pyiqa.archs.q_align.configuration_mplug_owl2

Module Contents

pyiqa.archs.q_align.configuration_mplug_owl2.logger[source]
class pyiqa.archs.q_align.configuration_mplug_owl2.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)[source]

Bases: 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.

Parameters:

  • vocab_size (int, optional, defaults to 32000) – Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [LlamaModel]

  • hidden_size (int, optional, defaults to 4096) – Dimension of the hidden representations.

  • intermediate_size (int, optional, defaults to 11008) – Dimension of the MLP representations.

  • num_hidden_layers (int, optional, defaults to 32) – Number of hidden layers in the Transformer decoder.

  • num_attention_heads (int, optional, defaults to 32) – Number of attention heads for each attention layer in the Transformer decoder.

  • num_key_value_heads (int, optional) – 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.

  • 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 2048) – 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.

  • 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) – Padding token id.

  • bos_token_id (int, optional, defaults to 1) – Beginning of stream token id.

  • eos_token_id (int, optional, defaults to 2) – End of stream token id.

  • pretraining_tp (int, optional, defaults to 1) – 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).

  • tie_word_embeddings (bool, optional, defaults to False) – Whether to tie weight embeddings

  • rope_theta (float, optional, defaults to 10000.0) – The base period of the RoPE embeddings.

  • rope_scaling (Dict, optional) – 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.

  • 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.

```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
```
class pyiqa.archs.q_align.configuration_mplug_owl2.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)[source]

Bases: 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).

```

classmethod from_pretrained(pretrained_model_name_or_path: str | os.PathLike, **kwargs) → transformers.configuration_utils.PretrainedConfig[source]
class pyiqa.archs.q_align.configuration_mplug_owl2.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)[source]

Bases: transformers.configuration_utils.PretrainedConfig

classmethod from_pretrained(pretrained_model_name_or_path: str | os.PathLike, **kwargs) → transformers.configuration_utils.PretrainedConfig[source]
pyiqa.archs.q_align.configuration_mplug_owl2.DEFAULT_VISUAL_CONFIG[source]
class pyiqa.archs.q_align.configuration_mplug_owl2.MPLUGOwl2Config(visual_config=None, **kwargs)[source]

Bases: 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.

Parameters:

  • vocab_size (int, optional, defaults to 32000) – Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [LlamaModel]

  • hidden_size (int, optional, defaults to 4096) – Dimension of the hidden representations.

  • intermediate_size (int, optional, defaults to 11008) – Dimension of the MLP representations.

  • num_hidden_layers (int, optional, defaults to 32) – Number of hidden layers in the Transformer decoder.

  • num_attention_heads (int, optional, defaults to 32) – Number of attention heads for each attention layer in the Transformer decoder.

  • num_key_value_heads (int, optional) – 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.

  • 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 2048) – 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.

  • 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) – Padding token id.

  • bos_token_id (int, optional, defaults to 1) – Beginning of stream token id.

  • eos_token_id (int, optional, defaults to 2) – End of stream token id.

  • pretraining_tp (int, optional, defaults to 1) – 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).

  • tie_word_embeddings (bool, optional, defaults to False) – Whether to tie weight embeddings

  • rope_theta (float, optional, defaults to 10000.0) – The base period of the RoPE embeddings.

  • rope_scaling (Dict, optional) – 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.

  • 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.

```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
```