o i"@@sZddlmZmZddlZddlmZddlmZmZddl m Z ddl m Z ddl mZdd lmZdd lmZdd lmZd d lmZmZmZmZmZmZmZmZmZd dlm Z e!e"Z#GdddeZ$GdddeZ%GdddeZ&GdddeZ'Gddde Z(GdddeZ)GdddeZ*GdddeZ+Gdd d eZ,gd!Z-dS)")CallableOptionalN)Cache)PretrainedConfiglayer_type_validation)FlashAttentionKwargs)rope_config_validation)ALL_ATTENTION_FUNCTIONS)Unpack)logging)deprecate_kwarg) LlamaAttentionLlamaDecoderLayerLlamaForCausalLMLlamaForQuestionAnsweringLlamaForSequenceClassificationLlamaForTokenClassificationLlamaPreTrainedModelapply_rotary_pos_embeager_attention_forward) Qwen2ModelcseZdZdZdZdgZddddddddZdgdgfd d gd gfd gd gfd Z                      d fdd ZZ S)! SmolLM3Configa This is the configuration class to store the configuration of a [`SmolLM3Model`]. It is used to instantiate a SmolLM3 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 SmolLM3 3B. e.g. [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) 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 128256): Vocabulary size of the SmolLM3 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SmolLM3Model`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 36): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 4): 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 checkout [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `16`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 32768): 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 128004): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 128000): The id of the beginning of sentence token. eos_token_id (`int`, *optional*, defaults to 128001): The id of the end of sentence token. rope_theta (`float`, *optional*, defaults to 2000000.0): 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 use_sliding_window (`bool`, *optional*, defaults to `False`): Whether to use sliding window attention. sliding_window (`int`, *optional*): Sliding window attention (SWA) window size. If not specified, will default to `None`. no_rope_layers (`List[int]`, *optional*): List with at least the same length as the number of layers in the model. A `1` at an index position indicates that the corresponding layer will use RoPE, while a `0` indicates that it's a NoPE layer. no_rope_layer_interval (`int`, *optional*, defaults to 4): If `no_rope_layers` is `None`, it will be created using a NoPE layer every `no_rope_layer_interval` layers. layer_types (`list`, *optional*): Attention pattern for each layer. Automatically computed based on sliding window and NoPE settings. 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 SmolLM3Model, SmolLM3Config >>> # Initializing a SmolLM3 style configuration >>> configuration = SmolLM3Config() >>> # Initializing a model from the SmolLM3 style configuration >>> model = SmolLM3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```smollm3past_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+$silu{Gz?ư>T>ANFc sNtjd| | |d|||_||_||_||_||_||_||_||_ ||_ |dur.|}||_ ||_ | |_ | |_| |_||_||_||_||_|durZfddt|D|_n||_|_|durg}t|D]}|j|}|r|dur|s|dqj|dqj||_t|j|j|jdurd|jvr|jd|jd<t|dS) N) pad_token_id bos_token_id eos_token_idcs g|] }t|ddkqS)r)int).0 layer_idxno_rope_layer_intervalh/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/transformers/models/smollm3/modular_smollm3.py sz*SmolLM3Config.__init__..sliding_attentionfull_attentiontype rope_typer=)super__init__ vocab_sizemax_position_embeddingsmlp_bias hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsuse_sliding_windowsliding_windownum_key_value_heads hidden_actinitializer_range rms_norm_eps use_cache rope_theta rope_scalingattention_biasattention_dropoutrangeno_rope_layersr<append layer_typesrr )selfrFrIrJrKrLrOrPrGrQrRrSr4r5r6rTrUrMrNrYr<r[rVrWrHkwargsr:has_rope __class__r;r>rEsZ       zSmolLM3Config.__init__)r%r&r'r(r)r*r+r,r-r.Tr/r0r1r2NFNNr*NFr3F) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planrE __classcell__r=r=r_r>r-sRs  rcseZdZdedeffdd Zedddd dd ejd e ejejfd e ejde e d e ej de ede eje ejffddZZS)SmolLM3Attentionconfigr:csDt|||j||_|jr|j|dkr|j|_dSd|_dS)Nr@)rDrErYuse_roperMr[rNr\rkr:r_r=r>rE s zSmolLM3Attention.__init__past_key_valuerz4.58)new_nameversionNr position_embeddingsr!cache_positionr]returncKs*|jdd}g|d|jR}|||dd} |||dd} |||dd} |jrE|\} } t| | | | \} } |durXd|i}| | | |j |\} } t }|j j dkrft|j j }||| | | |f|jsrdn|j|j|jd|\}}|jg|dR}||}||fS)Nr7rrreagerr3)dropoutscalingrN)shapehead_dimq_projview transposek_projv_projrlrupdater:rrk_attn_implementationr trainingrWrwrNreshape contiguouso_proj)r\r rqr!rrrr] input_shape hidden_shape query_states key_states value_statescossin cache_kwargsattention_interface attn_output attn_weightsr=r=r>forwards<     zSmolLM3Attention.forward)NN)rarbrcrr8rEr torchTensortuplerr LongTensorr rrrir=r=r_r>rj s( rjcs&eZdZdedeffdd ZZS)SmolLM3DecoderLayerrkr:cst|||j||_dS)N)rDrEr[attention_typermr_r=r>rEDszSmolLM3DecoderLayer.__init__)rarbrcrr8rErir=r=r_r>rCsrc@ eZdZdS)SmolLM3PreTrainedModelNrarbrcr=r=r=r>rIrc@r) SmolLM3ModelNrr=r=r=r>rMrrc@r)SmolLM3ForCausalLMNrr=r=r=r>rQrrc@r) SmolLM3ForSequenceClassificationNrr=r=r=r>rUrrc@r)SmolLM3ForTokenClassificationNrr=r=r=r>rYrrc@r)SmolLM3ForQuestionAnsweringNrr=r=r=r>r]rr)rrrrrrr).typingrrr cache_utilsrconfiguration_utilsrrmodeling_flash_attention_utilsrmodeling_rope_utilsr modeling_utilsr processing_utilsr utilsr utils.deprecationr llama.modeling_llamarrrrrrrrrqwen2.modeling_qwen2r get_loggerraloggerrrjrrrrrrr__all__r=r=r=r>s0       , ^9