# 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 [LoserCheems/doge-tiny-test](https://huggingface.co/LoserCheems/doge-tiny-test) 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`] hidden_size (`int`, *optional*, defaults to 1024): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 4096): Dimension of the CDMoE representations. num_hidden_layers (`int`, *optional*, defaults to 16): 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 `False`): 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. 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 4096): 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, hidden_size=1024, intermediate_size=4096, num_hidden_layers=16, hidden_bias=False, hidden_dropout=0.0, hidden_act="silu", max_position_embeddings=2048, rope_theta=10000.0, rope_scaling=None, initializer_range=0.02, rms_norm_eps=1e-06, use_cache=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, num_attention_heads=8, attention_dropout=0.0, is_moe=False, num_cdmmoe_experts=4096, num_cdmmoe_heads=4, num_cdmmoe_experts_per_head=8, expert_retrieval_size=256, **kwargs, ): self.vocab_size = vocab_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.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.tie_word_embeddings = tie_word_embeddings self.num_attention_heads = num_attention_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__( 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, )