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# coding=utf-8
# Copyright 2022 EleutherAI 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.
# """ GPTNeoX model configuration"""
# from ...configuration_utils import PretrainedConfig
# from ...utils import logging
# logger = logging.get_logger(__name__)
# GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP = {
# "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
# # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
# }
# class GPTNeoXConfig(PretrainedConfig):
# r"""
# This is the configuration class to store the configuration of a [`GPTNeoXModel`]. It is used to instantiate an
# GPTNeoX 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 GPTNeoX
# [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) architecture.
# 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 50432):
# Vocabulary size of the GPTNeoX model. Defines the number of different tokens that can be represented by the
# `inputs_ids` passed when calling [`GPTNeoXModel`].
# hidden_size (`int`, *optional*, defaults to 6144):
# Dimension of the encoder layers and the pooler layer.
# num_hidden_layers (`int`, *optional*, defaults to 44):
# Number of hidden layers in the Transformer encoder.
# num_attention_heads (`int`, *optional*, defaults to 64):
# Number of attention heads for each attention layer in the Transformer encoder.
# intermediate_size (`int`, *optional*, defaults to 24576):
# Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
# hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
# The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
# `"relu"`, `"selu"` and `"gelu_new"` are supported.
# rotary_pct (`float`, *optional*, defaults to 0.25):
# percentage of hidden dimensions to allocate to rotary embeddings
# rotary_emb_base (`int`, *optional*, defaults to 10000)
# base for computing rotary embeddings frequency
# attention_dropout (`float`, *optional*, defaults to 0.0):
# The dropout ratio probability of the attention score.
# hidden_dropout (`float`, *optional*, defaults to 0.0):
# The dropout ratio of (1) the word embeddings, (2) the post-attention hidden states, and (3) the post-mlp
# hidden states.
# classifier_dropout (`float`, *optional*, defaults to 0.1):
# Argument used when doing token classification, used in the model [`GPTNeoXForTokenClassification`].
# The dropout ratio for the hidden layer.
# max_position_embeddings (`int`, *optional*, defaults to 2048):
# The maximum sequence length that this model might ever be used with. Typically set this to something large
# just in case (e.g., 512 or 1024 or 2048).
# initializer_range (`float`, *optional*, defaults to 1e-5):
# The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
# layer_norm_eps (`float`, *optional*, defaults to 1e-12):
# The epsilon used by the layer 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`.
# use_parallel_residual (`bool`, *optional*, defaults to `True`):
# Whether to use a "parallel" formulation in each Transformer layer, which can provide a slight training
# speedup at large scales (e.g. 20B).
# 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/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
# experimental feature, subject to breaking API changes in future versions.
# Example:
# ```python
# >>> from transformers import GPTNeoXConfig, GPTNeoXModel
# >>> # Initializing a GPTNeoX gpt-neox-20b style configuration
# >>> configuration = GPTNeoXConfig()
# >>> # Initializing a model (with random weights) from the gpt-neox-20b style configuration
# >>> model = GPTNeoXModel(configuration) # doctest: +SKIP
# >>> # Accessing the model configuration
# >>> configuration = model.config # doctest: +SKIP
# ```"""
# model_type = "gpt_neox"
from transformers import PretrainedConfig
class CustomConfig4(PretrainedConfig):
model_type = "custom4"
def __init__(
self,
vocab_size=50432,
hidden_size=6144,
num_hidden_layers=44,
num_attention_heads=64,
intermediate_size=24576,
hidden_act="gelu",
rotary_pct=0.25,
rotary_emb_base=10000,
attention_dropout=0.0,
hidden_dropout=0.0,
classifier_dropout=0.1,
max_position_embeddings=2048,
initializer_range=0.02,
layer_norm_eps=1e-5,
use_cache=True,
bos_token_id=0,
eos_token_id=2,
tie_word_embeddings=False,
use_parallel_residual=True,
rope_scaling=None,
**kwargs,
):
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.rotary_pct = rotary_pct
self.rotary_emb_base = rotary_emb_base
self.attention_dropout = attention_dropout
self.hidden_dropout = hidden_dropout
self.classifier_dropout = classifier_dropout
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.tie_word_embeddings = tie_word_embeddings
self.use_parallel_residual = use_parallel_residual
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"The hidden size is not divisble by the number of attention heads! Make sure to update them!"
)
# 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 an float > 1, got {rope_scaling_factor}")
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