130 lines
5.3 KiB
Python
130 lines
5.3 KiB
Python
# coding=utf-8
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# Copyright 2024 Microsoft Research & University of Wisconsin-Madison and the 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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"""PaliGemmamodel configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ..auto import CONFIG_MAPPING, AutoConfig
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logger = logging.get_logger(__name__)
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class PaliGemmaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`PaliGemmaForConditionalGeneration`]. It is used to instantiate an
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PaliGemmamodel 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 PaliGemma-2B.
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e.g. [paligemma-hf/paligemma-2b](https://huggingface.co/paligemma-hf/paligemma-2b)
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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 (`PaliGemmaVisionConfig`, *optional*):
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Custom vision config or dict
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text_config (`Union[AutoConfig, dict]`, *optional*):
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The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
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image_token_index (`int`, *optional*, defaults to 256000):
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The image token index to encode the image prompt.
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vocab_size (`int`, *optional*, defaults to 257152):
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Vocabulary size of the PaliGemmamodel. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`~PaliGemmaForConditionalGeneration`]
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projection_dim (`int`, *optional*, defaults to 2048):
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Dimension of the multimodal projection space.
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden layer of the Language model.
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Example:
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```python
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>>> from transformers import PaliGemmaForConditionalGeneration, PaliGemmaConfig, SiglipVisionConfig, GemmaConfig
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>>> # Initializing a Siglip-like vision config
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>>> vision_config = SiglipVisionConfig()
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>>> # Initializing a PaliGemma config
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>>> text_config = GemmaConfig()
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>>> # Initializing a PaliGemma paligemma-3b-224 style configuration
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>>> configuration = PaliGemmaConfig(vision_config, text_config)
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>>> # Initializing a model from the paligemma-3b-224 style configuration
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>>> model = PaliGemmaForConditionalGeneration(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 = "paligemma"
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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": AutoConfig, "vision_config": AutoConfig}
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keys_to_ignore_at_inference = ["past_key_values"]
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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=256000,
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vocab_size=257152,
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projection_dim=2048,
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hidden_size=2048,
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**kwargs,
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):
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self.image_token_index = image_token_index
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self.projection_dim = projection_dim
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self.hidden_size = hidden_size
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self.vision_config = vision_config
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self.is_encoder_decoder = False
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if isinstance(self.vision_config, dict):
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vision_config["model_type"] = (
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vision_config["model_type"] if "model_type" in vision_config else "siglip_vision_model"
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)
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self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
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elif vision_config is None:
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self.vision_config = CONFIG_MAPPING["siglip_vision_model"](
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intermediate_size=4096,
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hidden_size=1152,
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patch_size=14,
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image_size=224,
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num_hidden_layers=27,
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num_attention_heads=16,
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vocab_size=257152,
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vision_use_head=False,
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)
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self.text_config = text_config
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if isinstance(self.text_config, dict):
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text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "gemma"
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self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
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elif text_config is None:
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self.text_config = CONFIG_MAPPING["gemma"](
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hidden_size=2048,
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num_hidden_layers=18,
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intermediate_size=16384,
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num_attention_heads=8,
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num_key_value_heads=1,
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is_encoder_decoder=False,
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vocab_size=vocab_size,
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)
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self.text_config.num_image_tokens = (self.vision_config.image_size // self.vision_config.patch_size) ** 2
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self.vision_config.projection_dim = projection_dim
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super().__init__(**kwargs)
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__all__ = ["PaliGemmaConfig"]
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