e ej ed <dS) GenerateBeamEncoderDecoderOutputa Outputs of encoder-decoder generation models, when using beam methods. Args: sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`): The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`. sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True`): Final beam scores of the generated `sequences`. scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True`): Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`. logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`): Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`. beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True`): Beam indices of generated token id at each generation step. `torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)`. decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`): Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, num_heads, generated_length, sequence_length)`. cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`): Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`. decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`): Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`. past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True`): Returns the model cache, used to speed up decoding. Different models have a different cache format, check the model's documentation. Usually, a [`~cache_utils.Cache`] instance. rhNr~rirjrrxryrzr{r|rb)rmrnrorprqrrrsr~rrtrir rjrrxryrzr{r|rbrurururvr s  *"rc"@seZdZdZ  ddeeeejfdee de fddZ de j d ee jd ee j dee je j ffd d Zde j d ee jd ee j dee je j ffd dZ    dde j deedee j d ee jd ee j f ddZ   ddee jdee jdeeee jfdee jeeeee jfffddZ   ddee jdee jdeeee jfde j fddZde jdedeeefde j fddZde jdeededeeeffdd Z dd!ededeee jfd"e jd#ee jdee j eee jfff d$d%Ze & ' dd(ed)e dee j dee j eeefffd*d+Z ' &dd,e!deeefd)e d-edeeeff d.d/Z"d0d1Z#dede j de jd2d3d4e$d5d6d7d6dede%fd8d9Z&    dded:ed;e j dee jde$fd?d@Z( ddedAee)dBed6de)fdCdDZ*dEee$e)fdFee$e)fdee$e)ffdGdHZ+  'ddIe jdJee jdKee jdLe de jf dMdNZ,dOdPZ-deeeffdQdRZ.dSdTZ/dUdVZ0 ddeedWee dXedeeeffdYdZZ1d[d\Z2deeeeeefffd]d^Z3d_ed!ed`ed#e jdef dadbZ4de fdcddZ5deded2d3d!edeed#e jde fdfdgZ6de fdhdiZ7  ddedjee d#eee jeffdkdlZ8dedede fdmdnZ9e :            ddee jdeed4ee$dAee)dee jdWee dreedee;e j ffdsdtZde j dzeee'efd4e$dAe)dedoe dpdqdee?e j ffd{d|Z@e :de j d4e$dAe)dedoe dpedqdee?e j ffd}d~ZAde j d4e$dAe)dedoe dpedqdee?e j ffddZBddZCede jde jfddZDede jd!edede jfddZEede jdKe jde jfddZFede jde jde jde jdedededee efdeGfddZHde jde jde jdedede dededed!edee je je jffddZIde jde jde jde jdedee je je jff ddZJdIe jde jde jde jdKe jde jde jde jde jdedededeGdee efdee je je je jffddZKde j d4e$dAe)dedoe deeLe j ff ddZMde j deNd4e$dAe)dedoe f ddZOde j dePd4e$dAe)dedoe deeLe j ffddZQde j de%d4e$dAe)dedoe dpedqdee?e j ffddZRde j defddZSdS)GenerationMixina A class containing all functions for auto-regressive text generation, to be used as a mixin in model classes. Inheriting from this class causes the model to have special generation-related behavior, such as loading a `GenerationConfig` at initialization time or ensuring `generate`-related tests are run in `transformers` CI. A model class should inherit from `GenerationMixin` to enable calling methods like `generate`, or when it has defined a custom `generate` method that relies on `GenerationMixin`, directly or indirectly, which approximately shares the same interface to public methods like `generate`. Three examples: - `LlamaForCausalLM` should inherit from `GenerationMixin` to enable calling `generate` and other public methods in the mixin; - `BlipForQuestionAnswering` has a custom `generate` method that approximately shares the same interface as `GenerationMixin.generate` (it has a few extra arguments, and the same output). That function also calls `GenerationMixin.generate` indirectly, through an inner model. As such, `BlipForQuestionAnswering` should inherit from `GenerationMixin` to benefit from all generation-related automation in our codebase; - `BarkModel` has a custom `generate` method and one of its inner models calls `GenerationMixin.generate`. However, its `generate` does not share the same interface as `GenerationMixin.generate`. In this case, `BarkModel` should NOT inherit from `GenerationMixin`, as it breaks the `generate` interface. The class exposes [`~generation.GenerationMixin.generate`], which can be used for: - *greedy decoding* if `num_beams=1` and `do_sample=False` - *contrastive search* if `penalty_alpha>0` and `top_k>1` - *multinomial sampling* if `num_beams=1` and `do_sample=True` - *beam-search decoding* if `num_beams>1` and `do_sample=False` - *beam-search multinomial sampling* if `num_beams>1` and `do_sample=True` - *diverse beam-search decoding* if `num_beams>1` and `num_beam_groups>1` - *constrained beam-search decoding* if `constraints!=None` or `force_words_ids!=None` - *assisted decoding* if `assistant_model` or `prompt_lookup_num_tokens` is passed to `.generate()` To learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies). Npretrained_model_name_or_pathtrust_remote_codereturnc Kstj|}d}|rtjtj|dsd}nt|dsd}|s)td|dd|d}t|||| |dt|fd d i|t|fd di|}t d |}|S) at Loads and returns a custom generate function, given a model repo. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. - A path to a *directory* containing model weights saved using [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. trust_remote_code (`bool`, *optional*): Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. **kwargs: Additional keyword arguments for remote code loading. Raises: OSError: If `pretrained_model_name_or_path` does not contain a `custom_generate` subdirectory. Returns: A callable that can be used to generate text. Tzcustom_generate/generate.pyF`zw` does not contain a `custom_generate` subdirectory with a `generate.py` file, can't load the custom generate function.zThe repository `zS` contains custom generation code that will override the default `generate` method.)Zhas_local_codeZhas_remote_code error_messageZrequirements_filez custom_generate/requirements.txtZ module_filegenerate) ospathexistsjoinr OSErrorrrrr) selfrrkwargsZ is_local_codeZhas_custom_generate_folderrmodulecustom_generate_functionrururvload_custom_generatesJ      z$GenerationMixin.load_custom_generate input_ids inputs_embedscache_positioncCstr ||||S|dur'|jddkr'|dd|jd df}||fS|dus4|d|jdkrF|dd|jd df}||fS|jd|jdkrX|dd|f}||fS)a Generic cache-dependent input preparation The code is put in a separate function to allow granular unit testing as it needs a different implementation to be exportable. If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens - Exception 1: when passing input_embeds, input_ids may be missing entries - Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here - Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case. - Exception 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and generate the first token for each sequence. Later use the generated Input ids for continuation. The current implementation does not rely on ``self`` and could be a class method. It is left as a standard method to be easily rewritten. Nr(r)r&,_cache_dependant_input_preparation_exportingshaperrrrrururv"_cache_dependant_input_preparationsz2GenerationMixin._cache_dependant_input_preparationcsv|dur|dd|f}||fSddddddt|jdd kfd d fd d |||g\}}||fS) z This method implements method ``_cache_dependant_input_preparation`` with :func:`torch.cond` to make it exportable with :func:`torch.export.export`. The code is put in a separate function to allow granular unit testing. NcS|dd|jd dfSNrr)rrrururvbranch_1zNGenerationMixin._cache_dependant_input_preparation_exporting..branch_1cSrrrrrrururvbranch_2rzNGenerationMixin._cache_dependant_input_preparation_exporting..branch_2cSs|dd|fSNrurrururvbranch_3szNGenerationMixin._cache_dependant_input_preparation_exporting..branch_3r(rcs|||fSrrurrr)rrurv szNGenerationMixin._cache_dependant_input_preparation_exporting..cs.|t|d|jdkfdd||gfS)Nrr(cs(t|jd|jdkdd||gS)Nr(rcSs|SrrurrururvrszrGenerationMixin._cache_dependant_input_preparation_exporting......rqZcondrr)rrurvrs z`GenerationMixin._cache_dependant_input_preparation_exporting....rr)rrrurvrs rrru)rrrrvrs 0   z&z9GenerationMixin._prepare_model_inputs..Nz `inputs`: z` were passed alongside z0 which is not allowed. Make sure to either pass z or z=...rrzAYou passed `inputs_embeds` to `.generate()`, but the model class z doesn't have its forwarding implemented. 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When generating with stop strings, you must pass the model's tokenizer to the `tokenizer` argument of `generate`.) stop_stringsrS)rr)assistant_confidence_threshold)r[r-rrrIrXrUrYrVrr\rrWZ is_assistantrWrVrO)rrrRrSrcriteriarTrururv_get_stopping_criteria(s8        z&GenerationMixin._get_stopping_criteria default_list custom_listc Cst|dkr|St|}|D]B}d}|D]4}t|t|urIt|tr&dnd}td|dt|dt|dt|d ||d }nq|sQ||q|D] }||vr_||qT|S) a4 Merge user-defined processors/criteria with the ones instantiated inside `generate`. In case the same processor/criteria is present on both lists, use the user-defined one. (Note: up to v4.49.0, this function threw an exception is the same logit processor was found twice.) rFzstopping criteriazlogits processorz A custom z of type zt has been passed to `.generate()`, but it was also created in `.generate()`, given its parameterization. The custom z5 will take precedence. Please check the docstring of z$ to see related `.generate()` flags.T)rr3rrZrrrI)rrZr[Z final_listdefaultZ using_customZcustomZ object_typerururvrOOs6     z.GenerationMixin._merge_criteria_processor_listrhrirnormalize_logitsc Cs>|durt|djddd|j}|dt|}t| t|d dd}|rO| d|j j |jd}tjjj|dd}| d|jd}|dk}d|d}|ddd|f}|ddd|f}d||<||j j }|jd|}|dd|df|} |d| } d| |<| S)a Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used). This is a convenient method to quickly obtain the scores of the selected tokens at generation time. Parameters: sequences (`torch.LongTensor`): The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`. scores (`tuple(torch.FloatTensor)`): Transition scores for each vocabulary token at each generation step. Beam transition scores consisting of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`. beam_indices (`torch.LongTensor`, *optional*): Beam indices of generated token id at each generation step. `torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`. Only required if a `num_beams>1` at generate-time. normalize_logits (`bool`, *optional*, defaults to `False`): Whether to normalize the logits (which, for legacy reasons, may be unnormalized). Return: `torch.Tensor`: A `torch.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)` containing the transition scores (logits) Examples: ```python >>> from transformers import GPT2Tokenizer, AutoModelForCausalLM >>> import numpy as np >>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2") >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2") >>> tokenizer.pad_token_id = tokenizer.eos_token_id >>> inputs = tokenizer(["Today is"], return_tensors="pt") >>> # Example 1: Print the scores for each token generated with Greedy Search >>> outputs = model.generate(**inputs, max_new_tokens=5, return_dict_in_generate=True, output_scores=True) >>> transition_scores = model.compute_transition_scores( ... outputs.sequences, outputs.scores, normalize_logits=True ... ) >>> # input_length is the length of the input prompt for decoder-only models, like the GPT family, and 1 for >>> # encoder-decoder models, like BART or T5. >>> input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1] >>> generated_tokens = outputs.sequences[:, input_length:] >>> for tok, score in zip(generated_tokens[0], transition_scores[0]): ... # | token | token string | log probability | probability ... print(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}") | 262 | the | -1.414 | 24.33% | 1110 | day | -2.609 | 7.36% | 618 | when | -2.010 | 13.40% | 356 | we | -1.859 | 15.58% | 460 | can | -2.508 | 8.14% >>> # Example 2: Reconstruct the sequence scores from Beam Search >>> outputs = model.generate( ... **inputs, ... max_new_tokens=5, ... num_beams=4, ... num_return_sequences=4, ... return_dict_in_generate=True, ... output_scores=True, ... ) >>> transition_scores = model.compute_transition_scores( ... outputs.sequences, outputs.scores, outputs.beam_indices, normalize_logits=False ... ) >>> # If you sum the generated tokens' scores and apply the length penalty, you'll get the sequence scores. >>> # Tip 1: recomputing the scores is only guaranteed to match with `normalize_logits=False`. Depending on the >>> # use case, you might want to recompute it with `normalize_logits=True`. >>> # Tip 2: the output length does NOT include the input length >>> output_length = np.sum(transition_scores.numpy() < 0, axis=1) >>> length_penalty = model.generation_config.length_penalty >>> reconstructed_scores = transition_scores.sum(axis=1) / (output_length**length_penalty) >>> print(np.allclose(outputs.sequences_scores, reconstructed_scores)) True ```Nrrr(r )rqrrr r rexpandrstackreshape transposerr0r1r r log_softmaxrsummaxrgather) rrhrirr]Zbeam_indices_maskZmax_beam_lengthZbeam_sequence_indicesZcut_idxindicesZtransition_scoresrururvcompute_transition_scorests&T$ z)GenerationMixin.compute_transition_scorescsdurdSjjr0jjs0gdfddtjDtfddD}|s0tdd}jjjjkrL|durJtd|d dS|dusT|dur\td |d dS) N)Zencoder_attention_headsZencoder_ffn_dimZencoder_layerscg|]}|vr|qSrururattr)attributes_to_checkrurv z7GenerationMixin._validate_assistant..c3s(|]}tj|tj|kVqdSr)rrri)r'rrurvrs z6GenerationMixin._validate_assistant..zThe main model and the assistant don't have compatible encoder-dependent input shapes. 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Note that `generation_config` is changed in place and stops being serializable after this method is called. That is no problem if called within `generate` (`generation_config` is a local copy that doesn't leave the function). However, if called outside `generate`, consider creating a copy of `generation_config` first. NcsF|dur|S|dur |nj}t|tjr||Stj||tjdS)Nrr)rrrqrr tensorr)tokenrrrurv_tensor_or_noneFs   z@GenerationMixin._prepare_special_tokens.._tensor_or_nonerCrzThe attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.z)Setting `pad_token_id` to `eos_token_id`:z for open-end generation.z\`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation.rzThe attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. 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Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the model's default generation configuration. You can override any `generation_config` by passing the corresponding parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. For an overview of generation strategies and code examples, check out the [following guide](../generation_strategies). Parameters: inputs (`torch.Tensor` of varying shape depending on the modality, *optional*): The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs` should be in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of `input_ids`, `input_values`, `input_features`, or `pixel_values`. generation_config ([`~generation.GenerationConfig`], *optional*): The generation configuration to be used as base parametrization for the generation call. `**kwargs` passed to generate matching the attributes of `generation_config` will override them. If `generation_config` is not provided, the default will be used, which has the following loading priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s default values, whose documentation should be checked to parameterize generation. logits_processor (`LogitsProcessorList`, *optional*): Custom logits processors that complement the default logits processors built from arguments and generation config. If a logit processor is passed that is already created with the arguments or a generation config an error is thrown. This feature is intended for advanced users. stopping_criteria (`StoppingCriteriaList`, *optional*): Custom stopping criteria that complements the default stopping criteria built from arguments and a generation config. If a stopping criteria is passed that is already created with the arguments or a generation config an error is thrown. If your stopping criteria depends on the `scores` input, make sure you pass `return_dict_in_generate=True, output_scores=True` to `generate`. This feature is intended for advanced users. prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, *optional*): If provided, this function constraints the beam search to allowed tokens only at each step. If not provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful for constrained generation conditioned on the prefix, as described in [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904). synced_gpus (`bool`, *optional*): Whether to continue running the while loop until max_length. Unless overridden, this flag will be set to `True` if using `FullyShardedDataParallel` or DeepSpeed ZeRO Stage 3 with multiple GPUs to avoid deadlocking if one GPU finishes generating before other GPUs. Otherwise, defaults to `False`. assistant_model (`PreTrainedModel`, *optional*): An assistant model that can be used to accelerate generation. The assistant model must have the exact same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistant model is much faster than running generation with the model you're calling generate from. As such, the assistant model should be much smaller. streamer (`BaseStreamer`, *optional*): Streamer object that will be used to stream the generated sequences. Generated tokens are passed through `streamer.put(token_ids)` and the streamer is responsible for any further processing. negative_prompt_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): The negative prompt needed for some processors such as CFG. The batch size must match the input batch size. This is an experimental feature, subject to breaking API changes in future versions. negative_prompt_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Attention_mask for `negative_prompt_ids`. use_model_defaults (`bool`, *optional*): When it is `True`, unset parameters in `generation_config` will be set to the model-specific default generation configuration (`model.generation_config`), as opposed to the global defaults (`GenerationConfig()`). If unset, models saved starting from `v4.50` will consider this flag to be `True`. custom_generate (`str`, *optional*): A string containing the name of a huggingface.co repository. If provided, the custom `generate` function defined in that reposity's `custom_generate/generate.py` file will be executed instead of the standard `generate` method. Note that the logic is for generation is entirely defined in that repository, and the return type may be different from the standard `generate` method. kwargs (`Dict[str, Any]`, *optional*): Ad hoc parametrization of `generation_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*. Return: [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when `config.return_dict_in_generate=True`) or a `torch.LongTensor`. If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible [`~utils.ModelOutput`] types are: - [`~generation.GenerateDecoderOnlyOutput`], - [`~generation.GenerateBeamDecoderOnlyOutput`] If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible [`~utils.ModelOutput`] types are: - [`~generation.GenerateEncoderDecoderOutput`], - [`~generation.GenerateBeamEncoderDecoderOutput`] Nrrrcsi|] \}}|vr||qSruru)rrr)global_keys_to_excluderurvr& sz,GenerationMixin.generate..modelrSr*r(rrrrCrrzA decoder-only architecture is being used, but right-padding was detected! 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The existence of unfinished sequences is fed through `this_peer_finished`. ZeRO stage 3-friendly. r=rArC)opFT)rqrrZ all_reduceZReduceOpZSUMr)rrrrZthis_peer_finished_flagrururv_has_unfinished_sequences s z)GenerationMixin._has_unfinished_sequencescsdurtdjj}}t}td|d}ddj|ddD}|d dd j|j }t ||k||} | d krE|S|ddd f }d d fdd|D} tt|| D]`\} \} } || } t | |kr~qj fdd|j| dD}t|dkrqj|| fd7<|j|d| dd } | d krqjt| | |kdkr||d <|j|d |d|| <qj|S)a Generates sequences of token ids for models with a language modeling head. Parameters: input_ids (`torch.LongTensor`): The sequence used as a prompt for the generation. tokenizer (`PreTrainedTokenizerBase`, *optional*): The tokenizer used to decode the input ids. Return: `torch.LongTensor` where each sequence has its tail token replaced with its appropriate extension. Nzs When generating with token healing, you must pass the model's tokenizer to the `tokenizer` argument of `generate`.r()rvrcSsg|]}|qSru)striprrururvrl z/GenerationMixin.heal_tokens..T)Zskip_special_tokenspt)Zreturn_tensorspaddingrr c3s"|] }|dVqdS)rN)decodereplace)rtZ space_tokrSrurvr s z.GenerationMixin.heal_tokens..csi|] }|fdqS)g$@)convert_tokens_to_ids)rZalt_tok)rSrurvr sz/GenerationMixin.heal_tokens..)prefixrAr;r)rrrr!Z get_vocabr:Z batch_decoderr rrqwhereZnumeltolistZconvert_ids_to_tokensr enumerateziprr extensionsrrrr)rrrSrrZ vocab_trierZpromptsZtail_idsZ tail_toks batch_idxZtail_idZtail_tokZ batch_idsZseq_biasZ trimmed_idsrurrvr sT           zGenerationMixin.heal_tokensrc' s\|jjrtd|j} |j} |j} |j} |j} |j}t dd|D}|j }|r,| r,dnd}|r4| r4dnd}|r<| r.ruNrrFrr(low(ruhighcsg|]}|kr|qSrururiZ final_layerrurvrl rmz2GenerationMixin._dola_decoding..z?dola_layers must be either 'low', 'high' or a list of integers.zCDoLa is not supported for models that don't have output embeddings.rCT)rrrr)rrrr Z num_samplesrbrhrirjrkrlrb)4rrrrrr output_scores output_logitsreturn_dict_in_generaterr/rrqrrrrr0rZtie_word_embeddingsrstrrPrget_output_embeddingsrrrjdetachr float32floatrlr#_dola_select_contrastrzrkr{r|r rsoftmax multinomialsqueezeargmaxrrrrdendrgr)'rrrr(rRrrrrrrrr r rhas_eos_stopping_criteriar/ri raw_logitsrzr{r|rZ cur_lengthunfinished_sequencesrZ start_layercandidate_premature_layersZlm_headrrZfinal_layer_next_token_logits final_logitscandidate_premature_logitsZcandidate_premature_layernext_token_logitsnext_token_scoresprobs next_tokensrurrvr s2    (            L zGenerationMixin._dola_decodingc? s tdd|D}|j|j} |j} |j} |j} |j} |j}|j}|j }|r*|r*dnd}|r2| r2dnd}|r:| r:dnd}|rB| rBdnd}|rJ| rJdnd}|rh|j j rh| r[|d dnd}| rf|d dnd}|j dd\}}tj|tj|jd }|||j|}tj|tjd }|j j rd |vr|d dur|d }n|d }|jd d}d}|j|||jdr| ddust|dttfr\|dd kr\d|d<|j|fi|}|di|dd| d}|j j r|jd}n|jd}|jdddddfjdtj|jd} |j |||j j d}|s)|j!d||j j d|\}!}| d}"|"durt?|/j@dd}/|/t2||3ddf}/tj9||/Adgdd}d}6|0D]})t>t?|)t2||3ddf})|6|)f7}6q|rF|j|&dd|3f3ddfi|}7|di|7dddd}8|8d}9nNd}9tBD] }:|9pTtC||:d}9qJt|9t.sjt|9trpt|9j/t.rp|9D|4n$g}(|9D]})g}*|)D] }+|*1|+|4d fqz|(1t%|*qtt%|(}9t>t?|1t2||3ddf} | |j} |j j rd};d}<| r|j-D]})t>tj?|)d dt2||3d f})|;|)f7};q|j+D]})t>tj?|)d dt2||3d f})|<|)f7}<qtE|9|6|<pd|;pdd!}n-d}=| r&|j,D]})t>tj?|)d dt2||3d f})|=|)f7}=q tF|9|6|=p-dd"}|j |||j j d}|rA|rAq|rN|5|| d|}5tj9||5dddfgdd}|duri|G|5H||||@}|Id k}|j|||jds|dur|J|r| ddurt|dt.st|dtrt|dj/t.r|d4dn-g}"|dD] })g}>|)D]}+|>1|+d ddddfq|"1t%|>qt%|"|d<|j j rtK||||||||| dd# StL|||||| dd$S|S)%a Generates sequences of token ids for models with a language modeling head using **contrastive search** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models. Parameters: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): The sequence used as a prompt for the generation. logits_processor (`LogitsProcessorList`): An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`] used to modify the prediction scores of the language modeling head applied at each generation step. stopping_criteria (`StoppingCriteriaList`): An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`] used to tell if the generation loop should stop. generation_config ([`~generation.GenerationConfig`]): The generation configuration to be used as parametrization of the decoding method. synced_gpus (`bool`): Whether to continue running the while loop until max_length (needed to avoid deadlocking with `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). streamer (`BaseStreamer`, *optional*): Streamer object that will be used to stream the generated sequences. Generated tokens are passed through `streamer.put(token_ids)` and the streamer is responsible for any further processing. model_kwargs: Additional model specific keyword arguments will be forwarded to the `forward` function of the model. If model is an encoder-decoder model the kwargs should include `encoder_outputs`. Return: [`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`] or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a [`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a [`~generation.GenerateEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`. csrrrrrururvr rz6GenerationMixin._contrastive_search..ruNrrkrlrrrrrrr FrCrbTr)rrrrrrrr rzQ does not support caching and therefore **can't** be used for contrastive search.z| does not have a standard cache format and therefore **can't** be used for contrastive search without further modifications.)r rr(rcsg|] \}}||qSruru)rrrqrDrurvrl z7GenerationMixin._contrastive_search...)rbr|rzr{)rbrlrk rhrirjrxryrzr{r|rbr )MrrD penalty_alpharrrr r r low_memoryrrrrrqrrrrrrrrrrrrr|rlrjr rr#rrrrmrrr rrtopkrzrkr{rrZbatch_repeat_interleaverIrr cropstack_model_outputsr0r _ranking_fastrrrrrr_splitrrrrZbatch_select_indicesrrrrrdrrwrg)?rrr(rRrrrrrr(rrrr r r sequentialrrirzr{r|rxryrcur_lenrcosine_matrix_maskrrrZlast_hidden_statesZlogit_for_next_steprrbZprocessed_logit_for_next_stepZ next_probsZ top_k_probsZ top_k_idsZpastZnew_key_valuesrrrZ all_outputsrZnext_model_inputs next_hiddenZfull_hidden_statesrjcontext_hidden selected_idxZ augmented_idxr#Znext_decoder_hidden_statesZnext_model_inputZselected_outputsZnext_past_key_valuesr!Znext_step_cross_attentionsZnext_step_decoder_attentionsZnext_step_attentionsZlayer_past_key_valuesrur%rvr s(,                   &        $    $   $ $ $           z#GenerationMixin._contrastive_searchc$ Ks|j}|j} |j} |j} |j} |j} tdd|D}|j}| r$| r$dnd}| r,| r,dnd}| r4| r4dnd}| r<| r.ruNrrkrlrFr0ZTOKENIZERS_PARALLELISMTrCrrrr rr$r r(r rrbr'r )2rrrr r rrr/rrrrrqrrrr__call__rrenvironget_compiled_callrprefill_chunk_size_prefill_chunkingrrrr#rjr rrzrkr{r|rlr rrrrrrrrrdrrwrg)$rrr(rRrrrrrrrr r rrr/rirrzr{r|rxryrr0rr model_forwardcompile_forwardZ is_prefillrrr r!r"r#rururvr s+     (         K   zGenerationMixin._samplecCsx|jj}t|ttfr|||}|Sd|vr5t|ttfs(t d|d|||}t |}|S| ||S)aC Temporary function to handle the different types of cache reordering processes while we roll out `Cache`. 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The tensor is a 2D or a 3D tensor with the two first dimensions depicting the batch and the beam dimensions. beam_indices (`torch.Tensor` of shape `(batch_size, num_beams_to_select)`): The indices of the beams to select . Returns: A tensor with the selected beams rr()inputrfr )rrrrqZtake_along_dim)rrZgathered_tensorrururv _gather_beamsjs  zGenerationMixin._gather_beamsrunning_beam_scores beam_scoresis_sent_finished!next_token_hits_stopping_criteriar0r-decoder_prompt_lenrrc Cs|dkr |dkr ||} n||} |ddddf| |} t|tj|ddddd} t| | k} t||du@} t|}| | @|@S) zv Beam Search stopping condition -- halts the generation loop if any of these conditions becomes False neverr=Nr(Tr Zkeepdimre)rqrr{rr)rCrDrErFr0r-rGrrZbest_hypothetical_lengthZbest_possible_running_scoreZworst_finished_scoreZimprovement_possibleZexists_open_beamZvalid_continuationsrururv%_beam_search_has_unfinished_sequences~s   z5GenerationMixin._beam_search_has_unfinished_sequencesaccumulated_log_probsrunning_sequencesrunning_beam_indicesr/ beams_to_keepr1c Cs|rtjtjj|dd|d} tj|d| d} n tj||d\} } | | } ||| }||| }| | }||dddd|f<tj| |j d dd|}| |}||dddd||f<| ||fS) a' Get top-K continuations given the accumulated log probs on the next token. A few notes to understand what's going on: 1. Each item in batch has `num_beams` * `vocab_size` candidate continuations. For each item, get the top K [K = (number of EOS tokens + 1) * `num_beams`] candidates with the highest accumulated log-probabilities, or sample them without replacement using the accumulated scores 2. We gather the top K (as opposed to `num_beams`, or any number lower than K) here so that we have at least `num_beams` sequences remaining to continue the live beam search. 3. Note that other stopping criteria might result in impossible to continue beams, i.e. all continuations selected in this step hit the stopping criteria. rr r r()rAr indexrNrC) rqrr rrrer*rBrrr )rrLrMrNr0rGr/rOr?r1rZ topk_indicestopk_log_probsZtopk_current_beam_indicestopk_running_beam_indicestopk_running_sequencesZtopk_idsZ batch_offsetZbatch_modified_indicesrururv_get_top_k_continuationss   z(GenerationMixin._get_top_k_continuationsrRrTrSc CsT||tjd}tj||dd}|||}|||} |||} || | fS)z Given the top-K continuations, their scores, and whether they hit a stopping criteria, select the best non-finished beams to continue beam search in the next iteration. rJrQr()r rqrr*rB) rrRrTrSrFr?Ztopk_running_log_probsZnext_topk_indicesrMrCrNrururv%_get_running_beams_for_next_iterations     z5GenerationMixin._get_running_beams_for_next_iterationtop_num_beam_maskcCs|| dddf@}|| d| | }tj|ddd|du@}||tjd7}||d7}tj||fdd}tj||fdd}tj||fdd}tj||fdd}tj|| dd}|||}|||}|||}|||}||||fS) z Updates the finished beams if (and only if) there are new completed sequences that have a higher score than the current finished sequences. Nr(rT)ZaxisZkeepdimsrJr rQ)rqrr rrr*rB)rrhrTrDrRrrSrErFrWr?r0rGrrZdid_top_num_beams_just_finishedZbeams_in_batch_are_fullZmerged_sequencesZ merged_scoresZmerged_beam_indicesZmerged_is_sent_finishedZtopk_merged_indicesrururv_update_finished_beamss     z&GenerationMixin._update_finished_beamsc8Ks|j}|j}|j} |j} |j} |j} |j} |j}|j}|j }|j }|j }|j }|j dd\}}||}|jjdkr?|jj}n|jjdkrK|j}n|jj}|}d}|dur^|j dnd}tdd||}tjtj|tjdtj||tjdfdd |j}|||j|}|j}|rt d | r| rd nd}| r| rd nd}| r| rd nd} | r| rd nd}!| r| rd nd}"| r| rd nd}#| r|jj!r| r|d "d nd}$| r|d "dnd}%|dur|p|dnd}&tj#|||f|&tj$|jd}'|%||||'ddddd|f<|'&'}(tj||ftj(|jd})d|)ddddf<tj#||fdtj(|jd}*tj||ftj|jd}+tj||ftj|jd},tj#||||fdtj)|jd}-|-&'} |j*|||jdr|+|'ddddd|f}.|j,|.fi|}/|/-| rd| ini|/-| rd| ini|d i|/ddi}0|j.|0||jj!d}|r|rqd|0j/dddddfjdtj0|jd}1t1j2j3|1dd }2||.|2}2| r&| r||1'f7}| r| r||2'f7}| r|!|jj!r|0j4fn|0j5f7}!|jj!r|"|0j6f7}"| r&|#|jj!r!|0j7fn|0j8f7}#~0|%|2||}2|2|)dddddf}2t9|2|||f}2|j:|2|'|-|||||||d \}3}4}5||+|4ddddd|df|},|%|,||},|j;|3|4|5|,|d\}'})}-|j<|(|4|*|3| |5|+|,||||||d\}(}*} }+|"dddur|j=|d|+|-d||fd|d<|d}|>|)|*|+|,||||| }|j*|||jdsn|+|(ddd|ddf}(|+|*ddd|f}*|+| ddd|ddf} | dj?dd }6||6}7|(ddd|7f}(| ddd|6f} | rO| s)d}*|jj!r@t@|(|*||| |$|%|!|"|#|"dd StA|(|*||| |!|#|"dd!S|(S)"a Generates sequences of token ids for models with a language modeling head using **beam search decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models. If it's the first time you're diving into Beam Search, we recommend you read the following blog post: https://huggingface.co/blog/how-to-generate (especially the beam search section). You can recompute the sequence scores from the individual scores using the `compute_transition_scores` function (https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationMixin.compute_transition_scores) Parameters: input_ids (`torch.LongTensor` of shape `(batch_size*num_beams, sequence_length)`): The sequence used as a prompt for the generation. logits_processor (`LogitsProcessorList`): An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`] used to modify the prediction scores of the language modeling head applied at each generation step. stopping_criteria (`StoppingCriteriaList`: An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`] used to tell if the generation loop should stop. generation_config ([`~generation.GenerationConfig`]): The generation configuration to be used as parametrization of the decoding method. synced_gpus (`bool`): Whether to continue running the while loop until max_length (needed to avoid deadlocking with `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). model_kwargs: Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is an encoder-decoder model the kwargs should include `encoder_outputs`. Return: [`generation.GenerateBeamDecoderOnlyOutput`], [`~generation.GenerateBeamEncoderDecoderOutput`] or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a [`~generation.GenerateBeamDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a [`~generation.GenerateBeamEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`. NrZMoshiDepthDecoderZImageGPTForCausalImageModelingFrr(rr z`low_memory=True` is not supported after the beam search refactor. Please check the discussion in #35802 *after the PR got merged*, and add a comment there if your questions are not yet answered.rurrkrlr)Z fill_valuerrrrJrCrrrTr r$) rLrMrNr0rGr/rOr?r1r)rRrTrSrFr?)rhrTrDrRrrSrErFrWr?r0rGrrrb.)rbr% rhr~rirjrrxryrzr{r|rbrhr~rirjrrkrlrb)Brrrrr r rr/rrr-r?rrrrmrZaudio_vocab_sizerZ out_featuresr0r1rdrqrrrzerosr rrr)rrrfullrr@rrrZint32rr?rrr#rjrr rrbrzrkr{r|rlr`rUrVrXr=rKrcrr})8rrr(rRrrrrrrrr r rr/rrr-r?rZbatch_size_unflattenedr0rr1rGr n_eos_tokensrOrWr/Z all_scoresrrrzr{r|rxryZoutput_fill_valuerMrhrCrDrErFrNZflat_running_sequencesr model_outputsrjZ log_probsrRrTrSZmax_generated_lengthZ output_lengthrururvr%s.     " $    (       "       zGenerationMixin._beam_searchrc4 s|j}|j} |j} |j} |j} |j} |j}|j|j}|t |j ||j }|j \}}| ||j |}|rG| rGfddt|Dnd|kr\tdd|d|rb| rbdnd}|rj| rjdnd}|rr| rrdnd}|rz| rzdnd}|r| rdnd}|r|jjr| r|dd nd}| r|dd nd}tjfd tj|d }d |ddddf<|f}d}|j d}|j|||j drtj|j|d }tjtj|d }|j|fi|}|| rd| ini|| rd| ini|di|ddi} |j| ||jjd}|r&|r&|d}q| r8t| jdddddf}!| rM| jdddddfj d|j d}"t|D]}#t!|#}$|$|#}%g}&tD]|&"fddt|#|$Dqi||&}'| j|&dddfj tj#|j d }(t$j%j&|(dd})|)j d}*||'|)|d}+|+||&'d})|)(|+})| r|+|!|&<|)|%|*})| dur| j d nd },tj)|)t*dd|,|%dddd\})}-tj+|-|*dd}.|-|*}-durt,dnd}/|j-|'|)|-|.|| |/|d }0|0d ||&<|0d!}1|0d"|r2| r2t.fd#d$tt d D<|'||&<tj/|'ddf|1'dgdd}'|'dddf||&<tj+|%dd|#|%||&<qQ|r| rs||!f7}| r{||"f7}| r||jjr| j0fn| j1f7}|jjr|| j2f7}| r||jjr| j3fn| j4f7}tj/||'dgdd}~ |d%ddur|5|d%||d%<|d}|j6st7|||rd}|j|||j dsЈdurt,dnd}2|j8|||-|.|| |j9|2|d& }3|r<| s d|3d'<|jjr't:|3d(|3d'|||3d)||||||d%d* St;|3d(|3d'|||3d)|||d%d+S|3d(S),a Generates sequences of token ids for models with a language modeling head using **diverse beam search decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models. Parameters: input_ids (`torch.LongTensor` of shape `(batch_size*num_beams, sequence_length)`): The sequence used as a prompt for the generation. beam_scorer (`BeamScorer`): An derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and sorted during generation. For more information, the documentation of [`BeamScorer`] should be read. logits_processor (`LogitsProcessorList`): An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`] used to modify the prediction scores of the language modeling head applied at each generation step. stopping_criteria (`StoppingCriteriaList`): An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`] used to tell if the generation loop should stop. generation_config ([`~generation.GenerationConfig`]): The generation configuration to be used as parametrization of the decoding method. synced_gpus (`bool`): Whether to continue running the while loop until max_length (needed to avoid deadlocking with `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). model_kwargs: Additional model specific kwargs that will be forwarded to the `forward` function of the model. If model is an encoder-decoder model the kwargs should include `encoder_outputs`. Return: [`~generation.GenerateBeamDecoderOnlyOutput`], [`~generation.GenerateBeamEncoderDecoderOutput`] or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a [`~generation.GenerateBeamDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a [`~generation.GenerateBeamEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`. cs&g|]}tddtDqS)cs|]}dVqdSruNrurrrururvrz@GenerationMixin._group_beam_search...)rrra)r num_sub_beamsrurvrlrz6GenerationMixin._group_beam_search..N)Batch dimension of `input_ids` should be , but is rnrurrkrlrJrrFr(rCrrrTr r)rrcsg|]}|qSruru)rr)rr?rurvrlrmr )current_tokensbeam_group_idxrr Zlargestsortedfloor)Z rounding_mode)rrrZ group_indexrGnext_beam_scoresnext_beam_tokensnext_beam_indicesc3s*|]}||fVqdSrrur)rgr%rrurvr(s z5GenerationMixin._group_beam_search..rbrrr-rrGsequence_scoresrhrrYrZ).rrkrlrrJr(FrCrrrTr rr$r rrh)rrrrGrkrlrmrbc3s&|]}||fVqdSrrurr%rrurvrG$rnrorhrrYrZ)5rrrrr r rrrpr?rrrrrrrrrrqr[rr rrrr#rjr rr rrbrrrrzrkr{r|rlr*rdrrtrrr=rurrvr-rr})*rrrr(rRrrrrrrrr r rrr?rwr0rirrzr{r|rxryrDrrGrrr r!rxZscores_for_all_vocabr1r]r#ryrzr{r|rur}rvrs,      (         $  j   z(GenerationMixin._constrained_beam_searchr4c+ s|j} |j} |j} |j} |j} |j}|r| rdnd}|r | r dnd}|r(| r(dnd}|r0| r0dnd}|r8| r8dnd}|rV|jjrV| rI|ddnd}| rT|ddnd}|j dd\}}t j |t j |j d}|||j |}d}d }|j|||j d r|j d }||\}}||j }|dur||j }|j d |j d }||d}t|}t||j d |jj}t||j d }d |vrt j|d t j||||j t j d fdd|d <|j|fi|} d| vr|d | d<| | rd| ini| | rd| ini|di| }!|!jdd| d dfjt j|j dt|dkrZt|d D]$}"||ddd||"fdd|"ddfdd|"ddf<q5| rm|durmt||||\}#}$n`| rjdd}%t j |%dddddfd d!d dddf}&nj"dd}&|dd|df}'|'|&ddddfkj#ddd k$}$|r|$|kr|$d 8}$|&ddd|$d f}#t j||#fdd}|dur|%|#&|j d }(|(d })t'||!j(|)|!_(|)||$|j*|!||jj|$d d}|r|rqu|r|$d }*| r'|t+fddt|*D7}| r9|t+fddt|*D7}|r>|(n|*}*| rm|jjr[t,||!j-||*}t,||!j.||*d d}n|!j/ddurmt,||!j/||*d d}| r|jjr~t,||!j0||*}nt,||!j1||*}||||@}|2dk}d}|j|||j d s|dur|3t4|dr|j5j6j7dkr|j8|j5j6_8|r|jjrt9|||||||||dd St:||||||ddS|S)a Generates sequences of token ids for models with a language modeling head using **greedy decoding** or **sample** (depending on `do_sample`), assisted by candidate sequences. Assisted generation is an example of a candidate decoding strategy. Can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models. Parameters: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): The sequence used as a prompt for the generation. candidate_generator (`CandidateGenerator`): A derived instance of [`CandidateGenerator`] that defines how candidate sequences are generated. For more information, the documentation of [`CandidateGenerator`] should be read. logits_processor (`LogitsProcessorList`): An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`] used to modify the prediction scores of the language modeling head applied at each generation step. stopping_criteria (`StoppingCriteriaList`): An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`] used to tell if the generation loop should stop. generation_config ([`~generation.GenerationConfig`]): The generation configuration to be used as parametrization of the decoding method. synced_gpus (`bool`): Whether to continue running the while loop until max_length (needed to avoid deadlocking with `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). streamer (`BaseStreamer`, *optional*): Streamer object that will be used to stream the generated sequences. Generated tokens are passed through `streamer.put(token_ids)` and the streamer is responsible for any further processing. model_kwargs: Additional model specific keyword arguments will be forwarded to the `forward` function of the model. If model is an encoder-decoder model the kwargs should include `encoder_outputs`. Return: [`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`] or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a [`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a [`~generation.GenerateEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`. ruNrrkrlrrFTrCr(rrrr rrrrr )rrc3&|]}dd|ddfVqdSrrur) new_logitsrurvr:r~z5GenerationMixin._assisted_decoding..c3rrrur)r rurvr<r~)is_decoder_attentionr' heuristicrbr'r );r/rrr r rrrrrrqrrrrrZget_candidatesr rr6r7rrrrrjrrrr_speculative_samplingrrrrrrcrrr5rbZupdate_candidate_strategyr#r_split_model_outputsr{rzrkr|rlrdrrr'rZnum_assistant_tokens_scheduleZnum_assistant_tokensrwrg)+rrr4r(rRrrrrr/rrr r rrirrzr{r|rxryrr0rrZis_first_iterationcandidate_input_idscandidate_logitscandidate_lengthis_done_candidateZcandidate_kwargsrrr valid_tokens n_matchesr"Zselected_tokensZcandidate_new_tokensZ new_cur_lenZnew_cache_sizeZnewly_added_lengthru)rr rvrzs61        F  4 *             z"GenerationMixin._assisted_decodingcKs<dtjj_|j}tj|ddddf|dd}d|vr td|j}|||}|r1| |j }| dd}d} |D]J} | | j d} |durT|ddd| f|d<tj | | tj| jd|d <|d d|d <|j| fi|} |di| d d i} | j|d<| } q;||d<|d ddd |d <| d d}|S)N@rr rbz+Cannot use prefill chunking without a cacherrrrrrTr(ru)rqZ_dynamorcache_size_limitr9r.rrrr8rrrrrrrrrb)rrrr chunk_sizeZ input_chunksr;r<rrZ input_chunkZcurrent_lengthrrrrururvr:s6         z!GenerationMixin._prefill_chunking)NN)NNNN)NNNr)r(FN)Fr()NF) NNNNNNNNNNNN)Trmrnrorprr rrPathLikerrrrqrrrtr rrrrrrrrr:rrr rrr staticmethodrr"r#r&rFr1r5rrQr[rYrOrgrprtryr}rrrrrrrrrZno_gradGenerateOutputrrrGenerateNonBeamOutputrrrr=r?r@rBrrKrUrVrXGenerateBeamOutputrr+rr-rrr:rurururvras! C " A   B    $  /  ; &   1  W   n '    ) z8,9  U"N @ y  R)        U U ! =   '    6        3  F % o  rcCs|dd| df}|jdd}|ddt||fdd}|jdd}|ddt||fdd} | |} t| } | | k} | jdddk} |ri| |kri| d8} |ddd| df}|| fS|jd}|dd| ddf}| |kr|dd| ddf}tj||dd}| |n|}tj |ddddddf}| dkrtj |ddd| f|fdd}|| fS|}|| fS)a Applies sampling as in the speculative decoding paper (https://arxiv.org/pdf/2211.17192.pdf, algorithm 1). Returns the selected tokens, as well as the number of candidate matches. NOTE: Unless otherwise stated, the variable names match those in the paper. Nrr rr()r{r ) rrqrrZ rand_likerrcrclampZdiv_rr)rrrrrZnew_candidate_input_idsqZq_irZp_iZprobability_ratioZr_iZ is_acceptedrrgammaZ p_n_plus_1Z q_n_plus_1Zp_primerrururvrs4        "rFc Cst|dkr1d}|D]}|r|n|jd}||dd|d|ff7}q ||f7}|d7}||8}t|D])}d}|D]}|rC||n|jd}||d||dd|ff7}q;||f7}q5|S)z Given the (decoder/cross attentions)/(decoder hidden states) for multiple generated tokens, splits it into a tuple where each member corresponds to a single generated token. rrur.Nr()rrr) rZ new_outputsr0Z added_lenrZ new_tuplerZ last_dim_sizerrururvrs   " rr3r2next_top_k_probsr1alpha beam_widthrc Cs||jddd}||jddd}t||ddd}|j|jd}d|t|jj}||}tj |dd\} } | d}d|||| } t t | |} | j dd\} } | S) a  Reranks the top_k candidates based on a degeneration penalty (cosine similarity with previous tokens), as described in the paper "A Contrastive Framework for Neural Text Generation". Returns the index of the best candidate for each row in the batch. rTrIr(rrr rA) Znormrqmatmulrarr rZfinfor{rdr r_r.) r3r2rr1rrZnorm_context_hiddenZnorm_next_hiddenZ cosine_matrixZdegeneration_penaltyrZcontrastive_scorer4rururvr-s  r-full_batch_size split_sizecsdur dg|Sttjrfddtd|DStts/ttr5tjtr5|Sttr]tdtrOfddtd|DSfddtd|DSt dt )a Takes care of three cases: 1. data is a tensor: e.g. last_hidden_state, pooler_output etc. split them on the batch_size dim 2. data is a tuple: e.g. hidden_states, attentions etc. Keep the tuple as it is and split each tensor in it and return a list of tuples 3. data is a tuple of tuples, e.g. past_key_values. Keep the tuple as it is and split each tuple in it and return a list of tuples of tuples (see documentation of ModelOutput) Ncsg|] }||qSrururdatarrurvrl0r&z_split..rc$g|]tfddDqS)c3s(|]}tfdd|DVqdS)c3 |] }|VqdSrru)rrrrrurvr:z._split....Nr)rZ inner_tuplerrurvr:s&$_split...rrrrrvrl9cr)c3rrru)rZ sub_tensorrrurvr@rrrrrrrvrl?rUnexpected attribute type: ) rrqrrrrrZ batch_splitr TypeErrorr3)rrrrurrv_split#s&          rrrcs6dur gStdkrtdkr!tdtdr+jn}fdd|D}fdd|D}gd fd d|Dfd dtD}fd d |Ddvrtd|fddt|D}dvrfdd|D}fdd|D}|S)a  Split a ModelOutput object (or its subclasses) or Dict into a list of same-class objects based on a specified split size. The input object is dict when it was prepared for forward pass and ModelOutput when it was returned from previous forward pass. 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