CheXagent-8b / configuration_chexagent.py

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	# coding=utf-8
	# Copyright 2023 The CheXagent Authors and The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import os
	from typing import Union

	from transformers.configuration_utils import PretrainedConfig
	from transformers.models.auto import CONFIG_MAPPING
	from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
	from transformers.utils import logging

	logger = logging.get_logger(__name__)


	class CheXagentVisionConfig(PretrainedConfig):
	model_type = "chexagent_vision_model"

	def __init__(
	self,
	hidden_size=1408,
	intermediate_size=6144,
	num_hidden_layers=39,
	num_attention_heads=16,
	image_size=224,
	patch_size=14,
	hidden_act="gelu",
	layer_norm_eps=1e-6,
	attention_dropout=0.0,
	initializer_range=1e-10,
	qkv_bias=True,
	**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.patch_size = patch_size
	self.image_size = image_size
	self.initializer_range = initializer_range
	self.attention_dropout = attention_dropout
	self.layer_norm_eps = layer_norm_eps
	self.hidden_act = hidden_act
	self.qkv_bias = qkv_bias

	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
	cls._set_token_in_kwargs(kwargs)

	config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

	if config_dict.get("model_type") == "chexagent":
	config_dict = config_dict["vision_config"]

	if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
	logger.warning(
	f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
	f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
	)

	return cls.from_dict(config_dict, **kwargs)


	class CheXagentQFormerConfig(PretrainedConfig):
	model_type = "chexagent_qformer"

	def __init__(
	self,
	vocab_size=30522,
	hidden_size=768,
	num_hidden_layers=12,
	num_attention_heads=12,
	intermediate_size=3072,
	hidden_act="gelu",
	hidden_dropout_prob=0.1,
	attention_probs_dropout_prob=0.1,
	max_position_embeddings=512,
	initializer_range=0.02,
	layer_norm_eps=1e-12,
	pad_token_id=0,
	position_embedding_type="absolute",
	cross_attention_frequency=2,
	encoder_hidden_size=1408,
	**kwargs,
	):
	super().__init__(pad_token_id=pad_token_id, **kwargs)

	self.vocab_size = vocab_size
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.hidden_act = hidden_act
	self.intermediate_size = intermediate_size
	self.hidden_dropout_prob = hidden_dropout_prob
	self.attention_probs_dropout_prob = attention_probs_dropout_prob
	self.max_position_embeddings = max_position_embeddings
	self.initializer_range = initializer_range
	self.layer_norm_eps = layer_norm_eps
	self.position_embedding_type = position_embedding_type
	self.cross_attention_frequency = cross_attention_frequency
	self.encoder_hidden_size = encoder_hidden_size

	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
	cls._set_token_in_kwargs(kwargs)

	config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

	if config_dict.get("model_type") == "chexagent":
	config_dict = config_dict["qformer_config"]

	if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
	logger.warning(
	f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
	f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
	)

	return cls.from_dict(config_dict, **kwargs)


	class CheXagentConfig(PretrainedConfig):
	model_type = "chexagent"

	def __init__(
	self, vision_config=None, qformer_config=None, text_config=None, num_query_tokens=128,
	num_max_images=2, **kwargs
	):
	super().__init__(**kwargs)

	if vision_config is None:
	vision_config = {}

	if qformer_config is None:
	qformer_config = {}

	if text_config is None:
	text_config = {}

	self.vision_config = CheXagentVisionConfig(**vision_config)
	self.qformer_config = CheXagentQFormerConfig(**qformer_config)
	text_model_type = text_config["model_type"] if "model_type" in text_config else "opt"
	self.text_config = CONFIG_MAPPING[text_model_type](**text_config)

	self.tie_word_embeddings = self.text_config.tie_word_embeddings
	self.is_encoder_decoder = self.text_config.is_encoder_decoder

	self.num_query_tokens = num_query_tokens
	self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size
	self.use_decoder_only_language_model = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
	self.initializer_factor = 1.0
	self.initializer_range = 0.02
	self.num_max_images = num_max_images

	@classmethod
	def from_vision_qformer_text_configs(
	cls,
	vision_config: CheXagentVisionConfig,
	qformer_config: CheXagentQFormerConfig,
	text_config: PretrainedConfig,
	**kwargs,
	):
	return cls(
	vision_config=vision_config.to_dict(),
	qformer_config=qformer_config.to_dict(),
	text_config=text_config.to_dict(),
	**kwargs,
	)