nanoLLaVA / configuration_llava_qwen2.py

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	# coding=utf-8
	# Copyright 2024 The Qwen team, Alibaba Group 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.
	""" Qwen2 model configuration"""

	from transformers.configuration_utils import PretrainedConfig
	from transformers.utils import logging


	logger = logging.get_logger(__name__)

	QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"Qwen/Qwen2-7B-beta": "https://huggingface.co/Qwen/Qwen2-7B-beta/resolve/main/config.json",
	}


	class Qwen2Config(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
	Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
	with the defaults will yield a similar configuration to that of
	Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).

	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 151936):
	Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`Qwen2Model`]
	hidden_size (`int`, optional, defaults to 4096):
	Dimension of the hidden representations.
	intermediate_size (`int`, optional, defaults to 22016):
	Dimension of the MLP representations.
	num_hidden_layers (`int`, optional, defaults to 32):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 32):
	Number of attention heads for each attention layer in the Transformer encoder.
	num_key_value_heads (`int`, optional, defaults to 32):
	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 `32`.
	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 32768):
	The maximum sequence length that this model might ever be used with.
	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`.
	tie_word_embeddings (`bool`, optional, defaults to `False`):
	Whether the model's input and output word embeddings should be tied.
	rope_theta (`float`, optional, defaults to 10000.0):
	The base period of the RoPE embeddings.
	use_sliding_window (`bool`, optional, defaults to `False`):
	Whether to use sliding window attention.
	sliding_window (`int`, optional, defaults to 4096):
	Sliding window attention (SWA) window size. If not specified, will default to `4096`.
	max_window_layers (`int`, optional, defaults to 28):
	The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for the attention probabilities.

	```python
	>>> from transformers import Qwen2Model, Qwen2Config

	>>> # Initializing a Qwen2 style configuration
	>>> configuration = Qwen2Config()

	>>> # Initializing a model from the Qwen2-7B style configuration
	>>> model = Qwen2Model(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""

	model_type = "qwen2"
	keys_to_ignore_at_inference = ["past_key_values"]

	def __init__(
	self,
	vocab_size=151936,
	hidden_size=4096,
	intermediate_size=22016,
	num_hidden_layers=32,
	num_attention_heads=32,
	num_key_value_heads=32,
	hidden_act="silu",
	max_position_embeddings=32768,
	initializer_range=0.02,
	rms_norm_eps=1e-6,
	use_cache=True,
	tie_word_embeddings=False,
	rope_theta=10000.0,
	use_sliding_window=False,
	sliding_window=4096,
	max_window_layers=28,
	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
	self.use_sliding_window = use_sliding_window
	self.sliding_window = sliding_window
	self.max_window_layers = max_window_layers

	# 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.use_cache = use_cache
	self.rope_theta = rope_theta
	self.attention_dropout = attention_dropout

	super().__init__(
	tie_word_embeddings=tie_word_embeddings,
	**kwargs,
	)

	from typing import Union
	from transformers import PretrainedConfig
	import os


	class SigLipVisionConfig(PretrainedConfig):
	model_type = "siglip_vision_model"

	def __init__(
	self,
	hidden_size=1152,
	image_mean=(0.5, 0.5, 0.5),
	intermediate_size=4304,
	num_hidden_layers=27,
	num_attention_heads=16,
	num_channels=3,
	image_size=384,
	patch_size=14,
	hidden_act="gelu_pytorch_tanh",
	layer_norm_eps=1e-6,
	attention_dropout=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.num_channels = num_channels
	self.patch_size = patch_size
	self.image_size = image_size
	self.attention_dropout = attention_dropout
	self.layer_norm_eps = layer_norm_eps
	self.hidden_act = hidden_act
	self.image_mean = image_mean

	@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)

	# get the vision config dict if we are loading from SigLipConfig
	if config_dict.get("model_type") == "siglip":
	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 LlavaQwen2Config(Qwen2Config):
	model_type = "llava-qwen2"