co-instruct / configuration_mplug_owl2.py

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	# Copyright (c) Alibaba.
	#
	# This source code is licensed under the license found in the
	# LICENSE file in the root directory of this source tree.
	import copy
	import os
	from typing import Union

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


	class LlamaConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
	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 LLaMA-7B.

	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 32000):
	Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`LlamaModel`]
	hidden_size (`int`, optional, defaults to 4096):
	Dimension of the hidden representations.
	intermediate_size (`int`, optional, defaults to 11008):
	Dimension of the MLP representations.
	num_hidden_layers (`int`, optional, defaults to 32):
	Number of hidden layers in the Transformer decoder.
	num_attention_heads (`int`, optional, defaults to 32):
	Number of attention heads for each attention layer in the Transformer decoder.
	num_key_value_heads (`int`, optional):
	This is the number of key_value heads that should be used to implement Grouped Query Attention. If
	`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
	`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
	converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
	by meanpooling all the original heads within that group. For more details checkout [this
	paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
	`num_attention_heads`.
	hidden_act (`str` or `function`, optional, defaults to `"silu"`):
	The non-linear activation function (function or string) in the decoder.
	max_position_embeddings (`int`, optional, defaults to 2048):
	The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
	Llama 2 up to 4096, CodeLlama up to 16384.
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	rms_norm_eps (`float`, optional, defaults to 1e-06):
	The epsilon used by the rms normalization layers.
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models). Only
	relevant if `config.is_decoder=True`.
	pad_token_id (`int`, optional):
	Padding token id.
	bos_token_id (`int`, optional, defaults to 1):
	Beginning of stream token id.
	eos_token_id (`int`, optional, defaults to 2):
	End of stream token id.
	pretraining_tp (`int`, optional, defaults to 1):
	Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
	document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
	necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
	issue](https://github.com/pytorch/pytorch/issues/76232).
	tie_word_embeddings (`bool`, optional, defaults to `False`):
	Whether to tie weight embeddings
	rope_theta (`float`, optional, defaults to 10000.0):
	The base period of the RoPE embeddings.
	rope_scaling (`Dict`, optional):
	Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
	strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
	`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
	`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
	these scaling strategies behave:
	https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
	experimental feature, subject to breaking API changes in future versions.
	attention_bias (`bool`, defaults to `False`, optional, defaults to `False`):
	Whether to use a bias in the query, key, value and output projection layers during self-attention.


	```python
	>>> from transformers import LlamaModel, LlamaConfig

	>>> # Initializing a LLaMA llama-7b style configuration
	>>> configuration = LlamaConfig()

	>>> # Initializing a model from the llama-7b style configuration
	>>> model = LlamaModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "llama"
	keys_to_ignore_at_inference = ["past_key_values"]

	def __init__(
	self,
	vocab_size=32000,
	hidden_size=4096,
	intermediate_size=11008,
	num_hidden_layers=32,
	num_attention_heads=32,
	num_key_value_heads=None,
	hidden_act="silu",
	max_position_embeddings=2048,
	initializer_range=0.02,
	rms_norm_eps=1e-6,
	use_cache=True,
	pad_token_id=None,
	bos_token_id=1,
	eos_token_id=2,
	pretraining_tp=1,
	tie_word_embeddings=False,
	rope_theta=10000.0,
	rope_scaling=None,
	attention_bias=False,
	attention_dropout=0.0,
	**kwargs,
	):
	self.vocab_size = vocab_size
	self.max_position_embeddings = max_position_embeddings
	self.hidden_size = hidden_size
	self.intermediate_size = intermediate_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads

	# for backward compatibility
	if num_key_value_heads is None:
	num_key_value_heads = num_attention_heads

	self.num_key_value_heads = num_key_value_heads
	self.hidden_act = hidden_act
	self.initializer_range = initializer_range
	self.rms_norm_eps = rms_norm_eps
	self.pretraining_tp = pretraining_tp
	self.use_cache = use_cache
	self.rope_theta = rope_theta
	self.rope_scaling = rope_scaling
	self._rope_scaling_validation()
	self.attention_bias = attention_bias
	self.attention_dropout = attention_dropout

	super().__init__(
	pad_token_id=pad_token_id,
	bos_token_id=bos_token_id,
	eos_token_id=eos_token_id,
	tie_word_embeddings=tie_word_embeddings,
	**kwargs,
	)

	def _rope_scaling_validation(self):
	"""
	Validate the `rope_scaling` configuration.
	"""
	if self.rope_scaling is None:
	return

	if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
	raise ValueError(
	"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
	f"got {self.rope_scaling}"
	)
	rope_scaling_type = self.rope_scaling.get("type", None)
	rope_scaling_factor = self.rope_scaling.get("factor", None)
	if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
	raise ValueError(
	f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
	)
	if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
	raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")


	class MplugOwlVisionConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`MplugOwlVisionModel`]. It is used to instantiate
	a
	mPLUG-Owl vision encoder according to the specified arguments, defining the model architecture. Instantiating a
	configuration defaults will yield a similar configuration to that of the mPLUG-Owl
	[x-plug/x_plug-llama-7b](https://huggingface.co/x-plug/x_plug-llama-7b) 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.
	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"` 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).


	```"""

	model_type = "mplug_owl_vision_model"

	def __init__(
	self,
	hidden_size=1024,
	intermediate_size=4096,
	projection_dim=768,
	num_hidden_layers=24,
	num_attention_heads=16,
	num_channels=3,
	image_size=448,
	patch_size=14,
	hidden_act="quick_gelu",
	layer_norm_eps=1e-6,
	attention_dropout=0.0,
	initializer_range=0.02,
	initializer_factor=1.0,
	use_flash_attn=False,
	**kwargs,
	):
	super().__init__(**kwargs)
	self.hidden_size = hidden_size
	self.intermediate_size = intermediate_size
	self.projection_dim = projection_dim
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.num_channels = num_channels
	self.patch_size = patch_size
	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.use_flash_attn = use_flash_attn

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

	# get the vision config dict if we are loading from MplugOwlConfig
	if config_dict.get("model_type") == "mplug-owl":
	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 MplugOwlVisualAbstractorConfig(PretrainedConfig):
	model_type = "mplug_owl_visual_abstract"

	def __init__(
	self,
	num_learnable_queries=64,
	hidden_size=1024,
	num_hidden_layers=6,
	num_attention_heads=16,
	intermediate_size=2816,
	attention_probs_dropout_prob=0.,
	initializer_range=0.02,
	layer_norm_eps=1e-6,
	encoder_hidden_size=1024,
	grid_size=None,
	**kwargs,
	):
	super().__init__(**kwargs)
	self.hidden_size = hidden_size
	self.num_learnable_queries = num_learnable_queries
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.intermediate_size = intermediate_size
	self.attention_probs_dropout_prob = attention_probs_dropout_prob
	self.initializer_range = initializer_range
	self.layer_norm_eps = layer_norm_eps
	self.encoder_hidden_size = encoder_hidden_size
	self.grid_size = grid_size if grid_size else 32

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

	# get the visual_abstractor config dict if we are loading from MplugOwlConfig
	if config_dict.get("model_type") == "mplug-owl":
	config_dict = config_dict["abstractor_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)



	DEFAULT_VISUAL_CONFIG = {
	"visual_model": MplugOwlVisionConfig().to_dict(),
	"visual_abstractor": MplugOwlVisualAbstractorConfig().to_dict()
	}

	class MPLUGOwl2Config(LlamaConfig):
	model_type = "mplug_owl2"
	def __init__(self, visual_config=None, **kwargs):
	if visual_config is None:
	self.visual_config = DEFAULT_VISUAL_CONFIG
	else:
	self.visual_config = visual_config

	super().__init__(
	**kwargs,
	)

	if __name__ == "__main__":
	print(MplugOwlVisionConfig().to_dict())