a hA@sVddlmZmZddlZddlmZddlmZddlmZddl m Z ddl m Z m Z d d lmZd d lmZmZmZmZmZmZmZmZmZmZd d lmZe eZGd ddeZ GdddeZ!GdddeZ"GdddeZ#GdddeZ$GdddeZ%GdddeZ&GdddeZ'GdddeZ(Gdd d eZ)gd!Z*dS)")CallableOptionalN)nn)Cache)ALL_ATTENTION_FUNCTIONS)Unpack)TransformersKwargslogging) LlamaConfig) LlamaAttentionLlamaDecoderLayerLlamaForCausalLMLlamaForTokenClassification LlamaModelLlamaPreTrainedModel LlamaRMSNormLlamaRotaryEmbeddingapply_rotary_pos_embeager_attention_forward) NemotronMLPcsleZdZdZdZddddddddZdd d d d d d dddddddddddddddddffdd ZZS) ApertusConfiga This is the configuration class to store the configuration of a [`ApertusModel`]. It is used to instantiate a Apertus 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 Apertus-8B. e.g. [swiss-ai/Apertus-8B](https://huggingface.co/swiss-ai/Apertus-8B) 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 131072): Vocabulary size of the Apertus model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ApertusModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*): 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`. hidden_act (`str` or `function`, *optional*, defaults to `"xielu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 65536): The maximum sequence length that this model might ever be used with. Apertus supports up to 65536 tokens. 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-05): 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 3): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 12000000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`list[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`list[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE attention_bias (`bool`, *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 ApertusModel, ApertusConfig >>> # Initializing a Apertus-8B style configuration >>> configuration = ApertusConfig() >>> # Initializing a model from the Apertus-8B style configuration >>> model = ApertusModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZapertusZ colwise_repZ rowwise_repZcolwiseZrowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.up_projzlayers.*.mlp.down_projzlayers.*.mlp.gate_projiii8 NZxieluig{Gz?gh㈵>Trr Fg`fAZllama3g @i g?g@)Z rope_typefactorZ original_max_position_embeddingsZlow_freq_factorZhigh_freq_factorc sJtjf||||||||| | | | | ||||||d||`|`|`dS)N) vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range rms_norm_eps use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddings rope_theta rope_scalingattention_biasattention_dropout)super__init__Zpretraining_tpZmlp_biashead_dim)selfrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/kwargs __class__g/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/apertus/modular_apertus.pyr1s4zApertusConfig.__init__)__name__ __module__ __qualname____doc__Z model_typeZbase_model_tp_planr1 __classcell__r7r7r5r8r,sFj rcseZdZfddZZS) ApertusMLPcs:ttj|j|jdd|_tj|j|jdd|_dS)NF)Zbias)r0r1rZLinearrrZup_projZ down_proj)r3configr5r7r8r1s zApertusMLP.__init__)r9r:r;r1r=r7r7r5r8r>sr>c@s eZdZdS)ApertusRMSNormNr9r:r;r7r7r7r8r@sr@c@s eZdZdS)ApertusRotaryEmbeddingNrAr7r7r7r8rBsrBc steZdZdeeedfdd Zd eje ejejfeejee eej e e e ejejfdddZZS) ApertusAttentionNr? layer_idxcs2t||t|j|j|_t|j|j|_dS)N)r0r1r@r2r&q_normk_normr3r?rEr5r7r8r1szApertusAttention.__init__) hidden_statesposition_embeddingsattention_maskpast_key_valuescache_positionr4returncKs8|jdd}g|d|jR}|||dd} |||dd} |||dd} || } || } |\} } t | | | | \} } |dur| | |d}| | | |j |\} } t }|j jdkrt|j j}||| | | |f|jsdn|j|jd|\}}|jg|dR}||}||fS)Nrr )sincosrMeagerr)Zdropoutscaling)shaper2Zq_projviewZ transposeZk_projZv_projrFrGrupdaterErr?Z_attn_implementationrZtrainingr/rSZreshape contiguousZo_proj)r3rIrJrKrLrMr4Z input_shapeZ hidden_shapeZ query_statesZ key_statesZ value_statesrQrPZ cache_kwargsZattention_interfaceZ attn_outputZ attn_weightsr7r7r8forwards<        zApertusAttention.forward)N)NN)r9r:r;rrintr1torchTensortupler LongTensorrr rXr=r7r7r5r8rCs rCc szeZdZeedfdd Zd ejeejeej ee ee eej ee ejejfe ee ejd ddZZS) ApertusDecoderLayerrDcs>t||t|j|jd|_t|j|jd|_|`|`dS)N)eps) r0r1r@rr&attention_layernormfeedforward_layernormZinput_layernormZpost_attention_layernormrHr5r7r8r1s zApertusDecoderLayer.__init__NF) rIrK position_idsrLr'rMrJr4rNc Ks^|} ||}|jf|||||||d|\}} | |}|} ||}||}| |}|S)N)rIrKrbrLr'rMrJ)r`Z self_attnraZmlp) r3rIrKrbrLr'rMrJr4Zresidual_r7r7r8rX%s&     zApertusDecoderLayer.forward)NNNFNN)r9r:r;rrYr1rZr[rr]rboolr\rr rXr=r7r7r5r8r^s$ r^c@s eZdZdS)ApertusPreTrainedModelNrAr7r7r7r8reFsrec@s eZdZdS) ApertusModelNrAr7r7r7r8rfJsrfcseZdZfddZZS)ApertusForCausalLMc stjfi|S)an labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> from transformers import AutoTokenizer, ApertusForCausalLM >>> model = ApertusForCausalLM.from_pretrained("swiss-ai/Apertus-8B") >>> tokenizer = AutoTokenizer.from_pretrained("swiss-ai/Apertus-8B") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```)r0rX)r3Z super_kwargsr5r7r8rXOszApertusForCausalLM.forward)r9r:r;rXr=r7r7r5r8rgNsrgc@s eZdZdS)ApertusForTokenClassificationNrAr7r7r7r8rhisrh)rrfrgrhre)+typingrrrZrZ cache_utilsrZmodeling_utilsrZprocessing_utilsrutilsr r Zllama.configuration_llamar Zllama.modeling_llamar rrrrrrrrrZnemotron.modeling_nemotronrZ get_loggerr9loggerrr>r@rBrCr^rerfrgrh__all__r7r7r7r8s,     0  /3*