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Uploading code
Browse files- configuration_intern_vit.py +119 -0
- configuration_nvlm_d.py +100 -0
- conversation.py +358 -0
- modeling_intern_vit.py +354 -0
- modeling_nvlm_d.py +434 -0
configuration_intern_vit.py
ADDED
@@ -0,0 +1,119 @@
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# --------------------------------------------------------
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+
# InternVL
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# Copyright (c) 2024 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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# --------------------------------------------------------
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import os
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from typing import Union
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class InternVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
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instantiate a vision encoder according to the specified arguments, defining the model architecture.
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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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num_channels (`int`, *optional*, defaults to 3):
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Number of color channels in the input images (e.g., 3 for RGB).
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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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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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qkv_bias (`bool`, *optional*, defaults to `False`):
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Whether to add a bias to the queries and values in the self-attention layers.
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hidden_size (`int`, *optional*, defaults to 3200):
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Dimensionality of the encoder layers and the pooler layer.
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num_attention_heads (`int`, *optional*, defaults to 25):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 12800):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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qk_normalization (`bool`, *optional*, defaults to `True`):
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Whether to normalize the queries and keys in the self-attention layers.
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num_hidden_layers (`int`, *optional*, defaults to 48):
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Number of hidden layers in the Transformer encoder.
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use_flash_attn (`bool`, *optional*, defaults to `True`):
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Whether to use flash attention mechanism.
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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"` ``"gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-6):
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The epsilon used by the layer normalization layers.
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dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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drop_path_rate (`float`, *optional*, defaults to 0.0):
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Dropout rate for stochastic depth.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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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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initializer_factor (`float`, *optional*, defaults to 0.1):
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A factor for layer scale.
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"""
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model_type = 'intern_vit_6b'
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def __init__(
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self,
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num_channels=3,
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patch_size=14,
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image_size=224,
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qkv_bias=False,
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hidden_size=3200,
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num_attention_heads=25,
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intermediate_size=12800,
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qk_normalization=True,
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num_hidden_layers=48,
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use_flash_attn=True,
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hidden_act='gelu',
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norm_type='rms_norm',
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layer_norm_eps=1e-6,
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dropout=0.0,
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drop_path_rate=0.0,
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attention_dropout=0.0,
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initializer_range=0.02,
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initializer_factor=0.1,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.drop_path_rate = drop_path_rate
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_channels = num_channels
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self.patch_size = patch_size
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self.image_size = image_size
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self.initializer_range = initializer_range
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self.initializer_factor = initializer_factor
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self.attention_dropout = attention_dropout
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.norm_type = norm_type
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self.qkv_bias = qkv_bias
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self.qk_normalization = qk_normalization
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self.use_flash_attn = use_flash_attn
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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if 'vision_config' in config_dict:
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config_dict = config_dict['vision_config']
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if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
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)
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return cls.from_dict(config_dict, **kwargs)
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configuration_nvlm_d.py
ADDED
@@ -0,0 +1,100 @@
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# --------------------------------------------------------
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# Adapted from https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B under MIT License
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# LICENSE is in incl_licenses directory.
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# --------------------------------------------------------
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import copy
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from transformers import AutoConfig, Qwen2Config
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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from .configuration_intern_vit import InternVisionConfig
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logger = logging.get_logger(__name__)
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class NVLM_D_Config(PretrainedConfig):
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model_type = 'NVLM_D'
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is_composition = True
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def __init__(
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self,
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vision_config=None,
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llm_config=None,
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use_backbone_lora=0,
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use_llm_lora=0,
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select_layer=-1,
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force_image_size=None,
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downsample_ratio=0.5,
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template=None,
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dynamic_image_size=False,
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use_thumbnail=False,
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ps_version='v1',
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min_dynamic_patch=1,
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max_dynamic_patch=6,
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**kwargs
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):
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super().__init__(**kwargs)
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+
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# Handle vision_config initialization
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if vision_config is None:
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vision_config = {}
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logger.info('vision_config is None. Initializing InternVisionConfig with default values.')
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+
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# Handle llm_config initialization
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if llm_config is None:
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llm_config = {}
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logger.info('llm_config is None. Initializing LLM Config with default values.')
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self.vision_config = InternVisionConfig(**vision_config)
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# Check for supported architecture
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if llm_config.get('architectures', [None])[0] == 'Qwen2ForCausalLM':
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self.llm_config = Qwen2Config(**llm_config)
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else:
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raise ValueError(f"Unsupported architecture: {llm_config.get('architectures', [None])[0]}")
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# Assign configuration values
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self.use_backbone_lora = use_backbone_lora
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self.use_llm_lora = use_llm_lora
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self.select_layer = select_layer
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self.force_image_size = force_image_size
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self.downsample_ratio = downsample_ratio
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self.template = template
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self.dynamic_image_size = dynamic_image_size
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self.use_thumbnail = use_thumbnail
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self.ps_version = ps_version # Pixel shuffle version
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self.min_dynamic_patch = min_dynamic_patch
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self.max_dynamic_patch = max_dynamic_patch
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# Log important parameters
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logger.info(f'vision_select_layer: {self.select_layer}')
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logger.info(f'ps_version: {self.ps_version}')
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logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
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logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')
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def to_dict(self):
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"""
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Serializes this instance to a Python dictionary. Overrides the default `PretrainedConfig.to_dict`.
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Returns:
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Dict[str, Any]: Dictionary of all the attributes that make up this configuration instance.
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"""
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output = copy.deepcopy(self.__dict__)
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output['vision_config'] = self.vision_config.to_dict()
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output['llm_config'] = self.llm_config.to_dict()
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output['model_type'] = self.model_type
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output['use_backbone_lora'] = self.use_backbone_lora
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output['use_llm_lora'] = self.use_llm_lora
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output['select_layer'] = self.select_layer
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output['force_image_size'] = self.force_image_size
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output['downsample_ratio'] = self.downsample_ratio
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output['template'] = self.template
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output['dynamic_image_size'] = self.dynamic_image_size
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output['use_thumbnail'] = self.use_thumbnail
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output['ps_version'] = self.ps_version
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output['min_dynamic_patch'] = self.min_dynamic_patch
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output['max_dynamic_patch'] = self.max_dynamic_patch
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+
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return output
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conversation.py
ADDED
@@ -0,0 +1,358 @@
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|
1 |
+
"""
|
2 |
+
Adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py under the Apache License 2.0.
|
3 |
+
LICENSE is in incl_licenses directory.
|
4 |
+
|
5 |
+
Conversation prompt templates.
|
6 |
+
|
7 |
+
We kindly request that you import fastchat instead of copying this file if you wish to use it.
|
8 |
+
If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
|
9 |
+
"""
|
10 |
+
|
11 |
+
import dataclasses
|
12 |
+
from enum import IntEnum, auto
|
13 |
+
from typing import Any, Dict, List, Tuple, Union
|
14 |
+
|
15 |
+
|
16 |
+
class SeparatorStyle(IntEnum):
|
17 |
+
"""Separator styles."""
|
18 |
+
|
19 |
+
ADD_COLON_SINGLE = auto()
|
20 |
+
ADD_COLON_TWO = auto()
|
21 |
+
ADD_COLON_SPACE_SINGLE = auto()
|
22 |
+
NO_COLON_SINGLE = auto()
|
23 |
+
NO_COLON_TWO = auto()
|
24 |
+
ADD_NEW_LINE_SINGLE = auto()
|
25 |
+
LLAMA2 = auto()
|
26 |
+
CHATGLM = auto()
|
27 |
+
CHATML = auto()
|
28 |
+
CHATINTERN = auto()
|
29 |
+
DOLLY = auto()
|
30 |
+
RWKV = auto()
|
31 |
+
PHOENIX = auto()
|
32 |
+
ROBIN = auto()
|
33 |
+
FALCON_CHAT = auto()
|
34 |
+
CHATGLM3 = auto()
|
35 |
+
INTERNVL_ZH = auto()
|
36 |
+
MPT = auto()
|
37 |
+
|
38 |
+
|
39 |
+
@dataclasses.dataclass
|
40 |
+
class Conversation:
|
41 |
+
"""A class that manages prompt templates and keeps all conversation history."""
|
42 |
+
|
43 |
+
# The name of this template
|
44 |
+
name: str
|
45 |
+
# The template of the system prompt
|
46 |
+
system_template: str = '{system_message}'
|
47 |
+
# The system message
|
48 |
+
system_message: str = ''
|
49 |
+
# The names of two roles
|
50 |
+
roles: Tuple[str] = ('USER', 'ASSISTANT')
|
51 |
+
# All messages. Each item is (role, message).
|
52 |
+
messages: List[List[str]] = ()
|
53 |
+
# The number of few shot examples
|
54 |
+
offset: int = 0
|
55 |
+
# The separator style and configurations
|
56 |
+
sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
|
57 |
+
sep: str = '\n'
|
58 |
+
sep2: str = None
|
59 |
+
# Stop criteria (the default one is EOS token)
|
60 |
+
stop_str: Union[str, List[str]] = None
|
61 |
+
# Stops generation if meeting any token in this list
|
62 |
+
stop_token_ids: List[int] = None
|
63 |
+
|
64 |
+
def get_prompt(self) -> str:
|
65 |
+
"""Get the prompt for generation."""
|
66 |
+
system_prompt = self.system_template.format(system_message=self.system_message)
|
67 |
+
if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
|
68 |
+
ret = system_prompt + self.sep
|
69 |
+
for role, message in self.messages:
|
70 |
+
if message:
|
71 |
+
ret += role + ': ' + message + self.sep
|
72 |
+
else:
|
73 |
+
ret += role + ':'
|
74 |
+
return ret
|
75 |
+
elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
|
76 |
+
seps = [self.sep, self.sep2]
|
77 |
+
ret = system_prompt + seps[0]
|
78 |
+
for i, (role, message) in enumerate(self.messages):
|
79 |
+
if message:
|
80 |
+
ret += role + ': ' + message + seps[i % 2]
|
81 |
+
else:
|
82 |
+
ret += role + ':'
|
83 |
+
return ret
|
84 |
+
elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
|
85 |
+
ret = system_prompt + self.sep
|
86 |
+
for role, message in self.messages:
|
87 |
+
if message:
|
88 |
+
ret += role + ': ' + message + self.sep
|
89 |
+
else:
|
90 |
+
ret += role + ': ' # must be end with a space
|
91 |
+
return ret
|
92 |
+
elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
|
93 |
+
ret = '' if system_prompt == '' else system_prompt + self.sep
|
94 |
+
for role, message in self.messages:
|
95 |
+
if message:
|
96 |
+
ret += role + '\n' + message + self.sep
|
97 |
+
else:
|
98 |
+
ret += role + '\n'
|
99 |
+
return ret
|
100 |
+
elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
|
101 |
+
ret = system_prompt
|
102 |
+
for role, message in self.messages:
|
103 |
+
if message:
|
104 |
+
ret += role + message + self.sep
|
105 |
+
else:
|
106 |
+
ret += role
|
107 |
+
return ret
|
108 |
+
elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
|
109 |
+
seps = [self.sep, self.sep2]
|
110 |
+
ret = system_prompt
|
111 |
+
for i, (role, message) in enumerate(self.messages):
|
112 |
+
if message:
|
113 |
+
ret += role + message + seps[i % 2]
|
114 |
+
else:
|
115 |
+
ret += role
|
116 |
+
return ret
|
117 |
+
elif self.sep_style == SeparatorStyle.RWKV:
|
118 |
+
ret = system_prompt
|
119 |
+
for i, (role, message) in enumerate(self.messages):
|
120 |
+
if message:
|
121 |
+
ret += (
|
122 |
+
role
|
123 |
+
+ ': '
|
124 |
+
+ message.replace('\r\n', '\n').replace('\n\n', '\n')
|
125 |
+
)
|
126 |
+
ret += '\n\n'
|
127 |
+
else:
|
128 |
+
ret += role + ':'
|
129 |
+
return ret
|
130 |
+
elif self.sep_style == SeparatorStyle.LLAMA2:
|
131 |
+
seps = [self.sep, self.sep2]
|
132 |
+
if self.system_message:
|
133 |
+
ret = system_prompt
|
134 |
+
else:
|
135 |
+
ret = '[INST] '
|
136 |
+
for i, (role, message) in enumerate(self.messages):
|
137 |
+
tag = self.roles[i % 2]
|
138 |
+
if message:
|
139 |
+
if i == 0:
|
140 |
+
ret += message + ' '
|
141 |
+
else:
|
142 |
+
ret += tag + ' ' + message + seps[i % 2]
|
143 |
+
else:
|
144 |
+
ret += tag
|
145 |
+
return ret
|
146 |
+
elif self.sep_style == SeparatorStyle.CHATGLM:
|
147 |
+
# source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
|
148 |
+
# source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
|
149 |
+
round_add_n = 1 if self.name == 'chatglm2' else 0
|
150 |
+
if system_prompt:
|
151 |
+
ret = system_prompt + self.sep
|
152 |
+
else:
|
153 |
+
ret = ''
|
154 |
+
|
155 |
+
for i, (role, message) in enumerate(self.messages):
|
156 |
+
if i % 2 == 0:
|
157 |
+
ret += f'[Round {i//2 + round_add_n}]{self.sep}'
|
158 |
+
|
159 |
+
if message:
|
160 |
+
ret += f'{role}:{message}{self.sep}'
|
161 |
+
else:
|
162 |
+
ret += f'{role}:'
|
163 |
+
return ret
|
164 |
+
elif self.sep_style == SeparatorStyle.CHATML:
|
165 |
+
ret = '' if system_prompt == '' else system_prompt + self.sep + '\n'
|
166 |
+
for role, message in self.messages:
|
167 |
+
if message:
|
168 |
+
ret += role + '\n' + message + self.sep + '\n'
|
169 |
+
else:
|
170 |
+
ret += role + '\n'
|
171 |
+
return ret
|
172 |
+
elif self.sep_style == SeparatorStyle.CHATGLM3:
|
173 |
+
ret = ''
|
174 |
+
if self.system_message:
|
175 |
+
ret += system_prompt
|
176 |
+
for role, message in self.messages:
|
177 |
+
if message:
|
178 |
+
ret += role + '\n' + ' ' + message
|
179 |
+
else:
|
180 |
+
ret += role
|
181 |
+
return ret
|
182 |
+
elif self.sep_style == SeparatorStyle.CHATINTERN:
|
183 |
+
# source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
|
184 |
+
seps = [self.sep, self.sep2]
|
185 |
+
ret = system_prompt
|
186 |
+
for i, (role, message) in enumerate(self.messages):
|
187 |
+
# if i % 2 == 0:
|
188 |
+
# ret += "<s>"
|
189 |
+
if message:
|
190 |
+
ret += role + ':' + message + seps[i % 2] + '\n'
|
191 |
+
else:
|
192 |
+
ret += role + ':'
|
193 |
+
return ret
|
194 |
+
elif self.sep_style == SeparatorStyle.DOLLY:
|
195 |
+
seps = [self.sep, self.sep2]
|
196 |
+
ret = system_prompt
|
197 |
+
for i, (role, message) in enumerate(self.messages):
|
198 |
+
if message:
|
199 |
+
ret += role + ':\n' + message + seps[i % 2]
|
200 |
+
if i % 2 == 1:
|
201 |
+
ret += '\n\n'
|
202 |
+
else:
|
203 |
+
ret += role + ':\n'
|
204 |
+
return ret
|
205 |
+
elif self.sep_style == SeparatorStyle.PHOENIX:
|
206 |
+
ret = system_prompt
|
207 |
+
for role, message in self.messages:
|
208 |
+
if message:
|
209 |
+
ret += role + ': ' + '<s>' + message + '</s>'
|
210 |
+
else:
|
211 |
+
ret += role + ': ' + '<s>'
|
212 |
+
return ret
|
213 |
+
elif self.sep_style == SeparatorStyle.ROBIN:
|
214 |
+
ret = system_prompt + self.sep
|
215 |
+
for role, message in self.messages:
|
216 |
+
if message:
|
217 |
+
ret += role + ':\n' + message + self.sep
|
218 |
+
else:
|
219 |
+
ret += role + ':\n'
|
220 |
+
return ret
|
221 |
+
elif self.sep_style == SeparatorStyle.FALCON_CHAT:
|
222 |
+
ret = ''
|
223 |
+
if self.system_message:
|
224 |
+
ret += system_prompt + self.sep
|
225 |
+
for role, message in self.messages:
|
226 |
+
if message:
|
227 |
+
ret += role + ': ' + message + self.sep
|
228 |
+
else:
|
229 |
+
ret += role + ':'
|
230 |
+
|
231 |
+
return ret
|
232 |
+
elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
|
233 |
+
seps = [self.sep, self.sep2]
|
234 |
+
ret = self.system_message + seps[0]
|
235 |
+
for i, (role, message) in enumerate(self.messages):
|
236 |
+
if message:
|
237 |
+
ret += role + ': ' + message + seps[i % 2]
|
238 |
+
else:
|
239 |
+
ret += role + ':'
|
240 |
+
return ret
|
241 |
+
elif self.sep_style == SeparatorStyle.MPT:
|
242 |
+
ret = system_prompt + self.sep
|
243 |
+
for role, message in self.messages:
|
244 |
+
if message:
|
245 |
+
if type(message) is tuple:
|
246 |
+
message, _, _ = message
|
247 |
+
ret += role + message + self.sep
|
248 |
+
else:
|
249 |
+
ret += role
|
250 |
+
return ret
|
251 |
+
else:
|
252 |
+
raise ValueError(f'Invalid style: {self.sep_style}')
|
253 |
+
|
254 |
+
def set_system_message(self, system_message: str):
|
255 |
+
"""Set the system message."""
|
256 |
+
self.system_message = system_message
|
257 |
+
|
258 |
+
def append_message(self, role: str, message: str):
|
259 |
+
"""Append a new message."""
|
260 |
+
self.messages.append([role, message])
|
261 |
+
|
262 |
+
def update_last_message(self, message: str):
|
263 |
+
"""Update the last output.
|
264 |
+
|
265 |
+
The last message is typically set to be None when constructing the prompt,
|
266 |
+
so we need to update it in-place after getting the response from a model.
|
267 |
+
"""
|
268 |
+
self.messages[-1][1] = message
|
269 |
+
|
270 |
+
def to_gradio_chatbot(self):
|
271 |
+
"""Convert the conversation to gradio chatbot format."""
|
272 |
+
ret = []
|
273 |
+
for i, (role, msg) in enumerate(self.messages[self.offset :]):
|
274 |
+
if i % 2 == 0:
|
275 |
+
ret.append([msg, None])
|
276 |
+
else:
|
277 |
+
ret[-1][-1] = msg
|
278 |
+
return ret
|
279 |
+
|
280 |
+
def to_openai_api_messages(self):
|
281 |
+
"""Convert the conversation to OpenAI chat completion format."""
|
282 |
+
ret = [{'role': 'system', 'content': self.system_message}]
|
283 |
+
|
284 |
+
for i, (_, msg) in enumerate(self.messages[self.offset :]):
|
285 |
+
if i % 2 == 0:
|
286 |
+
ret.append({'role': 'user', 'content': msg})
|
287 |
+
else:
|
288 |
+
if msg is not None:
|
289 |
+
ret.append({'role': 'assistant', 'content': msg})
|
290 |
+
return ret
|
291 |
+
|
292 |
+
def copy(self):
|
293 |
+
return Conversation(
|
294 |
+
name=self.name,
|
295 |
+
system_template=self.system_template,
|
296 |
+
system_message=self.system_message,
|
297 |
+
roles=self.roles,
|
298 |
+
messages=[[x, y] for x, y in self.messages],
|
299 |
+
offset=self.offset,
|
300 |
+
sep_style=self.sep_style,
|
301 |
+
sep=self.sep,
|
302 |
+
sep2=self.sep2,
|
303 |
+
stop_str=self.stop_str,
|
304 |
+
stop_token_ids=self.stop_token_ids,
|
305 |
+
)
|
306 |
+
|
307 |
+
def dict(self):
|
308 |
+
return {
|
309 |
+
'template_name': self.name,
|
310 |
+
'system_message': self.system_message,
|
311 |
+
'roles': self.roles,
|
312 |
+
'messages': self.messages,
|
313 |
+
'offset': self.offset,
|
314 |
+
}
|
315 |
+
|
316 |
+
|
317 |
+
# A global registry for all conversation templates
|
318 |
+
conv_templates: Dict[str, Conversation] = {}
|
319 |
+
|
320 |
+
|
321 |
+
def register_conv_template(template: Conversation, override: bool = False):
|
322 |
+
"""Register a new conversation template."""
|
323 |
+
if not override:
|
324 |
+
assert (
|
325 |
+
template.name not in conv_templates
|
326 |
+
), f'{template.name} has been registered.'
|
327 |
+
|
328 |
+
conv_templates[template.name] = template
|
329 |
+
|
330 |
+
|
331 |
+
def get_conv_template(name: str) -> Conversation:
|
332 |
+
"""Get a conversation template."""
|
333 |
+
return conv_templates[name].copy()
|
334 |
+
|
335 |
+
|
336 |
+
# Both Hermes-2 and internlm2-chat are chatml-format conversation templates. The difference
|
337 |
+
# is that during training, the preprocessing function for the Hermes-2 template doesn't add
|
338 |
+
# <s> at the beginning of the tokenized sequence, while the internlm2-chat template does.
|
339 |
+
# Therefore, they are completely equivalent during inference.
|
340 |
+
|
341 |
+
register_conv_template(
|
342 |
+
Conversation(
|
343 |
+
name='chatml',
|
344 |
+
system_template='<|im_start|>system\n{system_message}',
|
345 |
+
# note: The new system prompt was not used here to avoid changes in benchmark performance.
|
346 |
+
system_message='Answer the questions.',
|
347 |
+
roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
|
348 |
+
sep_style=SeparatorStyle.MPT,
|
349 |
+
sep='<|im_end|>',
|
350 |
+
stop_token_ids=[
|
351 |
+
2,
|
352 |
+
92543,
|
353 |
+
92542
|
354 |
+
]
|
355 |
+
)
|
356 |
+
)
|
357 |
+
|
358 |
+
|
modeling_intern_vit.py
ADDED
@@ -0,0 +1,354 @@
|
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|
1 |
+
# --------------------------------------------------------
|
2 |
+
# Adapted from https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B under MIT License
|
3 |
+
# LICENSE is in incl_licenses directory.
|
4 |
+
# --------------------------------------------------------
|
5 |
+
|
6 |
+
|
7 |
+
from typing import Optional, Tuple, Union
|
8 |
+
|
9 |
+
import torch
|
10 |
+
import torch.nn.functional as F
|
11 |
+
import torch.utils.checkpoint
|
12 |
+
from einops import rearrange
|
13 |
+
from timm.models.layers import DropPath
|
14 |
+
from torch import nn
|
15 |
+
from transformers.activations import ACT2FN
|
16 |
+
from transformers.modeling_outputs import (BaseModelOutput,
|
17 |
+
BaseModelOutputWithPooling)
|
18 |
+
from transformers.modeling_utils import PreTrainedModel
|
19 |
+
from transformers.utils import logging
|
20 |
+
|
21 |
+
from .configuration_intern_vit import InternVisionConfig
|
22 |
+
|
23 |
+
has_flash_attn = False
|
24 |
+
|
25 |
+
logger = logging.get_logger(__name__)
|
26 |
+
|
27 |
+
|
28 |
+
"""
|
29 |
+
The following code is adapted from the
|
30 |
+
https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B/blob/main/modeling_intern_vit.py repository
|
31 |
+
|
32 |
+
We added additional dummy heads to the original num of heads to make the number of heads divisible by 8
|
33 |
+
(tensor model parallel size) while having the same output as InternVIT.
|
34 |
+
We also turn off flash attn to have deterministic results.
|
35 |
+
"""
|
36 |
+
class InternRMSNorm(nn.Module):
|
37 |
+
def __init__(self, hidden_size, eps=1e-6):
|
38 |
+
super().__init__()
|
39 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
40 |
+
self.variance_epsilon = eps
|
41 |
+
|
42 |
+
def forward(self, hidden_states, var=None):
|
43 |
+
input_dtype = hidden_states.dtype
|
44 |
+
hidden_states = hidden_states.to(torch.float32)
|
45 |
+
if var is None:
|
46 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
47 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
48 |
+
else:
|
49 |
+
hidden_states = hidden_states * torch.rsqrt(var + self.variance_epsilon)
|
50 |
+
|
51 |
+
return hidden_states.to(input_dtype) * self.weight
|
52 |
+
|
53 |
+
|
54 |
+
class InternVisionEmbeddings(nn.Module):
|
55 |
+
def __init__(self, config: InternVisionConfig):
|
56 |
+
super().__init__()
|
57 |
+
self.config = config
|
58 |
+
self.embed_dim = config.hidden_size
|
59 |
+
self.image_size = config.image_size
|
60 |
+
self.patch_size = config.patch_size
|
61 |
+
|
62 |
+
self.class_embedding = nn.Parameter(
|
63 |
+
torch.randn(1, 1, self.embed_dim),
|
64 |
+
)
|
65 |
+
|
66 |
+
self.patch_embedding = nn.Conv2d(
|
67 |
+
in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
|
68 |
+
)
|
69 |
+
|
70 |
+
self.num_patches = (self.image_size // self.patch_size) ** 2
|
71 |
+
self.num_positions = self.num_patches + 1
|
72 |
+
|
73 |
+
self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
|
74 |
+
|
75 |
+
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
|
76 |
+
batch_size = pixel_values.shape[0]
|
77 |
+
target_dtype = self.patch_embedding.weight.dtype
|
78 |
+
patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
|
79 |
+
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
|
80 |
+
class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
|
81 |
+
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
|
82 |
+
embeddings = embeddings + self.position_embedding.to(target_dtype)
|
83 |
+
return embeddings
|
84 |
+
|
85 |
+
|
86 |
+
class InternAttention(nn.Module):
|
87 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
88 |
+
|
89 |
+
def __init__(self, config: InternVisionConfig):
|
90 |
+
super().__init__()
|
91 |
+
self.config = config
|
92 |
+
self.embed_dim = config.hidden_size
|
93 |
+
self.num_heads = config.num_attention_heads
|
94 |
+
self.use_flash_attn = config.use_flash_attn and has_flash_attn
|
95 |
+
if config.use_flash_attn and not has_flash_attn:
|
96 |
+
print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
|
97 |
+
|
98 |
+
self.head_dim = self.embed_dim // self.num_heads
|
99 |
+
if self.head_dim * self.num_heads != self.embed_dim:
|
100 |
+
raise ValueError(
|
101 |
+
f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
|
102 |
+
f' {self.num_heads}).'
|
103 |
+
)
|
104 |
+
|
105 |
+
self.scale = self.head_dim ** -0.5
|
106 |
+
# We added additional dummy heads to the original num of heads to make the number of heads divisible by 8.
|
107 |
+
self.num_dummy_heads = 7
|
108 |
+
self.dummy_dim = (self.num_dummy_heads + self.num_heads) * self.head_dim
|
109 |
+
self.qkv = nn.Linear(self.embed_dim, 3 * self.dummy_dim, bias=config.qkv_bias)
|
110 |
+
self.attn_drop = nn.Dropout(config.attention_dropout)
|
111 |
+
self.proj_drop = nn.Dropout(config.dropout)
|
112 |
+
|
113 |
+
self.qk_normalization = config.qk_normalization
|
114 |
+
|
115 |
+
if self.qk_normalization:
|
116 |
+
self.q_norm = InternRMSNorm(self.dummy_dim, eps=config.layer_norm_eps)
|
117 |
+
self.k_norm = InternRMSNorm(self.dummy_dim, eps=config.layer_norm_eps)
|
118 |
+
|
119 |
+
if self.use_flash_attn:
|
120 |
+
self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
|
121 |
+
self.proj = nn.Linear(self.dummy_dim, self.embed_dim)
|
122 |
+
|
123 |
+
def _naive_attn(self, x):
|
124 |
+
B, N, C = x.shape
|
125 |
+
|
126 |
+
qkv = torch.matmul(x, self.qkv.weight.t()).reshape(B, N, 3, self.num_dummy_heads + self.num_heads,
|
127 |
+
C // self.num_heads).permute(2, 0, 3, 1, 4)
|
128 |
+
|
129 |
+
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
|
130 |
+
|
131 |
+
if self.qk_normalization:
|
132 |
+
B_, H_, N_, D_ = q.shape
|
133 |
+
q_var = q.transpose(1, 2).flatten(-2, -1)[:, :, :self.embed_dim].float().pow(2).sum(-1,
|
134 |
+
keepdim=True) / self.embed_dim
|
135 |
+
k_var = k.transpose(1, 2).flatten(-2, -1)[:, :, :self.embed_dim].float().pow(2).sum(-1,
|
136 |
+
keepdim=True) / self.embed_dim
|
137 |
+
|
138 |
+
q = self.q_norm(q.transpose(1, 2).flatten(-2, -1), var=q_var).view(B_, N_, H_, D_).transpose(1, 2)
|
139 |
+
k = self.k_norm(k.transpose(1, 2).flatten(-2, -1), var=k_var).view(B_, N_, H_, D_).transpose(1, 2)
|
140 |
+
|
141 |
+
attn = ((q * self.scale) @ k.transpose(-2, -1))
|
142 |
+
attn = attn.softmax(dim=-1)
|
143 |
+
|
144 |
+
attn = self.attn_drop(attn)
|
145 |
+
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
|
146 |
+
|
147 |
+
x = torch.matmul(x, self.proj.weight.t()) + self.proj.bias
|
148 |
+
x = self.proj_drop(x)
|
149 |
+
return x
|
150 |
+
|
151 |
+
def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
|
152 |
+
qkv = self.qkv(x)
|
153 |
+
qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
|
154 |
+
|
155 |
+
if self.qk_normalization:
|
156 |
+
q, k, v = qkv.unbind(2)
|
157 |
+
q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
|
158 |
+
k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
|
159 |
+
qkv = torch.stack([q, k, v], dim=2)
|
160 |
+
|
161 |
+
context, _ = self.inner_attn(
|
162 |
+
qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
|
163 |
+
)
|
164 |
+
outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
|
165 |
+
outs = self.proj_drop(outs)
|
166 |
+
return outs
|
167 |
+
|
168 |
+
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
169 |
+
x = self._naive_attn(hidden_states)
|
170 |
+
return x
|
171 |
+
|
172 |
+
|
173 |
+
class InternMLP(nn.Module):
|
174 |
+
def __init__(self, config: InternVisionConfig):
|
175 |
+
super().__init__()
|
176 |
+
self.config = config
|
177 |
+
self.act = ACT2FN[config.hidden_act]
|
178 |
+
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
|
179 |
+
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
|
180 |
+
|
181 |
+
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
182 |
+
hidden_states = torch.matmul(hidden_states, self.fc1.weight.t()) + self.fc1.bias
|
183 |
+
hidden_states = self.act(hidden_states)
|
184 |
+
hidden_states = torch.matmul(hidden_states, self.fc2.weight.t()) + self.fc2.bias
|
185 |
+
return hidden_states
|
186 |
+
|
187 |
+
|
188 |
+
class InternVisionEncoderLayer(nn.Module):
|
189 |
+
def __init__(self, config: InternVisionConfig, drop_path_rate: float):
|
190 |
+
super().__init__()
|
191 |
+
self.embed_dim = config.hidden_size
|
192 |
+
self.intermediate_size = config.intermediate_size
|
193 |
+
self.norm_type = config.norm_type
|
194 |
+
|
195 |
+
self.attn = InternAttention(config)
|
196 |
+
self.mlp = InternMLP(config)
|
197 |
+
self.norm1 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
|
198 |
+
self.norm2 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
|
199 |
+
|
200 |
+
self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
|
201 |
+
self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
|
202 |
+
self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
|
203 |
+
self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
|
204 |
+
|
205 |
+
def forward(
|
206 |
+
self,
|
207 |
+
hidden_states: torch.Tensor,
|
208 |
+
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
|
209 |
+
"""
|
210 |
+
Args:
|
211 |
+
hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
212 |
+
"""
|
213 |
+
hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1)
|
214 |
+
|
215 |
+
hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2)
|
216 |
+
|
217 |
+
return hidden_states
|
218 |
+
|
219 |
+
|
220 |
+
class InternVisionEncoder(nn.Module):
|
221 |
+
"""
|
222 |
+
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
223 |
+
[`InternEncoderLayer`].
|
224 |
+
|
225 |
+
Args:
|
226 |
+
config (`InternConfig`):
|
227 |
+
The corresponding vision configuration for the `InternEncoder`.
|
228 |
+
"""
|
229 |
+
|
230 |
+
def __init__(self, config: InternVisionConfig):
|
231 |
+
super().__init__()
|
232 |
+
self.config = config
|
233 |
+
# stochastic depth decay rule
|
234 |
+
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
|
235 |
+
self.layers = nn.ModuleList([
|
236 |
+
InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
|
237 |
+
self.gradient_checkpointing = True
|
238 |
+
|
239 |
+
def forward(
|
240 |
+
self,
|
241 |
+
inputs_embeds,
|
242 |
+
output_hidden_states: Optional[bool] = None,
|
243 |
+
return_dict: Optional[bool] = None,
|
244 |
+
) -> Union[Tuple, BaseModelOutput]:
|
245 |
+
r"""
|
246 |
+
Args:
|
247 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
248 |
+
Embedded representation of the inputs. Should be float, not int tokens.
|
249 |
+
output_hidden_states (`bool`, *optional*):
|
250 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
251 |
+
for more detail.
|
252 |
+
return_dict (`bool`, *optional*):
|
253 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
254 |
+
"""
|
255 |
+
output_hidden_states = (
|
256 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
257 |
+
)
|
258 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
259 |
+
|
260 |
+
encoder_states = () if output_hidden_states else None
|
261 |
+
hidden_states = inputs_embeds
|
262 |
+
|
263 |
+
for idx, encoder_layer in enumerate(self.layers):
|
264 |
+
if output_hidden_states:
|
265 |
+
encoder_states = encoder_states + (hidden_states,)
|
266 |
+
if self.gradient_checkpointing and self.training:
|
267 |
+
layer_outputs = torch.utils.checkpoint.checkpoint(
|
268 |
+
encoder_layer,
|
269 |
+
hidden_states)
|
270 |
+
else:
|
271 |
+
layer_outputs = encoder_layer(
|
272 |
+
hidden_states,
|
273 |
+
)
|
274 |
+
hidden_states = layer_outputs
|
275 |
+
|
276 |
+
if output_hidden_states:
|
277 |
+
encoder_states = encoder_states + (hidden_states,)
|
278 |
+
|
279 |
+
if not return_dict:
|
280 |
+
return tuple(v for v in [hidden_states, encoder_states] if v is not None)
|
281 |
+
return BaseModelOutput(
|
282 |
+
last_hidden_state=hidden_states, hidden_states=encoder_states
|
283 |
+
)
|
284 |
+
|
285 |
+
|
286 |
+
class InternVisionModel(PreTrainedModel):
|
287 |
+
main_input_name = 'pixel_values'
|
288 |
+
_supports_flash_attn_2 = True
|
289 |
+
config_class = InternVisionConfig
|
290 |
+
_no_split_modules = ['InternVisionEncoderLayer']
|
291 |
+
|
292 |
+
def __init__(self, config: InternVisionConfig):
|
293 |
+
super().__init__(config)
|
294 |
+
self.config = config
|
295 |
+
|
296 |
+
self.embeddings = InternVisionEmbeddings(config)
|
297 |
+
self.encoder = InternVisionEncoder(config)
|
298 |
+
|
299 |
+
def resize_pos_embeddings(self, old_size, new_size, patch_size):
|
300 |
+
pos_emb = self.embeddings.position_embedding
|
301 |
+
_, num_positions, embed_dim = pos_emb.shape
|
302 |
+
cls_emb = pos_emb[:, :1, :]
|
303 |
+
pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
|
304 |
+
pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
|
305 |
+
pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
|
306 |
+
pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
|
307 |
+
self.embeddings.position_embedding = nn.Parameter(pos_emb)
|
308 |
+
self.embeddings.image_size = new_size
|
309 |
+
logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
|
310 |
+
|
311 |
+
def get_input_embeddings(self):
|
312 |
+
return self.embeddings
|
313 |
+
|
314 |
+
def forward(
|
315 |
+
self,
|
316 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
317 |
+
output_hidden_states: Optional[bool] = None,
|
318 |
+
return_dict: Optional[bool] = None,
|
319 |
+
pixel_embeds: Optional[torch.FloatTensor] = None,
|
320 |
+
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
321 |
+
output_hidden_states = (
|
322 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
323 |
+
)
|
324 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
325 |
+
|
326 |
+
if pixel_values is None and pixel_embeds is None:
|
327 |
+
raise ValueError('You have to specify pixel_values or pixel_embeds')
|
328 |
+
|
329 |
+
if pixel_embeds is not None:
|
330 |
+
hidden_states = pixel_embeds
|
331 |
+
else:
|
332 |
+
if len(pixel_values.shape) == 4:
|
333 |
+
hidden_states = self.embeddings(pixel_values)
|
334 |
+
else:
|
335 |
+
raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
|
336 |
+
encoder_outputs = self.encoder(
|
337 |
+
inputs_embeds=hidden_states,
|
338 |
+
output_hidden_states=output_hidden_states,
|
339 |
+
return_dict=return_dict,
|
340 |
+
)
|
341 |
+
|
342 |
+
last_hidden_state = encoder_outputs.last_hidden_state
|
343 |
+
|
344 |
+
pooled_output = last_hidden_state[:, 0, :]
|
345 |
+
|
346 |
+
if not return_dict:
|
347 |
+
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
|
348 |
+
|
349 |
+
return BaseModelOutputWithPooling(
|
350 |
+
last_hidden_state=last_hidden_state,
|
351 |
+
pooler_output=pooled_output,
|
352 |
+
hidden_states=encoder_outputs.hidden_states,
|
353 |
+
attentions=encoder_outputs.attentions,
|
354 |
+
)
|
modeling_nvlm_d.py
ADDED
@@ -0,0 +1,434 @@
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|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# --------------------------------------------------------
|
2 |
+
# Adapted from https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B under MIT License
|
3 |
+
# LICENSE is in incl_licenses directory.
|
4 |
+
# --------------------------------------------------------
|
5 |
+
|
6 |
+
|
7 |
+
import warnings
|
8 |
+
from typing import Any, List, Optional, Tuple, Union
|
9 |
+
|
10 |
+
import torch.utils.checkpoint
|
11 |
+
import transformers
|
12 |
+
from torch import nn
|
13 |
+
from torch.nn import CrossEntropyLoss
|
14 |
+
from transformers import AutoModel, GenerationConfig, Qwen2ForCausalLM
|
15 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
|
16 |
+
from transformers.modeling_utils import PreTrainedModel
|
17 |
+
from transformers.utils import ModelOutput, logging
|
18 |
+
|
19 |
+
from .configuration_nvlm_d import NVLM_D_Config
|
20 |
+
from .conversation import get_conv_template
|
21 |
+
from .modeling_intern_vit import InternVisionModel, has_flash_attn
|
22 |
+
|
23 |
+
logger = logging.get_logger(__name__)
|
24 |
+
|
25 |
+
|
26 |
+
"""
|
27 |
+
The following code is adapted from the
|
28 |
+
https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B/blob/main/modeling_internvl_chat.py repository
|
29 |
+
|
30 |
+
The chat function is adapted to handle NVLM 1-D tile-tagging design for dynamic high-resolution images.
|
31 |
+
"""
|
32 |
+
def version_cmp(v1, v2, op='eq'):
|
33 |
+
import operator
|
34 |
+
|
35 |
+
from packaging import version
|
36 |
+
op_func = getattr(operator, op)
|
37 |
+
return op_func(version.parse(v1), version.parse(v2))
|
38 |
+
|
39 |
+
|
40 |
+
class NVLM_D_Model(PreTrainedModel):
|
41 |
+
config_class = NVLM_D_Config
|
42 |
+
main_input_name = 'pixel_values'
|
43 |
+
_supports_flash_attn_2 = True
|
44 |
+
_no_split_modules = ['InternVisionModel', 'Qwen2DecoderLayer']
|
45 |
+
|
46 |
+
def __init__(self, config: NVLM_D_Config, vision_model=None, language_model=None, use_flash_attn=True):
|
47 |
+
super().__init__(config)
|
48 |
+
|
49 |
+
assert version_cmp(transformers.__version__, '4.36.2', 'ge')
|
50 |
+
image_size = config.force_image_size or config.vision_config.image_size
|
51 |
+
patch_size = config.vision_config.patch_size
|
52 |
+
self.patch_size = patch_size
|
53 |
+
self.select_layer = config.select_layer
|
54 |
+
self.template = config.template
|
55 |
+
self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
|
56 |
+
self.downsample_ratio = config.downsample_ratio
|
57 |
+
self.ps_version = config.ps_version
|
58 |
+
use_flash_attn = use_flash_attn if has_flash_attn else False
|
59 |
+
config.vision_config.use_flash_attn = True if use_flash_attn else False
|
60 |
+
config.llm_config._attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
|
61 |
+
|
62 |
+
logger.info(f'num_image_token: {self.num_image_token}')
|
63 |
+
logger.info(f'ps_version: {self.ps_version}')
|
64 |
+
if vision_model is not None:
|
65 |
+
self.vision_model = vision_model
|
66 |
+
else:
|
67 |
+
self.vision_model = InternVisionModel(config.vision_config)
|
68 |
+
if language_model is not None:
|
69 |
+
self.language_model = language_model
|
70 |
+
else:
|
71 |
+
if config.llm_config.architectures[0] == 'Qwen2ForCausalLM':
|
72 |
+
self.language_model = Qwen2ForCausalLM(config.llm_config)
|
73 |
+
else:
|
74 |
+
raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
|
75 |
+
|
76 |
+
vit_hidden_size = config.vision_config.hidden_size
|
77 |
+
llm_intermediate_size = config.llm_config.intermediate_size
|
78 |
+
llm_hidden_size = config.llm_config.hidden_size
|
79 |
+
|
80 |
+
self.mlp1 = nn.Sequential(
|
81 |
+
nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
|
82 |
+
nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_intermediate_size, bias=False),
|
83 |
+
nn.GELU(),
|
84 |
+
nn.Linear(llm_intermediate_size, llm_hidden_size, bias=False)
|
85 |
+
)
|
86 |
+
|
87 |
+
self.img_context_token_id = None
|
88 |
+
self.conv_template = get_conv_template(self.template)
|
89 |
+
self.system_message = self.conv_template.system_message
|
90 |
+
|
91 |
+
def forward(
|
92 |
+
self,
|
93 |
+
pixel_values: torch.FloatTensor,
|
94 |
+
input_ids: torch.LongTensor = None,
|
95 |
+
attention_mask: Optional[torch.Tensor] = None,
|
96 |
+
position_ids: Optional[torch.LongTensor] = None,
|
97 |
+
image_flags: Optional[torch.LongTensor] = None,
|
98 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
99 |
+
labels: Optional[torch.LongTensor] = None,
|
100 |
+
use_cache: Optional[bool] = None,
|
101 |
+
output_attentions: Optional[bool] = None,
|
102 |
+
output_hidden_states: Optional[bool] = None,
|
103 |
+
return_dict: Optional[bool] = None,
|
104 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
105 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
106 |
+
|
107 |
+
image_flags = image_flags.squeeze(-1)
|
108 |
+
input_embeds = self.language_model.get_input_embeddings()(input_ids)
|
109 |
+
|
110 |
+
vit_embeds = self.extract_feature(pixel_values)
|
111 |
+
vit_embeds = vit_embeds[image_flags == 1]
|
112 |
+
vit_batch_size = pixel_values.shape[0]
|
113 |
+
|
114 |
+
B, N, C = input_embeds.shape
|
115 |
+
input_embeds = input_embeds.reshape(B * N, C)
|
116 |
+
|
117 |
+
if torch.distributed.get_rank() == 0:
|
118 |
+
print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
|
119 |
+
|
120 |
+
input_ids = input_ids.reshape(B * N)
|
121 |
+
selected = (input_ids == self.img_context_token_id)
|
122 |
+
try:
|
123 |
+
input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
|
124 |
+
except Exception as e:
|
125 |
+
vit_embeds = vit_embeds.reshape(-1, C)
|
126 |
+
print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
|
127 |
+
f'vit_embeds.shape={vit_embeds.shape}')
|
128 |
+
n_token = selected.sum()
|
129 |
+
input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
|
130 |
+
|
131 |
+
input_embeds = input_embeds.reshape(B, N, C)
|
132 |
+
|
133 |
+
outputs = self.language_model(
|
134 |
+
inputs_embeds=input_embeds,
|
135 |
+
attention_mask=attention_mask,
|
136 |
+
position_ids=position_ids,
|
137 |
+
past_key_values=past_key_values,
|
138 |
+
use_cache=use_cache,
|
139 |
+
output_attentions=output_attentions,
|
140 |
+
output_hidden_states=output_hidden_states,
|
141 |
+
return_dict=return_dict,
|
142 |
+
)
|
143 |
+
logits = outputs.logits
|
144 |
+
|
145 |
+
loss = None
|
146 |
+
if labels is not None:
|
147 |
+
# Shift so that tokens < n predict n
|
148 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
149 |
+
shift_labels = labels[..., 1:].contiguous()
|
150 |
+
# Flatten the tokens
|
151 |
+
loss_fct = CrossEntropyLoss()
|
152 |
+
shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
|
153 |
+
shift_labels = shift_labels.view(-1)
|
154 |
+
# Enable model parallelism
|
155 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
156 |
+
loss = loss_fct(shift_logits, shift_labels)
|
157 |
+
|
158 |
+
if not return_dict:
|
159 |
+
output = (logits,) + outputs[1:]
|
160 |
+
return (loss,) + output if loss is not None else output
|
161 |
+
|
162 |
+
return CausalLMOutputWithPast(
|
163 |
+
loss=loss,
|
164 |
+
logits=logits,
|
165 |
+
past_key_values=outputs.past_key_values,
|
166 |
+
hidden_states=outputs.hidden_states,
|
167 |
+
attentions=outputs.attentions,
|
168 |
+
)
|
169 |
+
|
170 |
+
def pixel_shuffle(self, x, scale_factor=0.5):
|
171 |
+
n, w, h, c = x.size()
|
172 |
+
# N, W, H, C --> N, W, H * scale, C // scale
|
173 |
+
x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
|
174 |
+
# N, W, H * scale, C // scale --> N, H * scale, W, C // scale
|
175 |
+
x = x.permute(0, 2, 1, 3).contiguous()
|
176 |
+
# N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
|
177 |
+
x = x.view(n, int(h * scale_factor), int(w * scale_factor),
|
178 |
+
int(c / (scale_factor * scale_factor)))
|
179 |
+
if self.ps_version == 'v1':
|
180 |
+
warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
|
181 |
+
'which results in a transposed image.')
|
182 |
+
else:
|
183 |
+
x = x.permute(0, 2, 1, 3).contiguous()
|
184 |
+
return x
|
185 |
+
|
186 |
+
def extract_feature(self, pixel_values):
|
187 |
+
if self.select_layer == -1:
|
188 |
+
vit_embeds = self.vision_model(
|
189 |
+
pixel_values=pixel_values,
|
190 |
+
output_hidden_states=False,
|
191 |
+
return_dict=True).last_hidden_state
|
192 |
+
else:
|
193 |
+
vit_embeds = self.vision_model(
|
194 |
+
pixel_values=pixel_values,
|
195 |
+
output_hidden_states=True,
|
196 |
+
return_dict=True).hidden_states[self.select_layer]
|
197 |
+
vit_embeds = vit_embeds[:, 1:, :]
|
198 |
+
|
199 |
+
h = w = int(vit_embeds.shape[1] ** 0.5)
|
200 |
+
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
|
201 |
+
vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
|
202 |
+
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
|
203 |
+
vit_embeds = self.mlp1(vit_embeds)
|
204 |
+
return vit_embeds
|
205 |
+
|
206 |
+
|
207 |
+
"""
|
208 |
+
Adapts the chat function to handle NVLM 1-D tile-tagging design for dynamic high-resolution images.
|
209 |
+
Additionally, it supports the following:
|
210 |
+
- Chat without a system prompt.
|
211 |
+
- Chat without an image prompt.
|
212 |
+
"""
|
213 |
+
def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
|
214 |
+
num_patches_list=None, IMG_START_TOKEN='<|vision_start|>', IMG_END_TOKEN='<|vision_end|>',
|
215 |
+
IMG_CONTEXT_TOKEN='<|vision_pad|>', verbose=False, visual_features=None):
|
216 |
+
|
217 |
+
if history is None and pixel_values is not None and '<image>' not in question:
|
218 |
+
question = '<image>\n' + question
|
219 |
+
|
220 |
+
if num_patches_list is None:
|
221 |
+
num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
|
222 |
+
assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
|
223 |
+
|
224 |
+
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
|
225 |
+
self.img_context_token_id = img_context_token_id
|
226 |
+
|
227 |
+
template = get_conv_template(self.template)
|
228 |
+
template.system_message = self.system_message
|
229 |
+
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
|
230 |
+
|
231 |
+
history = [] if history is None else history
|
232 |
+
for (old_question, old_answer) in history:
|
233 |
+
template.append_message(template.roles[0], old_question)
|
234 |
+
template.append_message(template.roles[1], old_answer)
|
235 |
+
template.append_message(template.roles[0], question)
|
236 |
+
template.append_message(template.roles[1], None)
|
237 |
+
query = template.get_prompt()
|
238 |
+
|
239 |
+
if verbose and pixel_values is not None:
|
240 |
+
image_bs = pixel_values.shape[0]
|
241 |
+
print(f'dynamic ViT batch size: {image_bs}')
|
242 |
+
|
243 |
+
for num_patches in num_patches_list:
|
244 |
+
tile_pos_identifiers = [f"<tile_{i}>" for i in range(1, num_patches)] + ["<tile_global_thumbnail>"]
|
245 |
+
image_tokens = ''
|
246 |
+
for tile_pos_identifier in tile_pos_identifiers:
|
247 |
+
image_tokens += tile_pos_identifier + IMG_CONTEXT_TOKEN * self.num_image_token
|
248 |
+
image_tokens = '<Image>' + image_tokens + '</Image>'
|
249 |
+
query = query.replace('<image>', image_tokens, 1)
|
250 |
+
|
251 |
+
model_inputs = tokenizer(query, return_tensors='pt')
|
252 |
+
input_ids = model_inputs['input_ids'].cuda()
|
253 |
+
attention_mask = model_inputs['attention_mask'].cuda()
|
254 |
+
generation_config['eos_token_id'] = eos_token_id
|
255 |
+
generation_output = self.generate(
|
256 |
+
pixel_values=pixel_values,
|
257 |
+
visual_features=visual_features,
|
258 |
+
input_ids=input_ids,
|
259 |
+
attention_mask=attention_mask,
|
260 |
+
**generation_config
|
261 |
+
)
|
262 |
+
response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
|
263 |
+
response = response.split(template.sep)[0].strip()
|
264 |
+
history.append((question, response))
|
265 |
+
if return_history:
|
266 |
+
return response, history
|
267 |
+
else:
|
268 |
+
query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
|
269 |
+
query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
|
270 |
+
if verbose:
|
271 |
+
print(query_to_print, response)
|
272 |
+
return response
|
273 |
+
|
274 |
+
def chat_without_sys_prompt(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
|
275 |
+
num_patches_list=None, IMG_START_TOKEN='<|vision_start|>', IMG_END_TOKEN='<|vision_end|>',
|
276 |
+
IMG_CONTEXT_TOKEN='<|vision_pad|>', verbose=False, visual_features=None):
|
277 |
+
|
278 |
+
if history is None and pixel_values is not None and '<image>' not in question:
|
279 |
+
question = '<image>\n' + question
|
280 |
+
|
281 |
+
if num_patches_list is None:
|
282 |
+
num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
|
283 |
+
assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
|
284 |
+
|
285 |
+
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
|
286 |
+
self.img_context_token_id = img_context_token_id
|
287 |
+
|
288 |
+
template = get_conv_template(self.template)
|
289 |
+
system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>" # override dummy system prompt
|
290 |
+
template.system_message = system_prompt
|
291 |
+
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
|
292 |
+
|
293 |
+
history = [] if history is None else history
|
294 |
+
for (old_question, old_answer) in history:
|
295 |
+
template.append_message(template.roles[0], old_question)
|
296 |
+
template.append_message(template.roles[1], old_answer)
|
297 |
+
template.append_message(template.roles[0], question)
|
298 |
+
template.append_message(template.roles[1], None)
|
299 |
+
query = template.get_prompt()
|
300 |
+
|
301 |
+
if verbose and pixel_values is not None:
|
302 |
+
image_bs = pixel_values.shape[0]
|
303 |
+
print(f'dynamic ViT batch size: {image_bs}')
|
304 |
+
|
305 |
+
query = query[len(system_prompt):]
|
306 |
+
|
307 |
+
for num_patches in num_patches_list:
|
308 |
+
tile_pos_identifiers = [f"<tile_{i}>" for i in range(1, num_patches)] + ["<tile_global_thumbnail>"]
|
309 |
+
image_tokens = ''
|
310 |
+
for tile_pos_identifier in tile_pos_identifiers:
|
311 |
+
image_tokens += tile_pos_identifier + IMG_CONTEXT_TOKEN * self.num_image_token
|
312 |
+
image_tokens = '<Image>' + image_tokens + '</Image>'
|
313 |
+
query = query.replace('<image>', image_tokens, 1)
|
314 |
+
|
315 |
+
model_inputs = tokenizer(query, return_tensors='pt')
|
316 |
+
input_ids = model_inputs['input_ids'].cuda()
|
317 |
+
attention_mask = model_inputs['attention_mask'].cuda()
|
318 |
+
generation_config['eos_token_id'] = eos_token_id
|
319 |
+
generation_output = self.generate(
|
320 |
+
pixel_values=pixel_values,
|
321 |
+
visual_features=visual_features,
|
322 |
+
input_ids=input_ids,
|
323 |
+
attention_mask=attention_mask,
|
324 |
+
**generation_config
|
325 |
+
)
|
326 |
+
response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
|
327 |
+
response = response.split(template.sep)[0].strip()
|
328 |
+
history.append((question, response))
|
329 |
+
if return_history:
|
330 |
+
return response, history
|
331 |
+
else:
|
332 |
+
query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
|
333 |
+
query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
|
334 |
+
if verbose:
|
335 |
+
print(query_to_print, response)
|
336 |
+
return response
|
337 |
+
|
338 |
+
def chat_without_chat_prompt(self, tokenizer, pixel_values, question, generation_config,
|
339 |
+
num_patches_list=None, IMG_START_TOKEN='<|vision_start|>', IMG_END_TOKEN='<|vision_end|>',
|
340 |
+
IMG_CONTEXT_TOKEN='<|vision_pad|>', verbose=False, visual_features=None):
|
341 |
+
|
342 |
+
if pixel_values is not None and '<image>' not in question:
|
343 |
+
question = '<image>\n' + question
|
344 |
+
|
345 |
+
if num_patches_list is None:
|
346 |
+
num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
|
347 |
+
assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
|
348 |
+
|
349 |
+
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
|
350 |
+
self.img_context_token_id = img_context_token_id
|
351 |
+
|
352 |
+
template = get_conv_template(self.template)
|
353 |
+
template.system_message = self.system_message
|
354 |
+
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
|
355 |
+
|
356 |
+
if verbose and pixel_values is not None:
|
357 |
+
image_bs = pixel_values.shape[0]
|
358 |
+
print(f'dynamic ViT batch size: {image_bs}')
|
359 |
+
|
360 |
+
query = question
|
361 |
+
|
362 |
+
for num_patches in num_patches_list:
|
363 |
+
tile_pos_identifiers = [f"<tile_{i}>" for i in range(1, num_patches)] + ["<tile_global_thumbnail>"]
|
364 |
+
image_tokens = ''
|
365 |
+
for tile_pos_identifier in tile_pos_identifiers:
|
366 |
+
image_tokens += tile_pos_identifier + IMG_CONTEXT_TOKEN * self.num_image_token
|
367 |
+
image_tokens = '<Image>' + image_tokens + '</Image>'
|
368 |
+
query = query.replace('<image>', image_tokens, 1)
|
369 |
+
|
370 |
+
model_inputs = tokenizer(query, return_tensors='pt')
|
371 |
+
input_ids = model_inputs['input_ids'].cuda()
|
372 |
+
attention_mask = model_inputs['attention_mask'].cuda()
|
373 |
+
generation_config['eos_token_id'] = eos_token_id
|
374 |
+
generation_output = self.generate(
|
375 |
+
pixel_values=pixel_values,
|
376 |
+
visual_features=visual_features,
|
377 |
+
input_ids=input_ids,
|
378 |
+
attention_mask=attention_mask,
|
379 |
+
**generation_config
|
380 |
+
)
|
381 |
+
response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
|
382 |
+
response = response.split(template.sep)[0].strip()
|
383 |
+
|
384 |
+
query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
|
385 |
+
query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
|
386 |
+
if verbose:
|
387 |
+
print(query_to_print, response)
|
388 |
+
return response
|
389 |
+
|
390 |
+
@torch.no_grad()
|
391 |
+
def generate(
|
392 |
+
self,
|
393 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
394 |
+
input_ids: Optional[torch.FloatTensor] = None,
|
395 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
396 |
+
visual_features: Optional[torch.FloatTensor] = None,
|
397 |
+
generation_config: Optional[GenerationConfig] = None,
|
398 |
+
output_hidden_states: Optional[bool] = None,
|
399 |
+
return_dict: Optional[bool] = None,
|
400 |
+
**generate_kwargs,
|
401 |
+
) -> torch.LongTensor:
|
402 |
+
|
403 |
+
# assert self.img_context_token_id is not None
|
404 |
+
if pixel_values is not None:
|
405 |
+
if visual_features is not None:
|
406 |
+
vit_embeds = visual_features.cuda()
|
407 |
+
vit_embeds = self.mlp1(vit_embeds)
|
408 |
+
else:
|
409 |
+
vit_embeds = self.extract_feature(pixel_values)
|
410 |
+
|
411 |
+
input_embeds = self.language_model.get_input_embeddings()(input_ids)
|
412 |
+
B, N, C = input_embeds.shape
|
413 |
+
input_embeds = input_embeds.reshape(B * N, C)
|
414 |
+
|
415 |
+
input_ids = input_ids.reshape(B * N)
|
416 |
+
selected = (input_ids == self.img_context_token_id)
|
417 |
+
assert selected.sum() != 0
|
418 |
+
input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
|
419 |
+
|
420 |
+
input_embeds = input_embeds.reshape(B, N, C)
|
421 |
+
else:
|
422 |
+
input_embeds = self.language_model.get_input_embeddings()(input_ids)
|
423 |
+
|
424 |
+
outputs = self.language_model.generate(
|
425 |
+
inputs_embeds=input_embeds,
|
426 |
+
attention_mask=attention_mask,
|
427 |
+
generation_config=generation_config,
|
428 |
+
output_hidden_states=output_hidden_states,
|
429 |
+
return_dict=return_dict,
|
430 |
+
use_cache=True,
|
431 |
+
**generate_kwargs,
|
432 |
+
)
|
433 |
+
|
434 |
+
return outputs
|