o پio@spddlmZGdddeZGdddeZGdddeZGdd d eZGd d d eZGd d d eZdS))PretrainedConfigc DeZdZdZdZdddddddd d d d gd d f fdd ZZS)Qwen3VLVisionConfigqwen3_vl vision_configgelu_pytorch_tanh r {Gz?c dtjdi|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_dSNsuper__init__depth hidden_size hidden_actintermediate_size num_heads in_channels patch_sizespatial_merge_sizetemporal_patch_sizeout_hidden_sizenum_position_embeddingsinitializer_rangedeepstack_visual_indexesselfrrrrrrr r!r"r#r$r&r%kwargs __class__rO/home/ubuntu/.local/lib/python3.10/site-packages/sglang/srt/configs/qwen3_vl.pyr zQwen3VLVisionConfig.__init____name__ __module__ __qualname__ model_typebase_config_keyr __classcell__rrr*r,r"rcsLeZdZdZdZdZ            dfdd ZZS)Qwen3VLTextConfiga This is the configuration class to store the configuration of a [`Qwen3VLTextModel`]. It is used to instantiate a Qwen3-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Qwen3-VL-4B-Instruct [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct). 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 151936): Vocabulary size of the Qwen3VL model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Qwen3VLModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 22016): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 32): 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 `32`. head_dim (`int`, *optional*, defaults to 128): The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`. 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 128000): 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 5000000.0): 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`, 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 Qwen3VLTextModel, Qwen3VLTextConfig >>> # Initializing a Qwen3VL style configuration >>> configuration = Qwen3VLTextConfig() >>> # Initializing a model from the Qwen3-VL-7B style configuration >>> model = Qwen3VLTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` qwen3_vl_text text_configQV silurư>TFSANc s||_| |_||_||_||_||_|dur|}||_||_||_| |_ | |_ | |_ ||_ ||_ ||_||_tjdd| i|dSNtie_word_embeddingsr) vocab_sizemax_position_embeddingsrrnum_hidden_layersnum_attention_headsnum_key_value_headshead_dimrr% rms_norm_eps use_cache rope_theta rope_scalingattention_biasattention_dropoutrr)r(rErrrGrHrIrJrrFr%rKrLrDrMrNrOrPr)r*rr,rs&zQwen3VLTextConfig.__init__)r9r:r;r<r<r<r=r>r?rr@TFrANFrB)r/r0r1__doc__r2r3rr4rrr*r,r6*s,dr6cDeZdZdZdZeedZdgZ      d fd d Z Z S) Qwen3VLConfiga This is the configuration class to store the configuration of a [`Qwen3VLModel`]. It is used to instantiate a Qwen3-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Qwen3-VL-4B-Instruct [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3VLTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3VLVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 151655): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 151656): The video token index to encode the image prompt. vision_start_token_id (`int`, *optional*, defaults to 151652): The start token index to encode the image prompt. vision_end_token_id (`int`, *optional*, defaults to 151653): The end token index to encode the image prompt. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie the word embeddings. ```python >>> from transformers import Qwen3VLForConditionalGeneration, Qwen3VLConfig >>> # Initializing a Qwen3-VL style configuration >>> configuration = Qwen3VLConfig() >>> # Initializing a model from the Qwen3-VL-4B style configuration >>> model = Qwen3VLForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```rrr8past_key_valuesNgPhPdPePFc  t|tr|jddi||_n |dur|jd|_t|tr-|jddi||_n |dur8|jd|_||_||_||_||_t j di|d|idSNrr8rDr isinstancedict sub_configsrr8image_token_idvideo_token_idvision_start_token_idvision_end_token_idrr r(r8rr`rarbrcrDr)r*rr,r  zQwen3VLConfig.__init__NNrVrWrXrYF) r/r0r1rQr2rr6r_keys_to_ignore_at_inferencerr4rrr*r,rS'rScseZdZdZdZdZdgZddddddddZdgd gfd d gd gfd gd gfd Z                      d!fdd Z Z S)"Qwen3VLMoeTextConfigaC This is the configuration class to store the configuration of a [`Qwen3VLMoeTextModel`]. It is used to instantiate a Qwen3-VL-MOE model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Qwen3-VL-30B-A3B-Instruct [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct). 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 151936): Vocabulary size of the Qwen2MoE model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Qwen2MoeModel`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 5632): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 24): 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 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. 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 128000): 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 5000000.0): 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. decoder_sparse_step (`int`, *optional*, defaults to 1): The frequency of the MoE layer. moe_intermediate_size (`int`, *optional*, defaults to 1408): Intermediate size of the routed expert. num_experts_per_tok (`int`, *optional*, defaults to 4): Number of selected experts. num_experts (`int`, *optional*, defaults to 60): Number of routed experts. norm_topk_prob (`bool`, *optional*, defaults to `True`): Whether to normalize the topk probabilities. mlp_only_layers (`List[int]`, *optional*, defaults to `[]`): Indicate which layers use Qwen3VLMoeMLP rather than Qwen3VLMoeSparseMoeBlock The list contains layer index, from 0 to num_layers-1 if we have num_layers layers If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity. 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 head_dim (`int`, *optional*): The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`. ```python >>> from transformers import Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeConfig >>> # Initializing a Qwen3VLMoe style configuration >>> configuration = Qwen3VLMoeConfig() >>> # Initializing a model from the Qwen3-VL-30B-A3B style configuration >>> model = Qwen3VLMoeForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```qwen3_vl_moe_textr8rUcolwiserowwise)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_tokenslayersnormr9rr r>r?rr@TFrArB<Nc s||_||_||_||_||_||_|dur|}||_||_| |_| |_ | |_ | |_ ||_ ||_ ||_|p8|||_||_||_||_||_||_|durOgn||_tjdd| i|dSrC)rErFrrrGrHrIrr%rKrLrMrOrPrNrJdecoder_sparse_stepmoe_intermediate_sizenum_experts_per_tok num_expertsnorm_topk_probmlp_only_layersrr)r(rErrrGrHrIrrFr%rKrLrDrMrOrPrzr{r|r}r~rrNrJr)r*rr,rs2zQwen3VLMoeTextConfig.__init__)r9rtrurr r r>r?rr@TFrAFrBrvrwrxryTNNN) r/r0r1rQr2r3rgbase_model_tp_planbase_model_pp_planrr4rrr*r,ri sRr  ric r)Qwen3VLMoeVisionConfig qwen3_vl_moerrrr r r r r rrrrc rrrr'r*rr,rr-zQwen3VLMoeVisionConfig.__init__r.rrr*r,rr5rcrR)Qwen3VLMoeConfiga This is the configuration class to store the configuration of a [`Qwen3VLMoeModel`]. It is used to instantiate a Qwen3-VL-MOE model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Qwen3-VL-30B-A3B-Instruct [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3VLMoeTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3VLMoeVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 151655): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 151656): The video token index to encode the image prompt. vision_start_token_id (`int`, *optional*, defaults to 151652): The start token index to encode the image prompt. vision_end_token_id (`int`, *optional*, defaults to 151653): The end token index to encode the image prompt. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie the word embeddings. ```python >>> from transformers import Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeConfig >>> # Initializing a Qwen3-VL-MOE style configuration >>> configuration = Qwen3VLMoeConfig() >>> # Initializing a model from the Qwen3-VL-30B-A3B style configuration >>> model = Qwen3VLMoeForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```rrTrUNrVrWrXrYFc  rZr[r\rdr*rr,r"rezQwen3VLMoeConfig.__init__rf) r/r0r1rQr2rrir_rgrr4rrr*r,rrhrN) transformersrrr6rSrirrrrrr,s &KC&