o ZhV@s^ddlmZddlmZeeZGdddeZGdddeZGdddeZ gd Z d S) )PretrainedConfig)loggingcseZdZdZddddddddZdZdZ               d'dedededededed ed!ed"ed#e d$e ffd%d& Z Z S)(Llama4VisionConfiga This is the configuration class to store the configuration of a [`Llama4VisionModel`]. It is used to instantiate a Llama4 vision 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 Llama4 109B. e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. num_hidden_layers (`int`, *optional*, defaults to 34): 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_channels (`int`, *optional*, defaults to 3): Number of channels in the input image. intermediate_size (`int`, *optional*, defaults to 5632): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. vision_output_dim (`int`, *optional*, defaults to 7680): Dimensionality of the vision model output. Includes output of transformer encoder with intermediate layers and global transformer encoder. image_size (`int`, *optional*, defaults to 448): The size (resolution) of each image *tile*. patch_size (`int`, *optional*, defaults to 14): The size (resolution) of each patch. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. vision_feature_layer (``, *optional*, defaults to -1): TODO vision_feature_select_strategy (`int`, *optional*, defaults to `"default"`): TODO initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. pixel_shuffle_ratio (`int`, *optional*, defaults to 0.5): TODO projector_input_dim (`int`, *optional*, defaults to 4096): TODO projector_output_dim (`int`, *optional*, defaults to 4096): TODO multi_modal_projector_bias (`int`, *optional*, defaults to `False`): TODO projector_dropout (`int`, *optional*, defaults to 0.0): TODO attention_dropout (`int`, *optional*, defaults to 0.0): TODO rope_theta (`int`, *optional*, defaults to 10000): TODO colwiserowwise colwise_rep)zmodel.layers.*.self_attn.q_projzmodel.layers.*.self_attn.k_projzmodel.layers.*.self_attn.v_projzmodel.layers.*.self_attn.o_projzvision_adapter.mlp.fc1zvision_adapter.mlp.fc2zpatch_embedding.linearZllama4_vision_model vision_configgelu"rh㈵>default{Gz??F' hidden_size hidden_actnum_hidden_layersnum_attention_heads num_channelsintermediate_sizevision_output_dim image_size patch_sizenorm_epsinitializer_rangec s||_||_||_||_||_||_||_| |_| |_||_ | |_ ||_ ||_ ||_ ||_||_||_| |_| |_||_tjdi|dS)N)rrrrrr rr!r"rr#pixel_shuffle_ratioprojector_input_dimprojector_output_dimmulti_modal_projector_biasprojector_dropoutattention_dropoutvision_feature_layervision_feature_select_strategy rope_thetasuper__init__)selfrrrrrrrr r!r"r+r,r#r%r&r'r(r)r*r-kwargs __class__r$^/var/www/auris/lib/python3.10/site-packages/transformers/models/llama4/configuration_llama4.pyr/Ts*zLlama4VisionConfig.__init__)r r r r rr rrrrrrrrrrFrrr) __name__ __module__ __qualname____doc__base_model_tp_plan model_typeZbase_config_keyintstrfloatr/ __classcell__r$r$r2r4rsn/    rcseZdZdZdZdgZidddddddd d d d d d d ddddddddddddddddddddZ    ! " # $ % & ' ( ) * + , - . / + 0 * + ) - 1 / * * 2  )  3 4d7fd5d6 ZZS)8Llama4TextConfiga This is the configuration class to store the configuration of a [`Llama4TextModel`]. It is used to instantiate a Llama4 text 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 Llama4 109B. e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) 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 202048): Vocabulary size of the Llama4 text model. Defines the maximum number of different tokens that can be represented by the `inputs_ids` passed when calling [`Llama4TextModel`]. hidden_size (`int`, *optional*, defaults to 5120): Dimensionality of the embeddings and hidden states. intermediate_size (`int`, *optional*, defaults to 8192): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. intermediate_size_mlp (`int`, *optional*, defaults to 16384): TODO num_hidden_layers (`int`, *optional*, defaults to 48): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 40): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 8): This is the number of key_value heads that should be used to implement Grouped Query Attention. If not specified, will default to `num_attention_heads`. head_dim (`int`, *optional*, defaults to 128): TODO hidden_act (`str` or `Callable`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the encoder and pooler. max_position_embeddings (`int`, *optional*, defaults to 131072): 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-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. pad_token_id (`int`, *optional*, defaults to 128004): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the beginning of sentence token. eos_token_id (`int`, *optional*, defaults to 2): The id of the end of sentence token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to `500000.0`): The base period of the RoPE embeddings. attention_dropout (`int`, *optional*, defaults to 0.0): TODO num_experts_per_tok (`int`, *optional*, defaults to 1): TODO num_local_experts (`int`, *optional*, defaults to 16): TODO moe_layers (`int`, *optional*): TODO interleave_moe_layer_step (`int`, *optional*, defaults to 1): TODO use_qk_norm (`int`, *optional*, defaults to `True`): TODO output_router_logits (`int`, *optional*, defaults to `False`): TODO router_aux_loss_coef (`int`, *optional*, defaults to 0.001): TODO router_jitter_noise (`int`, *optional*, defaults to 0.0): TODO 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 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. attention_chunk_size (`int`, *optional*, defaults to 8192): attn_temperature_tuning (`bool`, *optional*, defaults to `True`): Whether to dynamically scale the attention temperature for each query token based on sequence length. Recommended for long sequences (e.g., >32k tokens) to maintain stable output results. floor_scale (`int`, *optional*, defaults to 8192): TODO attn_scale (`int`, *optional*, defaults to 0.1): TODO cache_implementation (``, *optional*, defaults to `"hybrid"`): Example: Z llama4_textZpast_key_valueszlayers.*.self_attn.q_projrzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projrzlayers.*.input_layernorm.weightZsequence_parallelz(layers.*.post_attention_layernorm.weightz norm.weightz-layers.*.feed_forward.shared_expert.gate_projZ local_colwisez+layers.*.feed_forward.shared_expert.up_projz-layers.*.feed_forward.shared_expert.down_projZ local_rowwisez*layers.*.feed_forward.experts.gate_up_projZlocal_packed_rowwisez'layers.*.feed_forward.experts.down_projzlayers.*.feed_forward.expertslocalzlayers.*.feed_forward.gate_projzlayers.*.feed_forward.up_projzlayers.*.feed_forward.down_projzlayers.*.feed_forwardgather@ @0(silurrTNF rr MbP?皙?hybridc$& sBtjd||||d|$| |_|"|_|!|_||_| |_||_||_||_ ||_ ||_ ||_ d|_ |#|_|dur;|}||_| |_| |_| |_| |_||_||_|durV|n|j|j |_||_||_||_||_||_||_|gkrud}fddt|j D}%|r|n|%|_||_|dur|n t t|d|||_!||_"dS)N) pad_token_id bos_token_id eos_token_idtie_word_embeddingsFcs g|] }t|ddkqS)rL)r;).0Z layer_idxno_rope_layer_intervalr$r4 `sz-Llama4TextConfig.__init__..rLr$)#r.r/attn_temperature_tuning attn_scale floor_scale vocab_sizemax_position_embeddingsrrintermediate_size_mlprr rope_scalingZattention_biascache_implementationnum_key_value_headsrr# rms_norm_eps use_cacher-r*head_dim use_qk_normnum_experts_per_toknum_local_expertsoutput_router_logitsrouter_aux_loss_coefrouter_jitter_noiserangeno_rope_layersinterleave_moe_layer_steplist moe_layersattention_chunk_size)&r0r_rrrarrrdrgrr`r#rerfrSrTrUrVr-r*rirjrrrprhrkrlrmrbrorZrsr\r^r]rcr1Zdefault_no_rope_layersr2rYr4r/ sb'  zLlama4TextConfig.__init__)#rBrCrDrErFrGrHrIrJrKrrTNrLrMFrNrrLr NrLTFrOrNNrPrDTrDrQrR) r5r6r7r8r:Zkeys_to_ignore_at_inferencer9r/r>r$r$r2r4r?sr     r?csPeZdZdZdZddddZeedZdd iZ dfdd Z Z S) Llama4Configa This is the configuration class to store the configuration of a [`Llama4Model`]. It is used to instantiate an Llama4 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 Llama4 109B. e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (`Llama4VisionConfig`, *optional*): The Llama4 Vision config. text_config (`Llama4TextConfig`, *optional*): The Llama4 Text config. boi_token_index (`int`, *optional*, defaults to 200080): The begin-of-image token index to wrap the image prompt. eoi_token_index (`int`, *optional*, defaults to 200081): The end-of-image token index to wrap the image prompt. image_token_index (`int`, *optional*, defaults to 200092): The image token index to encode the image prompt. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. ```python >>> from transformers import Llama4Model, Llama4Config >>> # Initializing a Llama4 7B style configuration >>> configuration = Llama4Config() >>> # Initializing a model from the Llama4 7B style configuration >>> model = Llama4Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zllama4image_token_indexboi_token_indexeoi_token_index)Zimage_token_idZ boi_token_idZ eoi_token_id) text_configrzmulti_modal_projector.linear_1rN   Fc s|durt|_tdnt|trtdi||_nt|tr$||_||_||_||_|dur;t |_ tdnt|trIt di||_ nt|t rQ||_ t j dd|i|dS)Nz9vision_config is None, using default llama4 vision configz5text_config is None, using default llama4 text configrVr$) rrloggerinfo isinstancedictrvrwrur?rxr.r/)r0rrxrvrwrurVr1r2r$r4r/s$       zLlama4Config.__init__)NNryrzr{F) r5r6r7r8r:Z attribute_mapr?rZ sub_configsr9r/r>r$r$r2r4rtos"& rt)rtr?rN) Zconfiguration_utilsrutilsrZ get_loggerr5r|rr?rt__all__r$r$r$r4s   jm R