Bunny-v1_0-3B / configuration_bunny_phi.py

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	# coding=utf-8
	# Copyright 2023 Microsoft 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.

	""" Phi model configuration"""

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

	logger = logging.get_logger(__name__)

	PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"microsoft/phi-1": "https://huggingface.co/microsoft/phi-1/resolve/main/config.json",
	"microsoft/phi-1_5": "https://huggingface.co/microsoft/phi-1_5/resolve/main/config.json",
	"microsoft/phi-2": "https://huggingface.co/microsoft/phi-2/resolve/main/config.json",
	}


	class PhiConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
	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 Phi
	[microsoft/phi-1](https://huggingface.co/microsoft/phi-1).

	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 51200):
	Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`PhiModel`].
	hidden_size (`int`, optional, defaults to 2048):
	Dimension of the hidden representations.
	intermediate_size (`int`, optional, defaults to 8192):
	Dimension of the MLP representations.
	num_hidden_layers (`int`, optional, defaults to 24):
	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`.
	resid_pdrop (`float`, optional, defaults to 0.0):
	Dropout probability for mlp outputs.
	embd_pdrop (`int`, optional, defaults to 0.0):
	The dropout ratio for the embeddings.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio after computing the attention scores.
	hidden_act (`str` or `function`, optional, defaults to `"gelu_new"`):
	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. Phi-1 and Phi-1.5 supports up to 2048
	tokens.
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	layer_norm_eps (`float`, optional, defaults to 1e-05):
	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`. Whether to tie weight embeddings or not.
	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 an 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/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
	is an experimental feature, subject to breaking API changes in future versions.
	partial_rotary_factor (`float`, optional, defaults to 0.5):
	Percentage of the query and keys which will have rotary embedding.
	qk_layernorm (`bool`, optional, defaults to `False`):
	Whether or not to normalize the Queries and Keys after projecting the hidden states.
	bos_token_id (`int`, optional, defaults to 1):
	Denotes beginning of sequences token id.
	eos_token_id (`int`, optional, defaults to 2):
	Denotes end of sequences token id.

	Example:

	```python
	>>> from transformers import PhiModel, PhiConfig

	>>> # Initializing a Phi-1 style configuration
	>>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")

	>>> # Initializing a model from the configuration
	>>> model = PhiModel(configuration)

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

	model_type = "phi"
	keys_to_ignore_at_inference = ["past_key_values"]

	def __init__(
	self,
	vocab_size=51200,
	hidden_size=2048,
	intermediate_size=8192,
	num_hidden_layers=24,
	num_attention_heads=32,
	num_key_value_heads=None,
	resid_pdrop=0.0,
	embd_pdrop=0.0,
	attention_dropout=0.0,
	hidden_act="gelu_new",
	max_position_embeddings=2048,
	initializer_range=0.02,
	layer_norm_eps=1e-5,
	use_cache=True,
	tie_word_embeddings=False,
	rope_theta=10000.0,
	rope_scaling=None,
	partial_rotary_factor=0.5,
	qk_layernorm=False,
	bos_token_id=1,
	eos_token_id=2,
	**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

	if num_key_value_heads is None:
	num_key_value_heads = num_attention_heads

	self.num_key_value_heads = num_key_value_heads
	self.resid_pdrop = resid_pdrop
	self.embd_pdrop = embd_pdrop
	self.attention_dropout = attention_dropout
	self.hidden_act = hidden_act
	self.max_position_embeddings = max_position_embeddings
	self.initializer_range = initializer_range
	self.layer_norm_eps = layer_norm_eps
	self.use_cache = use_cache
	self.rope_theta = rope_theta
	self.rope_scaling = rope_scaling
	self.partial_rotary_factor = partial_rotary_factor
	self.qk_layernorm = qk_layernorm
	self._rope_scaling_validation()

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

	# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
	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}")


	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 BunnyPhiConfig(PhiConfig):
	model_type = "bunny-phi"