pyiqa.archs.q_align.configuration_mplug_owl2 ============================================ .. py:module:: pyiqa.archs.q_align.configuration_mplug_owl2 Module Contents --------------- .. py:data:: logger .. py:class:: LlamaConfig(vocab_size=32000, hidden_size=4096, intermediate_size=11008, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=None, hidden_act='silu', max_position_embeddings=2048, initializer_range=0.02, rms_norm_eps=1e-06, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=2, pretraining_tp=1, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, **kwargs) Bases: :py:obj:`transformers.configuration_utils.PretrainedConfig` This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA 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 LLaMA-7B. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. :param vocab_size: Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LlamaModel`] :type vocab_size: `int`, *optional*, defaults to 32000 :param hidden_size: Dimension of the hidden representations. :type hidden_size: `int`, *optional*, defaults to 4096 :param intermediate_size: Dimension of the MLP representations. :type intermediate_size: `int`, *optional*, defaults to 11008 :param num_hidden_layers: Number of hidden layers in the Transformer decoder. :type num_hidden_layers: `int`, *optional*, defaults to 32 :param num_attention_heads: Number of attention heads for each attention layer in the Transformer decoder. :type num_attention_heads: `int`, *optional*, defaults to 32 :param num_key_value_heads: 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 `num_attention_heads`. :type num_key_value_heads: `int`, *optional* :param hidden_act: The non-linear activation function (function or string) in the decoder. :type hidden_act: `str` or `function`, *optional*, defaults to `"silu"` :param max_position_embeddings: The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens, Llama 2 up to 4096, CodeLlama up to 16384. :type max_position_embeddings: `int`, *optional*, defaults to 2048 :param initializer_range: The standard deviation of the truncated_normal_initializer for initializing all weight matrices. :type initializer_range: `float`, *optional*, defaults to 0.02 :param rms_norm_eps: The epsilon used by the rms normalization layers. :type rms_norm_eps: `float`, *optional*, defaults to 1e-06 :param use_cache: Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. :type use_cache: `bool`, *optional*, defaults to `True` :param pad_token_id: Padding token id. :type pad_token_id: `int`, *optional* :param bos_token_id: Beginning of stream token id. :type bos_token_id: `int`, *optional*, defaults to 1 :param eos_token_id: End of stream token id. :type eos_token_id: `int`, *optional*, defaults to 2 :param pretraining_tp: Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). :type pretraining_tp: `int`, *optional*, defaults to 1 :param tie_word_embeddings: Whether to tie weight embeddings :type tie_word_embeddings: `bool`, *optional*, defaults to `False` :param rope_theta: The base period of the RoPE embeddings. :type rope_theta: `float`, *optional*, defaults to 10000.0 :param rope_scaling: Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. :type rope_scaling: `Dict`, *optional* :param attention_bias: Whether to use a bias in the query, key, value and output projection layers during self-attention. :type attention_bias: `bool`, defaults to `False`, *optional*, defaults to `False` ```python >>> from transformers import LlamaModel, LlamaConfig >>> # Initializing a LLaMA llama-7b style configuration >>> configuration = LlamaConfig() >>> # Initializing a model from the llama-7b style configuration >>> model = LlamaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` .. py:class:: MplugOwlVisionConfig(hidden_size=1024, intermediate_size=4096, projection_dim=768, num_hidden_layers=24, num_attention_heads=16, num_channels=3, image_size=448, patch_size=14, hidden_act='quick_gelu', layer_norm_eps=1e-06, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, use_flash_attn=False, **kwargs) Bases: :py:obj:`transformers.configuration_utils.PretrainedConfig` This is the configuration class to store the configuration of a [`MplugOwlVisionModel`]. It is used to instantiate a mPLUG-Owl vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration defaults will yield a similar configuration to that of the mPLUG-Owl [x-plug/x_plug-llama-7b](https://huggingface.co/x-plug/x_plug-llama-7b) architecture. 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. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"quick_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. layer_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). ``` .. py:method:: from_pretrained(pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> transformers.configuration_utils.PretrainedConfig :classmethod: .. py:class:: MplugOwlVisualAbstractorConfig(num_learnable_queries=64, hidden_size=1024, num_hidden_layers=6, num_attention_heads=16, intermediate_size=2816, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-06, encoder_hidden_size=1024, grid_size=None, **kwargs) Bases: :py:obj:`transformers.configuration_utils.PretrainedConfig` .. py:method:: from_pretrained(pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> transformers.configuration_utils.PretrainedConfig :classmethod: .. py:data:: DEFAULT_VISUAL_CONFIG .. py:class:: MPLUGOwl2Config(visual_config=None, **kwargs) Bases: :py:obj:`LlamaConfig` This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA 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 LLaMA-7B. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. :param vocab_size: Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LlamaModel`] :type vocab_size: `int`, *optional*, defaults to 32000 :param hidden_size: Dimension of the hidden representations. :type hidden_size: `int`, *optional*, defaults to 4096 :param intermediate_size: Dimension of the MLP representations. :type intermediate_size: `int`, *optional*, defaults to 11008 :param num_hidden_layers: Number of hidden layers in the Transformer decoder. :type num_hidden_layers: `int`, *optional*, defaults to 32 :param num_attention_heads: Number of attention heads for each attention layer in the Transformer decoder. :type num_attention_heads: `int`, *optional*, defaults to 32 :param num_key_value_heads: 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 `num_attention_heads`. :type num_key_value_heads: `int`, *optional* :param hidden_act: The non-linear activation function (function or string) in the decoder. :type hidden_act: `str` or `function`, *optional*, defaults to `"silu"` :param max_position_embeddings: The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens, Llama 2 up to 4096, CodeLlama up to 16384. :type max_position_embeddings: `int`, *optional*, defaults to 2048 :param initializer_range: The standard deviation of the truncated_normal_initializer for initializing all weight matrices. :type initializer_range: `float`, *optional*, defaults to 0.02 :param rms_norm_eps: The epsilon used by the rms normalization layers. :type rms_norm_eps: `float`, *optional*, defaults to 1e-06 :param use_cache: Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. :type use_cache: `bool`, *optional*, defaults to `True` :param pad_token_id: Padding token id. :type pad_token_id: `int`, *optional* :param bos_token_id: Beginning of stream token id. :type bos_token_id: `int`, *optional*, defaults to 1 :param eos_token_id: End of stream token id. :type eos_token_id: `int`, *optional*, defaults to 2 :param pretraining_tp: Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). :type pretraining_tp: `int`, *optional*, defaults to 1 :param tie_word_embeddings: Whether to tie weight embeddings :type tie_word_embeddings: `bool`, *optional*, defaults to `False` :param rope_theta: The base period of the RoPE embeddings. :type rope_theta: `float`, *optional*, defaults to 10000.0 :param rope_scaling: Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. :type rope_scaling: `Dict`, *optional* :param attention_bias: Whether to use a bias in the query, key, value and output projection layers during self-attention. :type attention_bias: `bool`, defaults to `False`, *optional*, defaults to `False` ```python >>> from transformers import LlamaModel, LlamaConfig >>> # Initializing a LLaMA llama-7b style configuration >>> configuration = LlamaConfig() >>> # Initializing a model from the llama-7b style configuration >>> model = LlamaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```