# coding=utf-8
# Copyright 2024 Jingze Shi and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on the Wonderful Matrices paper implementation.
#
# https://arxiv.org/abs/2412.11834
#
# 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.
"""PyTorch Doge model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
class DogeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DogeModel`]. It is used to instantiate an Doge
model according to the specified arguments, defining the model architecture like [JingzeShi/Doge-20M](https://huggingface.co/JingzeShi/Doge-20M).
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 32768):
Vocabulary size of the Doge model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DogeModel`]
num_channels (`int`, *optional*, defaults to 3):
Number of channels in the input image.
patch_size (`int`, *optional*, defaults to 16):
Patch size of Vision Transformer Embeddings.
hidden_size (`int`, *optional*, defaults to 1024):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 2048):
Dimension of the CDMoE representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
hidden_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the hidden layers.
hidden_dropout (`float`, *optional*, defaults to 0.0):
Dropout probability for each sequence transformation and state transformation module.
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.
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.
NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings.
In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'.
The original max position embeddings used during pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation.
If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<`original_max_position_embeddings`).
Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<`original_max_position_embeddings`).
Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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*, defaults to 0):
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.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether to tie weight embeddings
num_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to `None`):
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`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
is_moe (`bool`, *optional*, defaults to `False`):
Whether to use the Cross Domain Mixture of Experts, if `True`, the MoE will inherit the MLP to initialize
num_cdmmoe_experts (`int`, *optional*, defaults to 2048):
Number of Private Experts for the Cross Domain Mixture of Experts.
num_cdmmoe_heads (`int`, *optional*, defaults to 4):
Number of heads of Private Experts for the Cross Domain Mixture of Experts.
num_cdmmoe_experts_per_head (`int`, *optional*, defaults to 8):
Number of Private Experts per head for the Cross Domain Mixture of Experts.
expert_retrieval_size (`int`, *optional*, defaults to 256):
Dimension of the Expert retrieval states for the Cross Domain Mixture of Experts.
"""
model_type = "doge"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32768,
num_channels=3,
patch_size=16,
hidden_size=1024,
intermediate_size=2048,
num_hidden_layers=32,
hidden_bias=False,
hidden_dropout=0.0,
hidden_act="silu",
max_position_embeddings=2048,
rope_theta=10000.0,
rope_scaling={
"rope_type": "dynamic",
"factor": 4.0,
"original_max_position_embeddings": 2048,
},
initializer_range=0.02,
rms_norm_eps=1e-06,
use_cache=True,
bos_token_id=0,
eos_token_id=1,
pad_token_id=2,
tie_word_embeddings=True,
num_attention_heads=8,
num_key_value_heads=None,
attention_dropout=0.0,
is_moe=False,
num_cdmmoe_experts=2048,
num_cdmmoe_heads=4,
num_cdmmoe_experts_per_head=8,
expert_retrieval_size=256,
**kwargs,
):
self.vocab_size = vocab_size
self.num_channels = num_channels
self.patch_size = patch_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.hidden_bias = hidden_bias
self.hidden_dropout = hidden_dropout
self.hidden_act = hidden_act
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.tie_word_embeddings = tie_word_embeddings
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.attention_dropout = attention_dropout
self.is_moe = is_moe
self.num_cdmmoe_experts = num_cdmmoe_experts
self.num_cdmmoe_heads = num_cdmmoe_heads
self.num_cdmmoe_experts_per_head = num_cdmmoe_experts_per_head
self.expert_retrieval_size = expert_retrieval_size
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, copy it it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)