# coding=utf-8 # Copyright 2025 The rednote-hilab team 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. from ...configuration_utils import PretrainedConfig, layer_type_validation from ...utils import logging logger = logging.get_logger(__name__) class Dots1Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Dots1Model`]. It is used to instantiate a `dots.llm1` model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of [rednote-hilab/dots.llm1.base](https://huggingface.co/rednote-hilab/dots.llm1.base). 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 152064): Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids` passed when calling [`Dots1Model`]. hidden_size (`int`, *optional*, defaults to 4608): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 10944): Dimension of the MLP representations. moe_intermediate_size (`int`, *optional*, defaults to 1408): Dimension of the MoE representations. num_hidden_layers (`int`, *optional*, defaults to 62): 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*, defaults to 32): Number of key/value heads for Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, Multi Head Attention (MHA) is used. If `num_key_value_heads=1`, Multi Query Attention (MQA) is used. Otherwise, Grouped Query Attention (GQA) is used. If not specified, defaults to `num_attention_heads`. n_shared_experts (`int`, *optional*, default=None): Number of shared experts. None means dense model. n_routed_experts (`int`, *optional*, default=None): Number of routed experts. None means dense model. n_group (`int`, *optional*, defaults to 1): Number of groups for routed experts. topk_group (`int`, *optional*, defaults to 1): Number of selected groups for each token (selected experts only within `topk_group` groups). num_experts_per_tok (`int`, *optional*, default=None): Number of selected experts. None means dense model. first_k_dense_replace (`int`, *optional*, defaults to 0): Number of dense layers at the beginning of the model before the first MoE layer. norm_topk_prob (`bool`, *optional*, defaults to `False`): Whether to normalize the weights of the routed experts. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string). max_position_embeddings (`int`, *optional*, defaults to 2048): Maximum sequence length the model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): Standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): 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. Only relevant if `config.is_decoder=True`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie the input and output word embeddings. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`dict`, *optional*): Dictionary for scaling RoPE embeddings. Supports `{"type": strategy name, "factor": scaling factor}`. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the self-attention projections. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout ratio for the attention probabilities. routed_scaling_factor (`float`, *optional*, defaults to 1.0): Scaling factor for routed experts. sliding_window (`int`, *optional*, defaults to 4096): Size of the sliding window for attention. If not specified, defaults to `4096`. max_window_layers (`int`, *optional*, defaults to 62): The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any additional layer afterwards will use SWA (Sliding Window Attention). layer_types (`list`, *optional*): Attention pattern for each layer. Examples: ```python >>> from transformers import Dots1Model, Dots1Config >>> # Initializing a Dots1 style configuration >>> configuration = Dots1Config() >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "dots1" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { # TODO: only replicate attention layers when > first_k_dense_replace "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.experts.*.gate_proj": "local_colwise", "layers.*.mlp.experts.*.up_proj": "local_colwise", "layers.*.mlp.experts.*.down_proj": "local_rowwise", "layers.*.mlp.experts.*": "local", # each expert is wrapped in a module list "layers.*.mlp.shared_experts.gate_proj": "local_colwise", "layers.*.mlp.shared_experts.up_proj": "local_colwise", "layers.*.mlp.shared_experts.down_proj": "local_rowwise", "layers.*.mlp.shared_experts": "local", "layers.*.mlp.gate_proj": "local_colwise", "layers.*.mlp.up_proj": "local_colwise", "layers.*.mlp.down_proj": "local_rowwise", "layers.*.mlp": "gather", # This is the only moment where results are gathered } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size=152064, hidden_size=4608, intermediate_size=10944, moe_intermediate_size=1408, num_hidden_layers=62, num_attention_heads=32, num_key_value_heads=32, n_shared_experts=None, n_routed_experts=None, n_group=1, topk_group=1, num_experts_per_tok=None, first_k_dense_replace=0, norm_topk_prob=False, hidden_act="silu", max_position_embeddings=2048, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, routed_scaling_factor=1.0, sliding_window=4096, max_window_layers=62, layer_types=None, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.moe_intermediate_size = moe_intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.n_shared_experts = n_shared_experts self.n_routed_experts = n_routed_experts self.num_experts_per_tok = num_experts_per_tok self.first_k_dense_replace = first_k_dense_replace self.norm_topk_prob = norm_topk_prob if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.n_group = n_group self.topk_group = topk_group 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.rope_scaling = rope_scaling self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.routed_scaling_factor = routed_scaling_factor self.sliding_window = sliding_window self.max_window_layers = max_window_layers self.layer_types = layer_types if self.layer_types is None: self.layer_types = [ "sliding_attention" if self.sliding_window is not None and i >= self.max_window_layers else "full_attention" for i in range(self.num_hidden_layers) ] layer_type_validation(self.layer_types) super().__init__( tie_word_embeddings=tie_word_embeddings, **kwargs, ) __all__ = ["Dots1Config"]