wehub-resource-sync
339 lines
16 KiB
Python
339 lines
16 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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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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""" VisualGLM model configuration """
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import copy
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import os
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from typing import Union
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from ...utils.log import logger
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from ..chatglm.configuration import ChatGLMConfig
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from ..configuration_utils import PretrainedConfig
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__all__ = ["VisualGLMVisionConfig", "VisualGLMQFormerConfig", "VisualGLMConfig"]
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class VisualGLMVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`VisualGLMVisionModel`]. It is used to instantiate a
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VisualGLM 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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hidden_size (`int`, *optional*, defaults to 1408):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 6144):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 39):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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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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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. layer_norm_eps (`float`, *optional*, defaults
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to 1e-5): 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 probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
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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 1):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether to add a bias to the queries and values in the self-attention layers.
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Example:
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```python
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>>> from paddlenlp.transformers import VisualGLMVisionConfig, VisualGLMVisionModel
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>>> # Initializing a VisualGLMVisionConfig
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>>> configuration = VisualGLMVisionConfig()
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>>> # Initializing a VisualGLMVisionModel (with random weights) from the configuration above.
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>>> model = VisualGLMVisionModel(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 = "visualglm_vision_model"
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def __init__(
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self,
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hidden_size=1408,
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intermediate_size=6144,
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num_hidden_layers=39,
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num_attention_heads=16,
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num_channels=3,
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image_size=224,
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patch_size=14,
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hidden_act="gelu",
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layer_norm_eps=0.00001,
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dropout=0.1,
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attention_dropout=0.1,
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initializer_range=1e-10,
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initializer_factor=1.0,
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qkv_bias=True,
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**kwargs,
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):
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kwargs["return_dict"] = kwargs.pop("return_dict", True)
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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.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.qkv_bias = qkv_bias
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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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# get the vision config dict if we are loading from VisualGLMConfig
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if config_dict.get("model_type") == "visualglm":
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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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class VisualGLMQFormerConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`VisualGLMQFormerModel`]. It is used to instantiate a
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VisualGLM Querying Transformer (Q-Former) model 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 documentation from
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[`PretrainedConfig`] for more information.
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Note that [`VisualGLMQFormerModel`] is very similar to [`BertLMHeadModel`] with interleaved cross-attention.
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Args:
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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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 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `Callable`, *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"`, `"silu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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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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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
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Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
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positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
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[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
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For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
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with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
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classifier_dropout (`float`, *optional*):
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The dropout ratio for the classification head.
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cross_attention_frequency (`int`, *optional*, defaults to 2):
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The frequency of adding cross-attention to the Transformer layers.
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encoder_hidden_size (`int`, *optional*, defaults to 1408):
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The hidden size of the hidden states for cross-attention.
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Examples:
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```python
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>>> from paddlenlp.transformers import VisualGLMQFormerConfig, VisualGLMQFormerModel
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>>> # Initializing a VisualGLM configuration
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>>> configuration = VisualGLMQFormerConfig()
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>>> # Initializing a model (with random weights) from the configuration above
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>>> model = VisualGLMQFormerModel(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 = "visualglm_qformer_model"
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def __init__(
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self,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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position_embedding_type="absolute",
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classifier_dropout=None,
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cross_attention_frequency=2,
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encoder_hidden_size=1408,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, **kwargs)
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self.hidden_size = hidden_size
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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.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.position_embedding_type = position_embedding_type
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self.classifier_dropout = classifier_dropout
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self.cross_attention_frequency = cross_attention_frequency
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self.encoder_hidden_size = encoder_hidden_size
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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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# get the qformer config dict if we are loading from VisualGLMConfig
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if config_dict.get("model_type") == "visualglm":
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config_dict = config_dict["qformer_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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class VisualGLMConfig(PretrainedConfig):
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r"""
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[`VisualGLMConfig`] is the configuration class to store the configuration of a [`VisualGLMForConditionalGeneration`]. It is
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used to instantiate a VisualGLM model according to the specified arguments, defining the vision model, Q-Former model
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and language model configs.
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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 (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`VisualGLMVisionConfig`].
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qformer_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`VisualGLMQFormerConfig`].
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text_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize any [`PretrainedConfig`].
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num_query_tokens (`int`, *optional*, defaults to 32):
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The number of query tokens passed through the Transformer.
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kwargs (*optional*):
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Dictionary of keyword arguments.
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Example:
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```python
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>>> from paddlenlp.transformers import (
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... VisualGLMVisionConfig,
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... VisualGLMQFormerConfig,
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... ChatGLMConfig,
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... VisualGLMConfig,
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... VisualGLMForConditionalGeneration,
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... )
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>>> # Initializing a VisualGLMConfig configuration
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>>> configuration = VisualGLMConfig()
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>>> # Initializing a VisualGLMForConditionalGeneration (with random weights) from the configuration above
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>>> model = VisualGLMForConditionalGeneration(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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>>> # We can also initialize a VisualGLMConfig from a VisualGLMVisionConfig, VisualGLMQFormerConfig and any PretrainedConfig
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>>> # Initializing VisualGLM vision, VisualGLM Q-Former and language model configurations
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>>> vision_config = VisualGLMVisionConfig()
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>>> qformer_config = VisualGLMQFormerConfig()
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>>> text_config = ChatGLMConfig()
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>>> config = VisualGLMConfig.from_text_vision_configs(vision_config, qformer_config, text_config)
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```"""
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model_type = "visualglm"
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def __init__(self, vision_config=None, qformer_config=None, text_config=None, num_query_tokens=32, **kwargs):
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super().__init__(**kwargs)
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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 the VisualGLMVisionConfig with default values.")
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if qformer_config is None:
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qformer_config = {}
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logger.info("qformer_config is None. Initializing the VisualGLMQFormerConfig with default values.")
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if text_config is None:
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text_config = {}
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logger.info("text_config is None. Initializing the text config with default values (`ChatGLMConfig`).")
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self.vision_config = VisualGLMVisionConfig(**vision_config)
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self.qformer_config = VisualGLMQFormerConfig(**qformer_config)
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text_model_type = text_config["model_type"] if "model_type" in text_config else "chatglm"
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if text_model_type == "chatglm":
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self.text_config = ChatGLMConfig(**text_config)
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else:
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raise ValueError("Only chatglm accepted for model_type, but accepted {}.".format(text_model_type))
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self.num_query_tokens = num_query_tokens
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self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size
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self.initializer_factor = 1.0
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self.initializer_range = 0.02
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@classmethod
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def from_vision_qformer_text_configs(
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cls,
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vision_config: VisualGLMVisionConfig,
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qformer_config: VisualGLMQFormerConfig,
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text_config: PretrainedConfig,
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**kwargs,
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):
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r"""
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Instantiate a [`VisualGLMConfig`] (or a derived class) from a vision model, Q-Former and language model
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configurations.
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Returns:
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[`VisualGLM`]: An instance of a configuration object
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"""
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return cls(
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vision_config=vision_config.to_dict(),
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qformer_config=qformer_config.to_dict(),
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text_config=text_config.to_dict(),
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**kwargs,
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)
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def to_dict(self, *args, **kwargs):
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"""
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Serializes this instance to a Python dictionary. Override 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["qformer_config"] = self.qformer_config.to_dict()
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output["text_config"] = self.text_config.to_dict()
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output["model_type"] = self.__class__.model_type
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return output
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