# coding=utf-8 # Copyright 2022 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. """X-CLIP model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class XCLIPTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`XCLIPModel`]. It is used to instantiate an X-CLIP 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 X-CLIP [microsoft/xclip-base-patch32](https://huggingface.co/microsoft/xclip-base-patch32) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 49408): Vocabulary size of the X-CLIP text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`XCLIPModel`]. hidden_size (`int`, *optional*, defaults to 512): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 2048): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. max_position_embeddings (`int`, *optional*, defaults to 77): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). Example: ```python >>> from transformers import XCLIPTextModel, XCLIPTextConfig >>> # Initializing a XCLIPTextModel with microsoft/xclip-base-patch32 style configuration >>> configuration = XCLIPTextConfig() >>> # Initializing a XCLIPTextConfig from the microsoft/xclip-base-patch32 style configuration >>> model = XCLIPTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "xclip_text_model" base_config_key = "text_config" def __init__( self, vocab_size=49408, hidden_size=512, intermediate_size=2048, num_hidden_layers=12, num_attention_heads=8, max_position_embeddings=77, hidden_act="quick_gelu", layer_norm_eps=1e-5, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs, ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.max_position_embeddings = max_position_embeddings self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout class XCLIPVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`XCLIPModel`]. It is used to instantiate an X-CLIP 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 X-CLIP [microsoft/xclip-base-patch32](https://huggingface.co/microsoft/xclip-base-patch32) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. mit_hidden_size (`int`, *optional*, defaults to 512): Dimensionality of the encoder layers of the Multiframe Integration Transformer (MIT). mit_intermediate_size (`int`, *optional*, defaults to 2048): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Multiframe Integration Transformer (MIT). mit_num_hidden_layers (`int`, *optional*, defaults to 1): Number of hidden layers in the Multiframe Integration Transformer (MIT). mit_num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Multiframe Integration Transformer (MIT). image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"`, `"gelu_new"` and `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). drop_path_rate (`float`, *optional*, defaults to 0.0): Stochastic depth rate. Example: ```python >>> from transformers import XCLIPVisionModel, XCLIPVisionConfig >>> # Initializing a XCLIPVisionModel with microsoft/xclip-base-patch32 style configuration >>> configuration = XCLIPVisionConfig() >>> # Initializing a XCLIPVisionModel model from the microsoft/xclip-base-patch32 style configuration >>> model = XCLIPVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "xclip_vision_model" base_config_key = "vision_config" def __init__( self, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, mit_hidden_size=512, mit_intermediate_size=2048, mit_num_hidden_layers=1, mit_num_attention_heads=8, num_channels=3, image_size=224, patch_size=32, num_frames=8, hidden_act="quick_gelu", layer_norm_eps=1e-5, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, drop_path_rate=0.0, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.mit_hidden_size = mit_hidden_size self.mit_intermediate_size = mit_intermediate_size self.mit_num_hidden_layers = mit_num_hidden_layers self.mit_num_attention_heads = mit_num_attention_heads self.num_channels = num_channels self.patch_size = patch_size self.num_frames = num_frames self.image_size = image_size self.initializer_range = initializer_range self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.drop_path_rate = drop_path_rate class XCLIPConfig(PretrainedConfig): r""" [`XCLIPConfig`] is the configuration class to store the configuration of a [`XCLIPModel`]. It is used to instantiate X-CLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the X-CLIP [microsoft/xclip-base-patch32](https://huggingface.co/microsoft/xclip-base-patch32) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`XCLIPTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`XCLIPVisionConfig`]. projection_dim (`int`, *optional*, defaults to 512): Dimensionality of text and vision projection layers. prompt_layers (`int`, *optional*, defaults to 2): Number of layers in the video specific prompt generator. prompt_alpha (`float`, *optional*, defaults to 0.1): Alpha value to use in the video specific prompt generator. prompt_hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): The non-linear activation function (function or string) in the video specific prompt generator. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. prompt_num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads in the cross-attention of the video specific prompt generator. prompt_attention_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for the attention layers in the video specific prompt generator. prompt_projection_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for the projection layers in the video specific prompt generator. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The inital value of the *logit_scale* parameter. Default is used as per the original XCLIP implementation. kwargs (*optional*): Dictionary of keyword arguments. """ model_type = "xclip" sub_configs = {"text_config": XCLIPTextConfig, "vision_config": XCLIPVisionConfig} def __init__( self, text_config=None, vision_config=None, projection_dim=512, prompt_layers=2, prompt_alpha=0.1, prompt_hidden_act="quick_gelu", prompt_num_attention_heads=8, prompt_attention_dropout=0.0, prompt_projection_dropout=0.0, logit_scale_init_value=2.6592, **kwargs, ): # If `_config_dict` exist, we use them for the backward compatibility. # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot # of confusion!). text_config_dict = kwargs.pop("text_config_dict", None) vision_config_dict = kwargs.pop("vision_config_dict", None) super().__init__(**kwargs) # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`. if text_config_dict is not None: if text_config is None: text_config = {} # This is the complete result when using `text_config_dict`. _text_config_dict = XCLIPTextConfig(**text_config_dict).to_dict() # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different. for key, value in _text_config_dict.items(): if key in text_config and value != text_config[key] and key not in ["transformers_version"]: # If specified in `text_config_dict` if key in text_config_dict: message = ( f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. " f'The value `text_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`text_config_dict` is provided which will be used to initialize `XCLIPTextConfig`. The " f'value `text_config["{key}"]` will be overridden.' ) logger.info(message) # Update all values in `text_config` with the ones in `_text_config_dict`. text_config.update(_text_config_dict) if vision_config_dict is not None: if vision_config is None: vision_config = {} # This is the complete result when using `vision_config_dict`. _vision_config_dict = XCLIPVisionConfig(**vision_config_dict).to_dict() # convert keys to string instead of integer if "id2label" in _vision_config_dict: _vision_config_dict["id2label"] = { str(key): value for key, value in _vision_config_dict["id2label"].items() } # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different. for key, value in _vision_config_dict.items(): if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]: # If specified in `vision_config_dict` if key in vision_config_dict: message = ( f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different " f'values. The value `vision_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`vision_config_dict` is provided which will be used to initialize `XCLIPVisionConfig`. " f'The value `vision_config["{key}"]` will be overridden.' ) logger.info(message) # Update all values in `vision_config` with the ones in `_vision_config_dict`. vision_config.update(_vision_config_dict) if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `XCLIPTextConfig` with default values.") if vision_config is None: vision_config = {} logger.info("`vision_config` is `None`. initializing the `XCLIPVisionConfig` with default values.") self.text_config = XCLIPTextConfig(**text_config) self.vision_config = XCLIPVisionConfig(**vision_config) self.projection_dim = projection_dim self.prompt_layers = prompt_layers self.prompt_alpha = prompt_alpha self.prompt_hidden_act = prompt_hidden_act self.prompt_num_attention_heads = prompt_num_attention_heads self.prompt_attention_dropout = prompt_attention_dropout self.prompt_projection_dropout = prompt_projection_dropout self.logit_scale_init_value = logit_scale_init_value self.initializer_factor = 1.0 @classmethod def from_text_vision_configs(cls, text_config: XCLIPTextConfig, vision_config: XCLIPVisionConfig, **kwargs): r""" Instantiate a [`XCLIPConfig`] (or a derived class) from xclip text model configuration and xclip vision model configuration. Returns: [`XCLIPConfig`]: An instance of a configuration object """ return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) __all__ = ["XCLIPConfig", "XCLIPTextConfig", "XCLIPVisionConfig"]