o ei15@s^ddlmZddlZddlmZddlmZmZddlm Z ddl m Z ddl m Z dd lmZdd lmZd d lmZmZmZmZmZmZmZmZmZd d lmZmZee Z!GdddeZ"GdddeZ#GdddeZ$GdddeZ%GdddeZ&GdddeZ'GdddeZ(GdddeZ)GdddeZ*Gd d!d!eZ+gd"Z,dS)#)CallableN)Cache)PreTrainedConfiglayer_type_validation)FlashAttentionKwargs)RopeParameters)ALL_ATTENTION_FUNCTIONS)Unpack)logging) LlamaAttentionLlamaDecoderLayerLlamaForCausalLMLlamaForQuestionAnsweringLlamaForSequenceClassificationLlamaForTokenClassificationLlamaPreTrainedModelapply_rotary_pos_embeager_attention_forward) Qwen2ModelQwen2RotaryEmbeddingc2sheZdZdZdZdgZdZddddddddZdgd gfd d gd gfd gd gfd Z                       d8de dBde dBd e dBd!e dBd"e dBd#e dBd$e dBd%e dBd&e dBd'e dBd(e dBd)e dBd*e dBd+e dBd,e ee e fBdBd-e dBd.e dBd/e dBd0e dBd1e dBd2e dBd3e dBd4e dBd5e dBf0fd6d7 ZZS)9 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_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. 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_valuesg>Acolwiserowwise)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+$silu{Gz?ư>TNF vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range rms_norm_eps use_cache pad_token_id bos_token_id eos_token_idrope_parametersuse_sliding_windowsliding_windowno_rope_layersno_rope_layer_interval layer_typesattention_biasattention_dropoutmlp_biastie_word_embeddingsc s,| |_| |_||_||_||_||_||_||_||_||_ ||_ ||_ ||_ |dur-|}||_ ||_| |_| |_| |_||_||_|durSfddt|D|_n||_|_|dur~g}t|D]}|j|}|rx|durx|sx|dqc|dqc||_t|j|j ||_tjdi|dS)Ncs g|] }t|ddkqS)r)int).0 layer_idxrDi/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/smollm3/modular_smollm3.py sz*SmolLM3Config.__init__..sliding_attentionfull_attentionrO)r=r>r?rIr2r9rHr3r4r5r6rArBr7r8r:r;r<rFrGrangerCrDappendrErr@super__init__)selfr2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIkwargsrMhas_rope __class__rNrPrWsN      zSmolLM3Config.__init__)r$r%r&r'r(r)r*r+r,r-Tr.r/r0NFNNr)NFr1FT)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inference default_thetabase_model_tp_planbase_model_pp_planrKstrfloatboolrdictrW __classcell__rOrOr[rPr+sP       rc@ eZdZdS)SmolLM3RotaryEmbeddingNr]r^r_rOrOrOrPrlrlcseZdZdedeffdd Z  ddejdeejejfdejdBd e dBd ej dBd e e d eejejdBffd dZ ZS)SmolLM3AttentionconfigrMcsDt|||j||_|jr|j|dkr|j|_dSd|_dS)NrR)rVrWrCuse_roperArErBrXrprMr[rOrPrWs zSmolLM3Attention.__init__Nrposition_embeddingsr rcache_positionrYreturncKs|jdd}g|d|jR}|||dd} |||dd} |||dd} |jrE|\} } t| | | | \} } |durXd|i}| | | |j |\} } t |j jt}||| | | |f|jsldn|j|j|jd|\}}|jg|dR}||}||fS)NrJr rtr1)dropoutscalingrB)shapehead_dimq_projview transposek_projv_projrqrupdaterMr get_interfacerp_attn_implementationrtrainingrGrxrBreshape contiguouso_proj)rXrrsr rrtrY input_shape hidden_shape query_states key_states value_statescossin cache_kwargsattention_interface attn_output attn_weightsrOrOrPforwards<   zSmolLM3Attention.forward)NN)r]r^r_rrKrWtorchTensortupler LongTensorr rrrjrOrOr[rPros&rocs&eZdZdedeffdd ZZS)SmolLM3DecoderLayerrprMcst|||j||_dS)N)rVrWrEattention_typerrr[rOrPrWszSmolLM3DecoderLayer.__init__)r]r^r_rrKrWrjrOrOr[rPrsrc@rk)SmolLM3PreTrainedModelNrmrOrOrOrPr!rnrc@rk) SmolLM3ModelNrmrOrOrOrPr%rnrc@rk)SmolLM3ForCausalLMNrmrOrOrOrPr)rnrc@rk) SmolLM3ForSequenceClassificationNrmrOrOrOrPr-rnrc@rk)SmolLM3ForTokenClassificationNrmrOrOrOrPr1rnrc@rk)SmolLM3ForQuestionAnsweringNrmrOrOrOrPr5rnr)rrrrrrr)-collections.abcrr cache_utilsrconfiguration_utilsrrmodeling_flash_attention_utilsrmodeling_rope_utilsrmodeling_utilsr processing_utilsr utilsr llama.modeling_llamar rrrrrrrrqwen2.modeling_qwen2rr get_loggerr]loggerrrlrorrrrrrr__all__rOrOrOrPs0       , 58