o ۷iIQ@sddlmZmZmZddlZddlZddlmZddlm Z m Z ddl m Z ddl mZddlmZdd lmZdd lmZdd lmZmZdd lmZmZd dlmZmZmZmZm Z m!Z!m"Z"d dl#m$Z$ernddl%m&Z&ddiZ'dZ(e)e*Z+Gddde Z,Gddde$eZ-Gdddej.Z/GdddeZ0Gddde"Z1Gddde!Z2Gd d!d!e Z3Gd"d#d#eZ4Gd$d%d%eZ5Gd&d'd'eZ6gd(Z7dS))) TYPE_CHECKINGAnyOptionalN)nn)Cache DynamicCache)PretrainedConfig)create_causal_mask)BaseModelOutputWithPast)PreTrainedModel)Unpack) AddedTokenPreTrainedTokenizer)TransformersKwargslogging)LlamaForCausalLMLlamaForSequenceClassificationLlamaForTokenClassificationLlamaMLP LlamaModelLlamaPreTrainedModelLlamaRotaryEmbedding)LlamaTokenizer) TextInput vocab_fileztokenizer.modelu▁cseZdZdZdZdgZddddddddZdgdgfd d gd gfd gd gfd Z                  d fdd ZZ S)! GemmaConfiga This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Gemma-7B. e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 256000): Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GemmaModel`] hidden_size (`int`, *optional*, defaults to 3072): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 24576): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 16): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. head_dim (`int`, *optional*, defaults to 256): The attention head dimension. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The legacy activation function. It is overwritten by the `hidden_activation`. hidden_activation (`str` or `function`, *optional*): The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"` if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function. max_position_embeddings (`int`, *optional*, defaults to 8192): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. eos_token_id (`int`, *optional*, defaults to 1): End of stream token id. bos_token_id (`int`, *optional*, defaults to 2): Beginning of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import GemmaModel, GemmaConfig >>> # Initializing a Gemma gemma-7b style configuration >>> configuration = GemmaConfig() >>> # Initializing a model from the gemma-7b style configuration >>> model = GemmaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```gemmapast_key_valuescolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnorm `gelu_pytorch_tanhN {Gz?ư>Trr@Fc s||_| |_||_||_||_||_||_||_||_| |_ | |_ | |_ | |_ ||_ ||_||_tjd||||d|dS)N) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headshead_dimnum_key_value_heads hidden_acthidden_activationinitializer_range rms_norm_eps use_cache rope_thetaattention_biasattention_dropoutsuper__init__)selfr;r=r>r?r@rBrArCrDr<rErFrGr6r8r7r9rHrIrJkwargs __class__r:]/home/ubuntu/vllm_env/lib/python3.10/site-packages/transformers/models/gemma/modular_gemma.pyrLs0 zGemmaConfig.__init__)r)r*r+r,r-r-r.r/Nr0r1r2Trr3rTr4Fr5) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planrL __classcell__r:r:rOrQr5sJC  rc @seZdZdZ          dd eeeeffd d Zd d Z ddZ ddde efddZ ddZ  dde edededefddZddZdS) GemmaTokenizera Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is no padding token in the original model. Args: vocab_file (`str`): Path to the vocabulary file. unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`): The end of sequence token. pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`): A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. sp_model_kwargs (`dict[str, Any]`, `Optional`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. add_bos_token (`bool`, *optional*, defaults to `True`): Whether or not to add an `bos_token` at the start of sequences. add_eos_token (`bool`, *optional*, defaults to `False`): Whether or not to add an `eos_token` at the end of sequences. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra spaces. use_default_system_prompt (`bool`, *optional*, defaults to `False`): Whether or not the default system prompt for Gemma should be used. spaces_between_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to add spaces between special tokens. NTFsp_model_kwargsc Ks|durin||_t|trt|dddn|}t|tr#t|dddn|}t|tr1t|dddn|}t|tr?t|dddn|}||_||_||_| |_tj di|j|_ |j |t j |f|||||||| | | d | dS)NFT) normalizedspecial) bos_token eos_token unk_token pad_token add_bos_token add_eos_tokenr`clean_up_tokenization_spacesuse_default_system_promptspaces_between_special_tokensr:)r` isinstancestrrrrgrhrjspmSentencePieceProcessorsp_modelLoadrrL) rMrrercrdrfr`rgrhrirjrkrNr:r:rQrLs6  zGemmaTokenizer.__init__cCtdNzNot needed for GemmaAttributeErrorrMr:r:rQget_spm_processorz GemmaTokenizer.get_spm_processorcCrrrsrtrvr:r:rQunk_token_lengthrxzGemmaTokenizer.unk_token_lengthtextrreturncKstj||fi|S)ze Args: text: TextInput Simply calls PreTrainedTokenizer's method )rtokenizerMrzrNr:r:rQr|szGemmaTokenizer.tokenizecKs|jj|tdS)z Args: text: TextInput Returns a tokenized string. The Gemma tokenizer never adds a prefix space. )out_type)rpencodermr}r:r:rQ _tokenize$szGemmaTokenizer._tokenize token_idsskip_special_tokensrkcKsg}g}|D]+}|r||jvrq||jvr,|r ||j|||j|jg}q||q|r=||j||rEd|}nd|}|tdS)N ) all_special_ids_added_tokens_decoderappendrpdecodecontentjoinreplaceSPIECE_UNDERLINE)rMrrrkrN sub_textscurrent_sub_textidsr:r:rQ_decode,s"     zGemmaTokenizer._decodecCsTg}d}|D]}||jvr||j||7}g}q||q||j|7}|S)z:Converts a sequence of tokens (string) in a single string.r)_added_tokens_encoderrprr)rMtokenscurrent_sub_tokens out_stringtokenr:r:rQconvert_tokens_to_stringIs  z'GemmaTokenizer.convert_tokens_to_string) r\r]r^r_NTFFFF)FF)rRrSrTrUrdictrmrrLrwrylistr|rintboolrrr:r:r:rQr[s>1 +  r[cs@eZdZd dedeffdd ZddZdd Zd d ZZ S) GemmaRMSNormr2dimepscs&t||_tt||_dS)N)rKrLrr Parametertorchzerosweight)rMrrrOr:rQrLYs zGemmaRMSNorm.__init__cCs$|t|djddd|jS)NrT)keepdim)rrsqrtpowmeanr)rMxr:r:rQ_norm^s$zGemmaRMSNorm._normcCs*||}|d|j}||S)Ng?)rfloatrtype_as)rMroutputr:r:rQforwardas zGemmaRMSNorm.forwardcCst|jjd|jS)Nz, eps=)tuplershaperrvr:r:rQ extra_reprhszGemmaRMSNorm.extra_repr)r2) rRrSrTrrrLrrrrZr:r:rOrQrXs rceZdZfddZZS)GemmaMLPcsRt|tj|j|jdd|_tj|j|jdd|_tj|j|jdd|_dS)NF)bias) rKrLrLinearr=r> gate_projup_proj down_proj)rMconfigrOr:rQrLms zGemmaMLP.__init__)rRrSrTrLrZr:r:rOrQrlrc@ eZdZdS)GemmaRotaryEmbeddingNrRrSrTr:r:r:rQrtrc@seZdZddZdS)GemmaPreTrainedModelcCs,t||d|jjvr|jjdSdS)NRMSNorm)r _init_weightsrPrRrdatazero_)rMmoduler:r:rQrys  z"GemmaPreTrainedModel._init_weightsN)rRrSrTrr:r:r:rQrxs rc@steZdZ       d deejdeejdeejdeedeejdee deejd e e d e fd d Z dS) GemmaModelNr"r% position_idsrr#rGcache_positionrNr{c Ks*|du|duAr td|dur||}|r!|dur!t|jd}|dur=|dur-|nd} tj| | |jd|jd}|durF| d}t |j|||||d} |} | | |} tj |jj d| jd} | | } |jd|jjD]}|| f| ||||| d |} qr|| } t| |r|d Sdd S) Nz:You must specify exactly one of input_ids or inputs_embeds)rrr3)device)r input_embedsr%rrrg?)dtype)r%rrrGrposition_embeddings)last_hidden_stater) ValueErrorr&rrget_seq_lengthrarangerr unsqueezer rotary_embtensorr=rr'r?r(r )rMr"r%rrr#rGrrNpast_seen_tokens causal_maskr$r normalizer decoder_layerr:r:rQrsZ       zGemmaModel.forward)NNNNNNN)rRrSrTrr LongTensorTensorr FloatTensorrr rr rr:r:r:rQrs6  rcr)GemmaForCausalLMc stjdi|S)a| Example: ```python >>> from transformers import AutoTokenizer, GemmaForCausalLM >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b") >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") >>> prompt = "What is your favorite condiment?" >>> 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] "What is your favorite condiment?" ```Nr:)rKr) super_kwargsrOr:rQrszGemmaForCausalLM.forward)rRrSrTrrZr:r:rOrQrrrc@r)GemmaForSequenceClassificationNrr:r:r:rQrrrc@r)GemmaForTokenClassificationNrr:r:r:rQrrr)rr[rrrrr)8typingrrr sentencepiecernrr cache_utilsrrconfiguration_utilsr masking_utilsr modeling_outputsr modeling_utilsr processing_utilsr tokenization_utilsrrutilsrrllama.modeling_llamarrrrrrrllama.tokenization_llamartokenization_utils_baserVOCAB_FILES_NAMESr get_loggerrRloggerrr[Modulerrrrrrrr__all__r:r:r:rQs@      $    E