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# coding=utf-8 | ||
# Copyright 2024 Microsoft Research & University of Wisconsin-Madison and the HuggingFace Inc. team. All rights reserved. | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
"""Mllama model configuration""" | ||
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import warnings | ||
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from ...configuration_utils import PretrainedConfig | ||
from ...utils import logging | ||
from ..auto import CONFIG_MAPPING | ||
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logger = logging.get_logger(__name__) | ||
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class MllamaConfig(PretrainedConfig): | ||
r""" | ||
This is the configuration class to store the configuration of a [`MllamaForConditionalGeneration`]. It is used to instantiate an | ||
Mllama 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 Mllama-9B. | ||
e.g. [mllama-hf/mllama-9b](https://huggingface.co/mllama-hf/mllama-9b) | ||
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 (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`): | ||
The config object or dictionary of the vision backbone. | ||
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`): | ||
The config object or dictionary of the text backbone. | ||
ignore_index (`int`, *optional*, defaults to -100): | ||
The ignore index for the loss function. | ||
image_token_index (`int`, *optional*, defaults to 32000): | ||
The image token index to encode the image prompt. | ||
projector_hidden_act (`str`, *optional*, defaults to `"gelu"`): | ||
The activation function used by the multimodal projector. | ||
vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`): | ||
The feature selection strategy used to select the vision feature from the vision backbone. | ||
Can be one of `"default"` or `"full"`. | ||
vision_feature_layer (`int`, *optional*, defaults to -2): | ||
The index of the layer to select the vision feature. | ||
Example: | ||
```python | ||
>>> from transformers import MllamaForConditionalGeneration, MllamaConfig, CLIPVisionConfig, LlamaConfig | ||
>>> # Initializing a CLIP-vision config | ||
>>> vision_config = CLIPVisionConfig() | ||
>>> # Initializing a Llama config | ||
>>> text_config = LlamaConfig() | ||
>>> # Initializing a Mllama mllama-1.5-7b style configuration | ||
>>> configuration = MllamaConfig(vision_config, text_config) | ||
>>> # Initializing a model from the mllama-1.5-7b style configuration | ||
>>> model = MllamaForConditionalGeneration(configuration) | ||
>>> # Accessing the model configuration | ||
>>> configuration = model.config | ||
```""" | ||
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model_type = "mllama" | ||
is_composition = False | ||
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def __init__( | ||
self, | ||
vision_config=None, | ||
global_vision_config=None, | ||
text_config=None, | ||
**kwargs, | ||
): | ||
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if isinstance(vision_config, dict): | ||
vision_config["model_type"] = ( | ||
vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model" | ||
) | ||
vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config) | ||
elif vision_config is None: | ||
vision_config = CONFIG_MAPPING["clip_vision_model"]( | ||
intermediate_size=4096, | ||
hidden_size=1024, | ||
patch_size=14, | ||
image_size=336, | ||
num_hidden_layers=32, | ||
num_attention_heads=16, | ||
vocab_size=32000, | ||
projection_dim=768, | ||
) | ||
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self.vision_config = vision_config | ||
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if isinstance(text_config, dict): | ||
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama" | ||
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config) | ||
elif text_config is None: | ||
text_config = CONFIG_MAPPING["llama"]( | ||
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) | ||
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self.text_config = text_config | ||
self._vocab_size = self.text_config.vocab_size | ||
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super().__init__(**kwargs) | ||
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@property | ||
def vocab_size(self): | ||
warnings.warn( | ||
"The `vocab_size` attribute is deprecated and will be removed in v4.42, Please use `text_config.vocab_size` instead.", | ||
FutureWarning, | ||
) | ||
return self._vocab_size | ||
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@vocab_size.setter | ||
def vocab_size(self, value): | ||
self._vocab_size = value | ||
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def to_dict(self): | ||
output = super().to_dict() | ||
output.pop("_vocab_size", None) | ||
return output |