373 lines
18 KiB
Python
373 lines
18 KiB
Python
# coding=utf-8
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# Copyright 2024 HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Mllama model configuration"""
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from typing import Optional
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from ...configuration_utils import PretrainedConfig
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from ...modeling_rope_utils import rope_config_validation
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class MllamaVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MllamaVisionModel`]. It is used to instantiate an
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Mllama vision model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Mllama-11B.
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e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 1280):
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Dimensionality of the encoder layers and the pooler layer.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_global_layers (`int`, *optional*, defaults to 8):
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Number of global layers in the Transformer encoder.
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Vision model has a second transformer encoder, called global.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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Number of channels in the input image.
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intermediate_size (`int`, *optional*, defaults to 5120):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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vision_output_dim (`int`, *optional*, defaults to 7680):
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Dimensionality of the vision model output. Includes output of transformer
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encoder with intermediate layers and global transformer encoder.
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image_size (`int`, *optional*, defaults to 448):
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The size (resolution) of each image *tile*.
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patch_size (`int`, *optional*, defaults to 14):
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The size (resolution) of each patch.
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norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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max_num_tiles (`int`, *optional*, defaults to 4):
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Maximum number of tiles for image splitting.
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intermediate_layers_indices (`list[int]`, *optional*, defaults to [3, 7, 15, 23, 30]):
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Indices of intermediate layers of transformer encoder from which to extract and output features.
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These output features are concatenated with final hidden state of transformer encoder.
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supported_aspect_ratios (`list[list[int]]`, *optional*):
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List of supported aspect ratios for image splitting. If not specified, the default supported aspect ratios
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are [[1, 1], [1, 2], [1, 3], [1, 4], [2, 1], [2, 2], [3, 1], [4, 1]] for `max_num_tiles=4`.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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Example:
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```python
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>>> from transformers import MllamaVisionConfig, MllamaVisionModel
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>>> # Initializing a Llama config
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>>> config = MllamaVisionConfig()
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>>> # Initializing a vision model from the mllama-11b style configuration
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>>> model = MllamaVisionModel(config)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "mllama_vision_model"
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base_config_key = "vision_config"
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def __init__(
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self,
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hidden_size: int = 1280,
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hidden_act: str = "gelu",
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num_hidden_layers: int = 32,
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num_global_layers: int = 8,
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num_attention_heads: int = 16,
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num_channels: int = 3,
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intermediate_size: int = 5120,
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vision_output_dim: int = 7680,
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image_size: int = 448,
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patch_size: int = 14,
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norm_eps: float = 1e-5,
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max_num_tiles: int = 4,
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intermediate_layers_indices: Optional[list[int]] = None,
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supported_aspect_ratios: Optional[list[list[int]]] = None,
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initializer_range: float = 0.02,
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**kwargs,
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):
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if supported_aspect_ratios is None:
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if max_num_tiles != 4:
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raise ValueError("max_num_tiles must be 4 for default supported aspect ratios")
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supported_aspect_ratios = [[1, 1], [1, 2], [1, 3], [1, 4], [2, 1], [2, 2], [3, 1], [4, 1]]
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if intermediate_layers_indices is None:
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intermediate_layers_indices = [3, 7, 15, 23, 30]
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self.hidden_size = hidden_size
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self.hidden_act = hidden_act
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self.num_hidden_layers = num_hidden_layers
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self.num_channels = num_channels
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self.intermediate_size = intermediate_size
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self.image_size = image_size
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self.vision_output_dim = vision_output_dim
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self.patch_size = patch_size
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self.intermediate_layers_indices = intermediate_layers_indices
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self.num_global_layers = num_global_layers
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self.max_num_tiles = max_num_tiles
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self.norm_eps = norm_eps
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self.attention_heads = num_attention_heads
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self.supported_aspect_ratios = supported_aspect_ratios
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self.initializer_range = initializer_range
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super().__init__(**kwargs)
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@property
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def max_aspect_ratio_id(self) -> int:
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return len(self.supported_aspect_ratios)
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class MllamaTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MllamaTextModel`]. It is used to instantiate an
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Mllama text model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Mllama-11B.
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e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 128256):
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Vocabulary size of the Mllama text model. Defines the maximum number of different tokens that can be represented
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by the `inputs_ids` passed when calling [`MllamaTextModel`].
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimensionality of the embeddings and hidden states.
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hidden_act (`str` or `Callable`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the encoder and pooler.
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num_hidden_layers (`int`, *optional*, defaults to 40):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If not
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specified, will default to `num_attention_heads`.
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intermediate_size (`int`, *optional*, defaults to 14336):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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rope_theta (`float`, *optional*, defaults to `500000.0`):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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max_position_embeddings (`int`, *optional*, defaults to 131072):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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cross_attention_layers (`list[int]`, *optional*):
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Indices of the cross attention layers. If not specified, will default to [3, 8, 13, 18, 23, 28, 33, 38].
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dropout (`float`, *optional*, defaults to 0):
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The dropout probability for self- and cross-attention layers.
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bos_token_id (`int`, *optional*, defaults to 128000):
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The id of the beginning of sentence token.
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eos_token_id (`int`, *optional*, defaults to 128001):
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The id of the end of sentence token.
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pad_token_id (`int`, *optional*, defaults to 128004):
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The id of the padding token.
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Example:
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```python
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>>> from transformers import MllamaTextModel, MllamaTextConfig
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>>> # Initializing a Mllama text config
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>>> config = MllamaTextConfig()
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>>> # Initializing a model from the Mllama text configuration
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>>> model = MllamaTextModel(config)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "mllama_text_model"
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base_config_key = "text_config"
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def __init__(
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self,
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vocab_size: int = 128256,
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hidden_size: int = 4096,
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hidden_act: str = "silu",
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num_hidden_layers: int = 40,
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num_attention_heads: int = 32,
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num_key_value_heads: int = 8,
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intermediate_size: int = 14_336,
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rope_theta: float = 500_000,
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rope_scaling: Optional[dict] = None,
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rms_norm_eps: float = 1e-5,
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max_position_embeddings: int = 131_072,
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initializer_range: float = 0.02,
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use_cache: bool = True,
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tie_word_embeddings: bool = False,
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cross_attention_layers: Optional[list[int]] = None,
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dropout: float = 0,
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bos_token_id: int = 128000,
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eos_token_id: int = 128001,
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pad_token_id: Optional[int] = 128004,
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**kwargs,
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):
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if cross_attention_layers is None:
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cross_attention_layers = [3, 8, 13, 18, 23, 28, 33, 38]
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self.vocab_size = vocab_size
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self.num_hidden_layers = num_hidden_layers
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self.cross_attention_layers = cross_attention_layers
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rms_norm_eps = rms_norm_eps
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.hidden_act = hidden_act
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self.rope_scaling = rope_scaling
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self.max_position_embeddings = max_position_embeddings
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rope_config_validation(self)
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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class MllamaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MllamaForConditionalGeneration`]. It is used to instantiate an
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Mllama model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Mllama-9B.
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e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaVisionConfig`):
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The config object or dictionary of the vision backbone.
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text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaTextConfig`):
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The config object or dictionary of the text backbone.
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image_token_index (`int`, *optional*, defaults to 128256):
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The image token index to encode the image prompt.
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Example:
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```python
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>>> from transformers import MllamaForConditionalGeneration, MllamaConfig, MllamaVisionConfig, MllamaTextConfig
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>>> # Initializing a CLIP-vision config
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>>> vision_config = MllamaVisionConfig()
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>>> # Initializing a Llama config
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>>> text_config = MllamaTextConfig()
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>>> # Initializing a mllama-11b style configuration
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>>> configuration = MllamaConfig(vision_config, text_config)
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>>> # Initializing a model from the mllama-11b style configuration
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>>> model = MllamaForConditionalGeneration(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "mllama"
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attribute_map = {
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"image_token_id": "image_token_index",
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}
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sub_configs = {"text_config": MllamaTextConfig, "vision_config": MllamaVisionConfig}
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def __init__(
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self,
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vision_config=None,
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text_config=None,
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image_token_index=128256,
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**kwargs,
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):
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if vision_config is None:
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self.vision_config = MllamaVisionConfig()
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logger.info("vision_config is None, using default mllama vision config")
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elif isinstance(vision_config, dict):
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self.vision_config = MllamaVisionConfig(**vision_config)
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elif isinstance(vision_config, MllamaVisionConfig):
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self.vision_config = vision_config
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self.image_token_index = image_token_index
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if text_config is None:
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self.text_config = MllamaTextConfig()
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logger.info("text_config is None, using default mllama text config")
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elif isinstance(text_config, dict):
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self.text_config = MllamaTextConfig(**text_config)
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elif isinstance(text_config, MllamaTextConfig):
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self.text_config = text_config
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super().__init__(**kwargs)
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__all__ = ["MllamaConfig"]
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