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# 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
from transformers.configuration_utils import PretrainedConfig, layer_type_validation
from transformers.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`.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental: tensor parallelism rank used during pretraining. This is necessary for exact reproducibility
of pretraining results.
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.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
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 that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full 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,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
routed_scaling_factor=1.0,
use_sliding_window=False,
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.pretraining_tp = pretraining_tp
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.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window if self.use_sliding_window else None
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"]
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